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.gitignore

@@ -0,0 +1,2 @@
+__pycache__/__init__.cpython-310.pyc
+__pycache__/efficiency_nodes.cpython-310.pyc

LICENSE

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+
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
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+reviewing courts shall apply local law that most closely approximates
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+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
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+    Copyright (C) <year>  <name of author>
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+    (at your option) any later version.
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
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+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,43 @@
+Efficiency Nodes for ComfyUI
+=======
+### A collection of <a href="https://github.com/comfyanonymous/ComfyUI" >ComfyUI</a> custom nodes to help streamline workflows and reduce total node count.
+## [Direct Download Link](https://github.com/LucianoCirino/efficiency-nodes-comfyui/releases/download/v1.62/efficiency-nodes-comfyui.zip)
+
+## **Currently Available Nodes:**
+<details><summary>Ksampler (Efficient)</summary><ul>
+• A modded KSampler with the ability to preview/output images and run scripts.<br>
+</ul></details>
+
+<details><summary>Efficient Loader</summary><ul>
+• A combination of common initialization nodes.
+</ul></details>
+
+<details><summary>XY Plotter Nodes</summary><ul>
+• A collection of nodes that allows users to specify parameters for the KSampler (Efficient) to plot on a grid.
+</ul></details>
+
+<details><summary>Image Overlay</summary><ul>
+• Node that allows for flexible image overlaying.
+</ul></details>
+
+<details><summary>SimpleEval Nodes</summary><ul>
+•  A collection of nodes that allows users to write simple Python expressions for a variety of data types using the "<a href="https://github.com/danthedeckie/simpleeval" >simpleeval</a>" library.
+  
+</ul></details>
+
+## **Examples:**
+  
+- HiResFix using the **Efficient Loader** & **Ksampler (Efficient)**
+
+<img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/main/workflows/HiRes Fix (overview).png" width="720">
+
+- 2D Plotting using the **XY Plot** & **Ksampler (Efficient)** nodes 
+
+<img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/main/workflows/XYplot/X-Seeds Y-Checkpoints (overview).png" width="720">
+
+- Photobashing using the **Image Overlay** node
+
+<img src="https://github.com/LucianoCirino/efficiency-nodes-comfyui/blob/main/workflows/Image Overlay (overview).png" width="720">
+
+## **Install:**
+To install, drop the "_**efficiency-nodes-comfyui**_" folder into the "_**...\ComfyUI\ComfyUI\custom_nodes**_" directory and restart UI.

__init__.py

@@ -0,0 +1,3 @@
+from .efficiency_nodes import NODE_CLASS_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS']

node_settings.json

@@ -0,0 +1,21 @@
+{
+    "Efficient Loader": {
+        "model_cache": {
+            "ckpt": 1,
+            "vae": 1,
+            "lora": 1
+        }
+    },
+    "KSampler (Efficient)": {
+	"vae_tiled": false
+    },
+    "XY Plot": {
+        "model_cache": {
+            "ckpt": 5,
+            "vae": 5,
+            "lora": 5
+        },
+	"vae_tiled": false
+    }
+}
+

requirements.txt

@@ -0,0 +1 @@
+simpleeval

tsc_sd.py

@@ -0,0 +1,308 @@
+import os
+import sys
+
+# Get the absolute path of the parent directory of the current script
+my_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Add the My directory path to the sys.path list
+sys.path.append(my_dir)
+
+# Construct the absolute path to the ComfyUI directory
+comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
+
+# Add the ComfyUI directory path to the sys.path list
+sys.path.append(comfy_dir)
+
+# Import functions from ComfyUI
+from comfy.sd import *
+from comfy import utils
+
+LORA_CLIP_MAP = {
+    "mlp.fc1": "mlp_fc1",
+    "mlp.fc2": "mlp_fc2",
+    "self_attn.k_proj": "self_attn_k_proj",
+    "self_attn.q_proj": "self_attn_q_proj",
+    "self_attn.v_proj": "self_attn_v_proj",
+    "self_attn.out_proj": "self_attn_out_proj",
+}
+
