Add files using upload-large-folder tool

checkpoints/SDXL/PornMaster-Pro-SDXL-V7-VAE.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "PornMaster-Pro-SDXL-V7-VAE",
+  "model_name": "PornMaster-Pro-SDXL-V7-VAE",
+  "file_path": "/home/ComfyUI/models/checkpoints/SDXL/PornMaster-Pro-SDXL-V7-VAE.safetensors",
+  "size": 7105349470,
+  "modified": 1759056801.513531,
+  "sha256": "57b14abe8a6634c1f3ea24f310a5ab2f49968c5adf033a69ee2bf656737a1c17",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "model_type": "checkpoint"
+}

clip/siglip-so400m-patch14-384/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

clip/siglip-so400m-patch14-384/README.md

@@ -0,0 +1,112 @@
+---
+license: apache-2.0
+tags:
+- vision
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# SigLIP (shape-optimized model) 
+
+SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
+
+This model has the SoViT-400m architecture, which is the shape-optimized version as presented in [Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design](https://arxiv.org/abs/2305.13035) by Alabdulmohsin et al.
+
+Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.
+
+A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713).
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for
+other versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+
+model = AutoModel.from_pretrained("google/siglip-so400m-patch14-384")
+processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-384")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+texts = ["a photo of 2 cats", "a photo of 2 dogs"]
+inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+logits_per_image = outputs.logits_per_image
+probs = torch.sigmoid(logits_per_image) # these are the probabilities
+print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+```
+
+Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user:
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+
+# load pipe
+image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-so400m-patch14-384")
+
+# load image
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# inference
+outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
+outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
+print(outputs)
+```
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
+
+## Training procedure
+
+### Training data
+
+SigLIP is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
+
+### Preprocessing
+
+Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+Texts are tokenized and padded to the same length (64 tokens).
+
+### Compute
+
+The model was trained on 16 TPU-v4 chips for three days.
+
+## Evaluation results
+
+Evaluation of SigLIP compared to CLIP is shown below (taken from the paper).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
+alt="drawing" width="600"/>
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{zhai2023sigmoid,
+      title={Sigmoid Loss for Language Image Pre-Training}, 
+      author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer},
+      year={2023},
+      eprint={2303.15343},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

clip/siglip-so400m-patch14-384/config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "SiglipModel"
+  ],
+  "initializer_factor": 1.0,
+  "model_type": "siglip",
+  "text_config": {
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "model_type": "siglip_text_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0.dev0",
+  "vision_config": {
+    "hidden_size": 1152,
+    "image_size": 384,
+    "intermediate_size": 4304,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 14
+  }
+}

clip/siglip-so400m-patch14-384/preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

clip/siglip-so400m-patch14-384/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  }
+}

clip/siglip-so400m-patch14-384/tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "added_tokens_decoder": {
+    "1": {
+      "content": "</s>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "model_input_names": [
+    "input_ids"
+  ],
+  "model_max_length": 64,
+  "pad_token": "</s>",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SiglipTokenizer",
+  "unk_token": "<unk>"
+}

configs/anything_v3.yaml

@@ -0,0 +1,73 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      params:
+        layer: "hidden"
+        layer_idx: -2

configs/v1-inference.yaml

@@ -0,0 +1,70 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

configs/v1-inference_clip_skip_2.yaml

@@ -0,0 +1,73 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      params:
+        layer: "hidden"
+        layer_idx: -2

configs/v1-inference_clip_skip_2_fp16.yaml

@@ -0,0 +1,74 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      params:
+        layer: "hidden"
+        layer_idx: -2

configs/v1-inference_fp16.yaml

@@ -0,0 +1,71 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

configs/v1-inpainting-inference.yaml

@@ -0,0 +1,71 @@
+model:
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    finetune_keys: null
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 9  # 4 data + 4 downscaled image + 1 mask
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+

configs/v2-inference-v.yaml

@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

configs/v2-inference-v_fp32.yaml

@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: False
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

configs/v2-inference.yaml

@@ -0,0 +1,67 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

configs/v2-inference_fp32.yaml

@@ -0,0 +1,67 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: False
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