+LORA_UNET_MAP_ATTENTIONS = {
+    "proj_in": "proj_in",
+    "proj_out": "proj_out",
+    "transformer_blocks.0.attn1.to_q": "transformer_blocks_0_attn1_to_q",
+    "transformer_blocks.0.attn1.to_k": "transformer_blocks_0_attn1_to_k",
+    "transformer_blocks.0.attn1.to_v": "transformer_blocks_0_attn1_to_v",
+    "transformer_blocks.0.attn1.to_out.0": "transformer_blocks_0_attn1_to_out_0",
+    "transformer_blocks.0.attn2.to_q": "transformer_blocks_0_attn2_to_q",
+    "transformer_blocks.0.attn2.to_k": "transformer_blocks_0_attn2_to_k",
+    "transformer_blocks.0.attn2.to_v": "transformer_blocks_0_attn2_to_v",
+    "transformer_blocks.0.attn2.to_out.0": "transformer_blocks_0_attn2_to_out_0",
+    "transformer_blocks.0.ff.net.0.proj": "transformer_blocks_0_ff_net_0_proj",
+    "transformer_blocks.0.ff.net.2": "transformer_blocks_0_ff_net_2",
+}
+
+LORA_UNET_MAP_RESNET = {
+    "in_layers.2": "resnets_{}_conv1",
+    "emb_layers.1": "resnets_{}_time_emb_proj",
+    "out_layers.3": "resnets_{}_conv2",
+    "skip_connection": "resnets_{}_conv_shortcut"
+}
+
+def load_lora_tsc(path, to_load):
+    lora = utils.load_torch_file(path)
+    patch_dict = {}
+    loaded_keys = set()
+    for x in to_load:
+        alpha_name = "{}.alpha".format(x)
+        alpha = None
+        if alpha_name in lora.keys():
+            alpha = lora[alpha_name].item()
+            loaded_keys.add(alpha_name)
+
+        A_name = "{}.lora_up.weight".format(x)
+        B_name = "{}.lora_down.weight".format(x)
+        mid_name = "{}.lora_mid.weight".format(x)
+
+        if A_name in lora.keys():
+            mid = None
+            if mid_name in lora.keys():
+                mid = lora[mid_name]
+                loaded_keys.add(mid_name)
+            patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
+            loaded_keys.add(A_name)
+            loaded_keys.add(B_name)
+
+
+        ######## loha
+        hada_w1_a_name = "{}.hada_w1_a".format(x)
+        hada_w1_b_name = "{}.hada_w1_b".format(x)
+        hada_w2_a_name = "{}.hada_w2_a".format(x)
+        hada_w2_b_name = "{}.hada_w2_b".format(x)
+        hada_t1_name = "{}.hada_t1".format(x)
+        hada_t2_name = "{}.hada_t2".format(x)
+        if hada_w1_a_name in lora.keys():
+            hada_t1 = None
+            hada_t2 = None
+            if hada_t1_name in lora.keys():
+                hada_t1 = lora[hada_t1_name]
+                hada_t2 = lora[hada_t2_name]
+                loaded_keys.add(hada_t1_name)
+                loaded_keys.add(hada_t2_name)
+
+            patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
+            loaded_keys.add(hada_w1_a_name)
+            loaded_keys.add(hada_w1_b_name)
+            loaded_keys.add(hada_w2_a_name)
+            loaded_keys.add(hada_w2_b_name)
+
+
+        ######## lokr
+        lokr_w1_name = "{}.lokr_w1".format(x)
+        lokr_w2_name = "{}.lokr_w2".format(x)
+        lokr_w1_a_name = "{}.lokr_w1_a".format(x)
+        lokr_w1_b_name = "{}.lokr_w1_b".format(x)
+        lokr_t2_name = "{}.lokr_t2".format(x)
+        lokr_w2_a_name = "{}.lokr_w2_a".format(x)
+        lokr_w2_b_name = "{}.lokr_w2_b".format(x)
+
+        lokr_w1 = None
+        if lokr_w1_name in lora.keys():
+            lokr_w1 = lora[lokr_w1_name]
+            loaded_keys.add(lokr_w1_name)
+
+        lokr_w2 = None
+        if lokr_w2_name in lora.keys():
+            lokr_w2 = lora[lokr_w2_name]
+            loaded_keys.add(lokr_w2_name)
+
+        lokr_w1_a = None
+        if lokr_w1_a_name in lora.keys():
+            lokr_w1_a = lora[lokr_w1_a_name]
+            loaded_keys.add(lokr_w1_a_name)
+
+        lokr_w1_b = None
+        if lokr_w1_b_name in lora.keys():
+            lokr_w1_b = lora[lokr_w1_b_name]
+            loaded_keys.add(lokr_w1_b_name)
+
+        lokr_w2_a = None
+        if lokr_w2_a_name in lora.keys():
+            lokr_w2_a = lora[lokr_w2_a_name]
+            loaded_keys.add(lokr_w2_a_name)
+
+        lokr_w2_b = None
+        if lokr_w2_b_name in lora.keys():
+            lokr_w2_b = lora[lokr_w2_b_name]
+            loaded_keys.add(lokr_w2_b_name)
+
+        lokr_t2 = None
+        if lokr_t2_name in lora.keys():
+            lokr_t2 = lora[lokr_t2_name]
+            loaded_keys.add(lokr_t2_name)