configs/v2-inpainting-inference.yaml

@@ -0,0 +1,158 @@
+model:
+  base_learning_rate: 5.0e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: hybrid
+    scale_factor: 0.18215
+    monitor: val/loss_simple_ema
+    finetune_keys: null
+    use_ema: False
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 9
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+            - 1
+            - 2
+            - 4
+            - 4
+          num_res_blocks: 2
+          attn_resolutions: [ ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
+
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: null  # for concat as in LAION-A
+    p_unsafe_threshold: 0.1
+    filter_word_list: "data/filters.yaml"
+    max_pwatermark: 0.45
+    batch_size: 8
+    num_workers: 6
+    multinode: True
+    min_size: 512
+    train:
+      shards:
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
+        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -"  #{00000-94333}.tar"
+      shuffle: 10000
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 512
+          interpolation: 3
+      - target: torchvision.transforms.RandomCrop
+        params:
+          size: 512
+      postprocess:
+        target: ldm.data.laion.AddMask
+        params:
+          mode: "512train-large"
+          p_drop: 0.25
+    # NOTE use enough shards to avoid empty validation loops in workers
+    validation:
+      shards:
+        - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - "
+      shuffle: 0
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 512
+          interpolation: 3
+      - target: torchvision.transforms.CenterCrop
+        params:
+          size: 512
+      postprocess:
+        target: ldm.data.laion.AddMask
+        params:
+          mode: "512train-large"
+          p_drop: 0.25
+
+lightning:
+  find_unused_parameters: True
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+
+  callbacks:
+    metrics_over_trainsteps_checkpoint:
+      params:
+        every_n_train_steps: 10000
+
+    image_logger:
+      target: main.ImageLogger
+      params:
+        enable_autocast: False
+        disabled: False
+        batch_frequency: 1000
+        max_images: 4
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          inpaint: False
+          plot_progressive_rows: False
+          plot_diffusion_rows: False
+          N: 4
+          unconditional_guidance_scale: 5.0
+          unconditional_guidance_label: [""]
+          ddim_steps: 50  # todo check these out for depth2img,
+          ddim_eta: 0.0   # todo check these out for depth2img,
+
+  trainer:
+    benchmark: True
+    val_check_interval: 5000000
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1

diffusion_models/lotus-depth-d-v1-1.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "lotus-depth-d-v1-1",
+  "model_name": "lotus-depth-d-v1-1",
+  "file_path": "/home/ComfyUI/models/diffusion_models/lotus-depth-d-v1-1.safetensors",
+  "size": 1735197352,
+  "modified": 1759046255.353416,
+  "sha256": "dc1394219a04afdd9f72ad41ea0d3dfa435603fc7831a0ac5b884cc2b2ebf688",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "model_type": "checkpoint"
+}

diffusion_models/qwen_image_edit_fp8_e4m3fn.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen_image_edit_fp8_e4m3fn",
+  "model_name": "qwen_image_edit_fp8_e4m3fn",
+  "file_path": "/home/ComfyUI/models/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors",
+  "size": 20430635136,
+  "modified": 1759046270.392764,
+  "sha256": "393c6743d1de2e9031b5197027b36116f2096958ccc0223526d34e1860266021",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "model_type": "checkpoint"
+}

diffusion_models/qwen_image_fp8_e4m3fn.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen_image_fp8_e4m3fn",
+  "model_name": "qwen_image_fp8_e4m3fn",
+  "file_path": "/home/ComfyUI/models/diffusion_models/qwen_image_fp8_e4m3fn.safetensors",
+  "size": 20430635136,
+  "modified": 1759046287.123044,
+  "sha256": "98763a127701eb6fb59096f7742cb3aa7d64ed510b9f4e882d8351f8176e3ce3",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "model_type": "checkpoint"
+}

loras/lora_manager_stats.json

@@ -0,0 +1,21 @@
+{
+  "checkpoints": {
+    "9e3f23a2c662fd4ccc84ca259fa93a6e5a6beec81a51662f4437a6a399b84d87": {
+      "total": 10,
+      "history": {
+        "2025-09-28": 2,
+        "2025-10-15": 8
+      }
+    }
+  },
+  "loras": {
+    "b1eb2c89e27ffc2d6aa677343d0d4ad3523fda72ec8aded1c9370f268a07136e": {
+      "total": 2,
+      "history": {
+        "2025-07-13": 2
+      }
+    }
+  },
+  "total_executions": 165,
+  "last_save_time": 1760608959.288762
+}

loras/qwen/Qwen-Image-Edit-Lightning-4steps-V1.0-float8_e4m3fn.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-Image-Edit-Lightning-4steps-V1.0-float8_e4m3fn",
+  "model_name": "Qwen-Image-Edit-Lightning-4steps-V1.0-float8_e4m3fn",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-Image-Edit-Lightning-4steps-V1.0-float8_e4m3fn.safetensors",
+  "size": 424950736,
+  "modified": 1759046259.020633,
+  "sha256": "cf5eb995b0651cc3a88479972d80d6bb69cb0a14f83a448fd69a3871b6326051",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-Image-Edit-Lightning-8steps-V1.0-float8_e4m3fn.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-Image-Edit-Lightning-8steps-V1.0-float8_e4m3fn",
+  "model_name": "Qwen-Image-Edit-Lightning-8steps-V1.0-float8_e4m3fn",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-Image-Edit-Lightning-8steps-V1.0-float8_e4m3fn.safetensors",
+  "size": 424950736,
+  "modified": 1759046258.714444,
+  "sha256": "4c2ecbd0d14f638a3ae7c965f62ece03205ce8f44a5d66b73e6754634d76fdfa",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-Image-Lightning-4steps-V1.0-fp8-e4m3.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-Image-Lightning-4steps-V1.0-fp8-e4m3",
+  "model_name": "Qwen-Image-Lightning-4steps-V1.0-fp8-e4m3",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-Image-Lightning-4steps-V1.0-fp8-e4m3.safetensors",
+  "size": 424950736,
+  "modified": 1759046258.446416,
+  "sha256": "ee9d08413f1e37c069ef9b1bbb5da50addbcfed0a9d535cc05999c4d6038ef58",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-Image-Lightning-4steps-V1.0.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-Image-Lightning-4steps-V1.0",
+  "model_name": "Qwen-Image-Lightning-4steps-V1.0",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-Image-Lightning-4steps-V1.0.safetensors",
+  "size": 424950736,
+  "modified": 1759046258.112651,
+  "sha256": "ee9d08413f1e37c069ef9b1bbb5da50addbcfed0a9d535cc05999c4d6038ef58",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-Image-Lightning-8steps-V1.0-float8_e4m3fn.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-Image-Lightning-8steps-V1.0-float8_e4m3fn",
+  "model_name": "Qwen-Image-Lightning-8steps-V1.0-float8_e4m3fn",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-Image-Lightning-8steps-V1.0-float8_e4m3fn.safetensors",
+  "size": 424950736,
+  "modified": 1759046257.812152,
+  "sha256": "c9a03ba7e4376602cab293ba0bca0126ba4e581c6b1bcace5e8c37f6fae6fe36",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-Image-Lightning-8steps-V1.1-float8_e5m2fn.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-Image-Lightning-8steps-V1.1-float8_e5m2fn",
+  "model_name": "Qwen-Image-Lightning-8steps-V1.1-float8_e5m2fn",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-Image-Lightning-8steps-V1.1-float8_e5m2fn.safetensors",
+  "size": 424950736,
+  "modified": 1759046257.505867,
+  "sha256": "1136a0a8e2b2864a34bfd4212257e219dc6a6a2c29c03dc31daa96e2b1d9fe27",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-MysticXXX-v1.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-MysticXXX-v1",
+  "model_name": "Qwen-MysticXXX-v1",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-MysticXXX-v1.safetensors",
+  "size": 295146184,
+  "modified": 1759046260.522925,
+  "sha256": "e812731c86410482b40a439d7e3e8950dee93a6a694f3fcfdafae8ec0d8688fb",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/Qwen-NSFW-Beta5.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "Qwen-NSFW-Beta5",
+  "model_name": "Qwen-NSFW-Beta5",
+  "file_path": "/home/ComfyUI/models/loras/qwen/Qwen-NSFW-Beta5.safetensors",
+  "size": 590154464,
+  "modified": 1759046257.158052,