+
+        if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
+            patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)
+
+    for x in lora.keys():
+        if x not in loaded_keys:
+            print("lora key not loaded", x)
+    return patch_dict
+
+def model_lora_keys(model, key_map={}):
+    sdk = model.state_dict().keys()
+
+    counter = 0
+    for b in range(12):
+        tk = "diffusion_model.input_blocks.{}.1".format(b)
+        up_counter = 0
+        for c in LORA_UNET_MAP_ATTENTIONS:
+            k = "{}.{}.weight".format(tk, c)
+            if k in sdk:
+                lora_key = "lora_unet_down_blocks_{}_attentions_{}_{}".format(counter // 2, counter % 2, LORA_UNET_MAP_ATTENTIONS[c])
+                key_map[lora_key] = k
+                up_counter += 1
+        if up_counter >= 4:
+            counter += 1
+    for c in LORA_UNET_MAP_ATTENTIONS:
+        k = "diffusion_model.middle_block.1.{}.weight".format(c)
+        if k in sdk:
+            lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP_ATTENTIONS[c])
+            key_map[lora_key] = k
+    counter = 3
+    for b in range(12):
+        tk = "diffusion_model.output_blocks.{}.1".format(b)
+        up_counter = 0
+        for c in LORA_UNET_MAP_ATTENTIONS:
+            k = "{}.{}.weight".format(tk, c)
+            if k in sdk:
+                lora_key = "lora_unet_up_blocks_{}_attentions_{}_{}".format(counter // 3, counter % 3, LORA_UNET_MAP_ATTENTIONS[c])
+                key_map[lora_key] = k
+                up_counter += 1
+        if up_counter >= 4:
+            counter += 1
+    counter = 0
+    text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
+    for b in range(24):
+        for c in LORA_CLIP_MAP:
+            k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
+            if k in sdk:
+                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
+                key_map[lora_key] = k
+
+
+    #Locon stuff
+    ds_counter = 0
+    counter = 0
+    for b in range(12):
+        tk = "diffusion_model.input_blocks.{}.0".format(b)
+        key_in = False
+        for c in LORA_UNET_MAP_RESNET:
+            k = "{}.{}.weight".format(tk, c)
+            if k in sdk:
+                lora_key = "lora_unet_down_blocks_{}_{}".format(counter // 2, LORA_UNET_MAP_RESNET[c].format(counter % 2))
+                key_map[lora_key] = k
+                key_in = True
+        for bb in range(3):
+            k = "{}.{}.op.weight".format(tk[:-2], bb)
+            if k in sdk:
+                lora_key = "lora_unet_down_blocks_{}_downsamplers_0_conv".format(ds_counter)
+                key_map[lora_key] = k
+                ds_counter += 1
+        if key_in:
+            counter += 1
+
+    counter = 0
+    for b in range(3):
+        tk = "diffusion_model.middle_block.{}".format(b)
+        key_in = False
+        for c in LORA_UNET_MAP_RESNET:
+            k = "{}.{}.weight".format(tk, c)
+            if k in sdk:
+                lora_key = "lora_unet_mid_block_{}".format(LORA_UNET_MAP_RESNET[c].format(counter))
+                key_map[lora_key] = k
+                key_in = True
+        if key_in:
+            counter += 1
+
+    counter = 0
+    us_counter = 0
+    for b in range(12):
+        tk = "diffusion_model.output_blocks.{}.0".format(b)
+        key_in = False
+        for c in LORA_UNET_MAP_RESNET:
+            k = "{}.{}.weight".format(tk, c)
+            if k in sdk:
+                lora_key = "lora_unet_up_blocks_{}_{}".format(counter // 3, LORA_UNET_MAP_RESNET[c].format(counter % 3))
+                key_map[lora_key] = k
+                key_in = True
+        for bb in range(3):
+            k = "{}.{}.conv.weight".format(tk[:-2], bb)
+            if k in sdk:
+                lora_key = "lora_unet_up_blocks_{}_upsamplers_0_conv".format(us_counter)
+                key_map[lora_key] = k
+                us_counter += 1
+        if key_in:
+            counter += 1
+
+    return key_map
+
+def load_lora_for_models_tsc(model, clip, lora_path, strength_model, strength_clip):
+    key_map = model_lora_keys(model.model)
+    key_map = model_lora_keys(clip.cond_stage_model, key_map)
+    loaded = load_lora_tsc(lora_path, key_map)
+    new_modelpatcher = model.clone()
+    k = new_modelpatcher.add_patches(loaded, strength_model)
+    new_clip = clip.clone()
+    k1 = new_clip.add_patches(loaded, strength_clip)
+    k = set(k)
+    k1 = set(k1)
+    for x in loaded:
+        if (x not in k) and (x not in k1):
+            print("NOT LOADED", x)
+
+    return (new_modelpatcher, new_clip)
+
+def load_checkpoint_guess_config_tsc(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
+    sd = utils.load_torch_file(ckpt_path)
+    sd_keys = sd.keys()
+    clip = None
+    clipvision = None
+    vae = None
+    model = None
+    clip_target = None
+
+    parameters = calculate_parameters(sd, "model.diffusion_model.")
+    fp16 = model_management.should_use_fp16(model_params=parameters)
+
+    class WeightsLoader(torch.nn.Module):
+        pass
+
+    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16)
+    if model_config is None:
+        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
+
+    if model_config.clip_vision_prefix is not None:
+        if output_clipvision:
+            clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
+
+    offload_device = model_management.unet_offload_device()
+    model = model_config.get_model(sd, "model.diffusion_model.")
+    model = model.to(offload_device)
+    model.load_model_weights(sd, "model.diffusion_model.")
+
+    if output_vae:
+        vae = VAE()
+        w = WeightsLoader()
+        w.first_stage_model = vae.first_stage_model
+        load_model_weights(w, sd)
+
+    if output_clip:
+        w = WeightsLoader()
+        clip_target = model_config.clip_target()
+        clip = CLIP(clip_target, embedding_directory=embedding_directory)
+        w.cond_stage_model = clip.cond_stage_model
+        sd = model_config.process_clip_state_dict(sd)
+        load_model_weights(w, sd)
+
+    return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae, clipvision)
+

tsc_utils.py

@@ -0,0 +1,427 @@
+# Efficiency Nodes Utility functions
+
+from torch import Tensor
+import torch
+from PIL import Image
+import numpy as np
+
+import os
+import sys
+import json
+import folder_paths
+
+# Get the absolute path of the parent directory of the current script
+my_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Add the My directory path to the sys.path list
+sys.path.append(my_dir)
+
+# Construct the absolute path to the ComfyUI directory
+comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))
+
+# Add the ComfyUI directory path to the sys.path list
+sys.path.append(comfy_dir)
+
+# Import functions from ComfyUI
+import comfy.sd
+
+# Load my version of Comfy functions
+from tsc_sd import *
+
+# Cache for Efficiency Node models
+loaded_objects = {
+    "ckpt": [], # (ckpt_name, ckpt_model, clip, bvae, [id])
+    "vae": [],  # (vae_name, vae, [id])
+    "lora": []  # ([(lora_name, strength_model, strength_clip)], ckpt_name, lora_model, clip_lora, [id])
+}
+
+# Cache for Ksampler (Efficient) Outputs
+last_helds: dict[str, list] = {
+    "results": [],      # (results, id) # Preview Images, stored as a pil image list
+    "latent": [],       # (latent, id)  # Latent outputs, stored as a latent tensor list
+    "images": [],       # (images, id)  # Image outputs, stored as an image tensor list
+    "vae_decode": [],   # (vae_decode, id) # Used to track wether to vae-decode or not
+}
+
+# Tensor to PIL (grabbed from WAS Suite)
+def tensor2pil(image: torch.Tensor) -> Image.Image:
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor (grabbed from WAS Suite)
+def pil2tensor(image: Image.Image) -> torch.Tensor:
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def extract_node_info(prompt, id, indirect_key=None):
+    # Convert ID to string
+    id = str(id)
+    node_id = None
+
+    # If an indirect_key (like 'script') is provided, perform a two-step lookup
+    if indirect_key:
+        # Ensure the id exists in the prompt and has an 'inputs' entry with the indirect_key
+        if id in prompt and 'inputs' in prompt[id] and indirect_key in prompt[id]['inputs']:
+            # Extract the indirect_id
+            indirect_id = prompt[id]['inputs'][indirect_key][0]
+
+            # Ensure the indirect_id exists in the prompt