+  "sha256": "e269f22403ffe2127397fc4f791df245e22e58c4ca6be5ab11a59667ab009265",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/[QWEN] Send Nudes Pro - Beta v1.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "[QWEN] Send Nudes Pro - Beta v1",
+  "model_name": "[QWEN] Send Nudes Pro - Beta v1",
+  "file_path": "/home/ComfyUI/models/loras/qwen/[QWEN] Send Nudes Pro - Beta v1.safetensors",
+  "size": 590059058,
+  "modified": 1759046261.329339,
+  "sha256": "73cb29bd3d73ea2cb2df031ec8135ae637ec08d4e9e38cddbcb686fe86871698",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/flymy_qwen_image_edit_inscene_lora.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "flymy_qwen_image_edit_inscene_lora",
+  "model_name": "flymy_qwen_image_edit_inscene_lora",
+  "file_path": "/home/ComfyUI/models/loras/qwen/flymy_qwen_image_edit_inscene_lora.safetensors",
+  "size": 47249496,
+  "modified": 1759046253.309099,
+  "sha256": "dd902c211307b92b71b957855634ddb871baa64aad9bb58f8819122ca3935a8f",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/jib_qwen_fix_000002750.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "jib_qwen_fix_000002750",
+  "model_name": "jib_qwen_fix_000002750",
+  "file_path": "/home/ComfyUI/models/loras/qwen/jib_qwen_fix_000002750.safetensors",
+  "size": 590058824,
+  "modified": 1759046261.823359,
+  "sha256": "397972af82f6b17bfdd3a793df34b7c45e8430aa318de4f9be26332300f893c0",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/qwen-edit-remove-clothes.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen-edit-remove-clothes",
+  "model_name": "qwen-edit-remove-clothes",
+  "file_path": "/home/ComfyUI/models/loras/qwen/qwen-edit-remove-clothes.safetensors",
+  "size": 472047184,
+  "modified": 1759046253.898765,
+  "sha256": "36b01e192095006ff7ac732ed22b9b716194495038ea3eb372c8ca377f281253",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/qwen-image_d!ck_P3N1S_LoRA_V1.1.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen-image_d!ck_P3N1S_LoRA_V1.1",
+  "model_name": "qwen-image_d!ck_P3N1S_LoRA_V1.1",
+  "file_path": "/home/ComfyUI/models/loras/qwen/qwen-image_d!ck_P3N1S_LoRA_V1.1.safetensors",
+  "size": 1179978088,
+  "modified": 1759046254.695376,
+  "sha256": "1f617d0b025fa6186373e489ffb0d67386bbac6e9ba2b9441bf56711b5739366",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/qwen_MCNL_v1.0.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen_MCNL_v1.0",
+  "model_name": "qwen_MCNL_v1.0",
+  "file_path": "/home/ComfyUI/models/loras/qwen/qwen_MCNL_v1.0.safetensors",
+  "size": 590058864,
+  "modified": 1759046255.09562,
+  "sha256": "16c4841028615bb82c38e79756c0abad42494d85bca0daebc2939384a74d86bb",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/qwen_image_nsfw.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen_image_nsfw",
+  "model_name": "qwen_image_nsfw",
+  "file_path": "/home/ComfyUI/models/loras/qwen/qwen_image_nsfw.safetensors",
+  "size": 188807872,
+  "modified": 1759046255.212291,
+  "sha256": "af10a4787691e56cd72803eeaed7f249c1b689583938147cd4ddb31e7d2e1350",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}

loras/qwen/qwen_uncensor_000014928.metadata.json

@@ -0,0 +1,22 @@
+{
+  "file_name": "qwen_uncensor_000014928",
+  "model_name": "qwen_uncensor_000014928",
+  "file_path": "/home/ComfyUI/models/loras/qwen/qwen_uncensor_000014928.safetensors",
+  "size": 2359534936,
+  "modified": 1759046256.727892,
+  "sha256": "5dae8d8505f624433275acc1f673fd819e520f78960498ec98caae77f1beb900",
+  "base_model": "Unknown",
+  "preview_url": "",
+  "preview_nsfw_level": 0,
+  "notes": "",
+  "from_civitai": true,
+  "civitai": null,
+  "tags": [],
+  "modelDescription": "",
+  "civitai_deleted": false,
+  "favorite": false,
+  "exclude": false,
+  "db_checked": false,
+  "last_checked_at": 0,
+  "usage_tips": "{}"
+}