+            if indirect_id in prompt:
+                node_id = indirect_id
+                return prompt[indirect_id].get('class_type', None), node_id
+
+        # If indirect_key is not found within the prompt
+        return None, None
+
+    # If no indirect_key is provided, perform a direct lookup
+    return prompt.get(id, {}).get('class_type', None), node_id
+
+def extract_node_value(prompt, id, key):
+    # If ID is in data, return its 'inputs' value for a given key. Otherwise, return None.
+    return prompt.get(str(id), {}).get('inputs', {}).get(key, None)
+
+def print_loaded_objects_entries(id=None, prompt=None, show_id=False):
+    print("-" * 40)  # Print an empty line followed by a separator line
+    if id is not None:
+        id = str(id)  # Convert ID to string
+    if prompt is not None and id is not None:
+        node_name, _ = extract_node_info(prompt, id)
+        if show_id:
+            print(f"\033[36m{node_name} Models Cache: (node_id:{int(id)})\033[0m")
+        else:
+            print(f"\033[36m{node_name} Models Cache:\033[0m")
+    elif id is None:
+        print(f"\033[36mGlobal Models Cache:\033[0m")
+    else:
+        print(f"\033[36mModels Cache: \nnode_id:{int(id)}\033[0m")
+    entries_found = False
+    for key in ["ckpt", "vae", "lora"]:
+        entries_with_id = loaded_objects[key] if id is None else [entry for entry in loaded_objects[key] if id in entry[-1]]
+        if not entries_with_id:  # If no entries with the chosen ID, print None and skip this key
+            continue
+        entries_found = True
+        print(f"{key.capitalize()}:")
+        for i, entry in enumerate(entries_with_id, 1):  # Start numbering from 1
+            if key == "lora":
+                lora_models_info = ', '.join(f"{os.path.splitext(os.path.basename(name))[0]}({round(strength_model, 2)},{round(strength_clip, 2)})" for name, strength_model, strength_clip in entry[0])
+                base_ckpt_name = os.path.splitext(os.path.basename(entry[1]))[0]  # Split logic for base_ckpt
+                if id is None:
+                    associated_ids = ', '.join(map(str, entry[-1]))  # Gather all associated ids
+                    print(f"  [{i}] base_ckpt: {base_ckpt_name}, lora(mod,clip): {lora_models_info} (ids: {associated_ids})")
+                else:
+                    print(f"  [{i}] base_ckpt: {base_ckpt_name}, lora(mod,clip): {lora_models_info}")
+            else:
+                name_without_ext = os.path.splitext(os.path.basename(entry[0]))[0]
+                if id is None:
+                    associated_ids = ', '.join(map(str, entry[-1]))  # Gather all associated ids
+                    print(f"  [{i}] {name_without_ext} (ids: {associated_ids})")
+                else:
+                    print(f"  [{i}] {name_without_ext}")
+    if not entries_found:
+        print("-")
+
+
+# This function cleans global variables associated with nodes that are no longer detected on UI
+def globals_cleanup(prompt):
+    global loaded_objects
+    global last_helds
+
+    # Step 1: Clean up last_helds
+    for key in list(last_helds.keys()):
+        original_length = len(last_helds[key])
+        last_helds[key] = [(value, id) for value, id in last_helds[key] if str(id) in prompt.keys()]
+        ###if original_length != len(last_helds[key]):
+            ###print(f'Updated {key} in last_helds: {last_helds[key]}')
+
+    # Step 2: Clean up loaded_objects
+    for key in list(loaded_objects.keys()):
+        for i, tup in enumerate(list(loaded_objects[key])):
+            # Remove ids from id array in each tuple that don't exist in prompt
+            id_array = [id for id in tup[-1] if str(id) in prompt.keys()]
+            if len(id_array) != len(tup[-1]):
+                if id_array:
+                    loaded_objects[key][i] = tup[:-1] + (id_array,)
+                    ###print(f'Updated tuple at index {i} in {key} in loaded_objects: {loaded_objects[key][i]}')
+                else:
+                    # If id array becomes empty, delete the corresponding tuple
+                    loaded_objects[key].remove(tup)
+                    ###print(f'Deleted tuple at index {i} in {key} in loaded_objects because its id array became empty.')
+
+def load_checkpoint(ckpt_name, id, output_vae=True, cache=None, cache_overwrite=False):
+    """
+    Searches for tuple index that contains ckpt_name in "ckpt" array of loaded_objects.
+    If found, extracts the model, clip, and vae from the loaded_objects.
+    If not found, loads the checkpoint, extracts the model, clip, and vae.
+    The id parameter represents the node ID and is used for caching models for the XY Plot node.
+    If the cache limit is reached for a specific id, clears the cache and returns the loaded model, clip, and vae without adding a new entry.
+    If there is cache space, adds the id to the ids list if it's not already there.
+    If there is cache space and the checkpoint was not found in loaded_objects, adds a new entry to loaded_objects.
+
+    Parameters:
+    - ckpt_name: name of the checkpoint to load.
+    - id: an identifier for caching models for specific nodes.
+    - output_vae: boolean, if True loads the VAE too.
+    - cache (optional): an integer that specifies how many checkpoint entries with a given id can exist in loaded_objects. Defaults to None.
+    """
+    global loaded_objects
+
+    for entry in loaded_objects["ckpt"]:
+        if entry[0] == ckpt_name:
+            _, model, clip, vae, ids = entry
+            cache_full = cache and len([entry for entry in loaded_objects["ckpt"] if id in entry[-1]]) >= cache
+
+            if cache_full:
+                clear_cache(id, cache, "ckpt")
+            elif id not in ids:
+                ids.append(id)
+
+            return model, clip, vae
+
+    ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+    out = load_checkpoint_guess_config_tsc(ckpt_path, output_vae, output_clip=True,
+                                                embedding_directory=folder_paths.get_folder_paths("embeddings"))
+    model = out[0]
+    clip = out[1]
+    vae = out[2]  # bvae
+
+    if cache:
+        if len([entry for entry in loaded_objects["ckpt"] if id in entry[-1]]) < cache:
+            loaded_objects["ckpt"].append((ckpt_name, model, clip, vae, [id]))
+        else:
+            clear_cache(id, cache, "ckpt")
+            if cache_overwrite:
+                # Find the first entry with the id, remove the id from the entry's id list
+                for e in loaded_objects["ckpt"]:
+                    if id in e[-1]:
+                        e[-1].remove(id)
+                        # If the id list becomes empty, remove the entry from the "ckpt" list
+                        if not e[-1]:
+                            loaded_objects["ckpt"].remove(e)
+                        break
+                loaded_objects["ckpt"].append((ckpt_name, model, clip, vae, [id]))
+
+    return model, clip, vae
+
+def get_bvae_by_ckpt_name(ckpt_name):
+    for ckpt in loaded_objects["ckpt"]:
+        if ckpt[0] == ckpt_name:
+            return ckpt[3]  # return 'bvae' variable
+    return None  # return None if no match is found
+
+def load_vae(vae_name, id, cache=None, cache_overwrite=False):
+    """
+    Extracts the vae with a given name from the "vae" array in loaded_objects.
+    If the vae is not found, creates a new VAE object with the given name and adds it to the "vae" array.
+    Also stores the id parameter, which is used for caching models specifically for nodes with the given ID.
+    If the cache limit is reached for a specific id, returns the loaded vae without adding id or making a new entry in loaded_objects.
+    If there is cache space, and the id is not in the ids list, adds the id to the ids list.
+    If there is cache space, and the vae was not found in loaded_objects, adds a new entry to the loaded_objects.
+
+    Parameters:
+    - vae_name: name of the VAE to load.
+    - id (optional): an identifier for caching models for specific nodes. Defaults to None.
+    - cache (optional): an integer that specifies how many vae entries with a given id can exist in loaded_objects. Defaults to None.
+    """
+    global loaded_objects
+
+    for i, entry in enumerate(loaded_objects["vae"]):
+        if entry[0] == vae_name:
+            vae, ids = entry[1], entry[2]
+            if id not in ids:
+                if cache and len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) >= cache:
+                    return vae
+                ids.append(id)
+            if cache:
+                clear_cache(id, cache, "vae")
+            return vae
+
+    vae_path = folder_paths.get_full_path("vae", vae_name)
+    vae = comfy.sd.VAE(ckpt_path=vae_path)
+
+    if cache:
+        if len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) < cache:
+            loaded_objects["vae"].append((vae_name, vae, [id]))
+        else:
+            clear_cache(id, cache, "vae")
+            if cache_overwrite:
+                # Find the first entry with the id, remove the id from the entry's id list
+                for e in loaded_objects["vae"]:
+                    if id in e[-1]:
+                        e[-1].remove(id)
+                        # If the id list becomes empty, remove the entry from the "vae" list
+                        if not e[-1]:
+                            loaded_objects["vae"].remove(e)
+                        break
+                loaded_objects["vae"].append((vae_name, vae, [id]))
+
+    return vae
+
+def load_lora(lora_params, ckpt_name, id, cache=None, ckpt_cache=None, cache_overwrite=False):
+    """
+    Extracts the Lora model with a given name from the "lora" array in loaded_objects.
+    If the Lora model is not found or strength values changed or model changed, creates a new Lora object with the given name and adds it to the "lora" array.
+    Also stores the id parameter, which is used for caching models specifically for nodes with the given ID.
+    If the cache limit is reached for a specific id, clears the cache and returns the loaded Lora model and clip without adding a new entry.
+    If there is cache space, adds the id to the ids list if it's not already there.
+    If there is cache space and the Lora model was not found in loaded_objects, adds a new entry to loaded_objects.
+
+    Parameters:
+    - lora_params: A list of tuples, where each tuple contains lora_name, strength_model, strength_clip.
+    - ckpt_name: name of the checkpoint from which the Lora model is created.
+    - id: an identifier for caching models for specific nodes.
+    - cache (optional): an integer that specifies how many Lora entries with a given id can exist in loaded_objects. Defaults to None.
+    """
+    global loaded_objects
+
+    for entry in loaded_objects["lora"]:
+        # Convert to sets and compare
+        if set(entry[0]) == set(lora_params) and entry[1] == ckpt_name:
+
+            _, _, lora_model, lora_clip, ids = entry
+            cache_full = cache and len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) >= cache
+
+            if cache_full:
+                clear_cache(id, cache, "lora")
+            elif id not in ids:
+                ids.append(id)
+
+            # Additional cache handling for 'ckpt' just like in 'load_checkpoint' function
+            for ckpt_entry in loaded_objects["ckpt"]:
+                if ckpt_entry[0] == ckpt_name:
+                    _, _, _, _, ckpt_ids = ckpt_entry
+                    ckpt_cache_full = ckpt_cache and len(
+                        [ckpt_entry for ckpt_entry in loaded_objects["ckpt"] if id in ckpt_entry[-1]]) >= ckpt_cache
+
+                    if ckpt_cache_full:
+                        clear_cache(id, ckpt_cache, "ckpt")
+                    elif id not in ckpt_ids:
+                        ckpt_ids.append(id)
+
+            return lora_model, lora_clip
+
+    def recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths):
+        if len(lora_params) == 0:
+            return ckpt, clip
+
+        lora_name, strength_model, strength_clip = lora_params[0]
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora_model, lora_clip = load_lora_for_models_tsc(ckpt, clip, lora_path, strength_model, strength_clip)
+
+        # Call the function again with the new lora_model and lora_clip and the remaining tuples
+        return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths)
+
+    # Unpack lora parameters from the first element of the list for now
+    lora_name, strength_model, strength_clip = lora_params[0]
+    ckpt, clip, _ = load_checkpoint(ckpt_name, id, cache=ckpt_cache, cache_overwrite=cache_overwrite)
+
+    lora_model, lora_clip = recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths)
+
+    if cache:
+        if len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) < cache:
+            loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
+        else:
+            clear_cache(id, cache, "lora")
+            if cache_overwrite:
+                # Find the first entry with the id, remove the id from the entry's id list
+                for e in loaded_objects["lora"]:
+                    if id in e[-1]:
+                        e[-1].remove(id)
+                        # If the id list becomes empty, remove the entry from the "lora" list
+                        if not e[-1]:
+                            loaded_objects["lora"].remove(e)
+                        break
+                loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
+
+    return lora_model, lora_clip
+
+def clear_cache(id, cache, dict_name):
+    """
+    Clear the cache for a specific id in a specific dictionary (either "ckpt" or "vae").
+    If the cache limit is reached for a specific id, deletes the id from the oldest entry.
+    If the id array of the entry becomes empty, deletes the entry.
+    """
+    # Get all entries associated with the id_element
+    id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
+    while len(id_associated_entries) > cache:
+        # Identify an older entry (but not necessarily the oldest) containing id
+        older_entry = id_associated_entries[0]
+        # Remove the id_element from the older entry
+        older_entry[-1].remove(id)
+        # If the id array of the older entry becomes empty after this, delete the entry
+        if not older_entry[-1]:
+            loaded_objects[dict_name].remove(older_entry)
+        # Update the id_associated_entries
+        id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
+
+def clear_cache_by_exception(node_id, vae_dict=None, ckpt_dict=None, lora_dict=None):
+    global loaded_objects
+
+    dict_mapping = {
+        "vae_dict": "vae",
+        "ckpt_dict": "ckpt",
+        "lora_dict": "lora"
+    }
+
+    for arg_name, arg_val in {"vae_dict": vae_dict, "ckpt_dict": ckpt_dict, "lora_dict": lora_dict}.items():
+        if arg_val is None:
+            continue
+
+        dict_name = dict_mapping[arg_name]
+
+        for tuple_idx, tuple_item in enumerate(loaded_objects[dict_name].copy()):
+            if arg_name == "lora_dict":
+                # Iterate over the tuples (lora_params, ckpt_name) in arg_val
+                for lora_params, ckpt_name in arg_val:
+                    # Compare lists of tuples considering order inside tuples, but not order of tuples
+                    if set(lora_params) == set(tuple_item[0]) and ckpt_name == tuple_item[1]:
+                        break
+                else:  # If no match was found in lora_dict, remove the tuple from loaded_objects
+                    if node_id in tuple_item[-1]:
+                        tuple_item[-1].remove(node_id)
+                        if not tuple_item[-1]:
+                            loaded_objects[dict_name].remove(tuple_item)
+                    continue
+            elif tuple_item[0] not in arg_val:  # Only remove the tuple if it's not in arg_val
+                if node_id in tuple_item[-1]:
+                    tuple_item[-1].remove(node_id)
+                    if not tuple_item[-1]:
+                        loaded_objects[dict_name].remove(tuple_item)
+
+
+# Retrieve the cache number from 'node_settings' json file
+def get_cache_numbers(node_name):
+    # Get the directory path of the current file
+    my_dir = os.path.dirname(os.path.abspath(__file__))
+    # Construct the file path for node_settings.json
+    settings_file = os.path.join(my_dir, 'node_settings.json')
+    # Load the settings from the JSON file
+    with open(settings_file, 'r') as file:
+        node_settings = json.load(file)
+    # Retrieve the cache numbers for the given node
+    model_cache_settings = node_settings.get(node_name, {}).get('model_cache', {})
+    vae_cache = int(model_cache_settings.get('vae', 1))
+    ckpt_cache = int(model_cache_settings.get('ckpt', 1))
+    lora_cache = int(model_cache_settings.get('lora', 1))
+    return vae_cache, ckpt_cache, lora_cache
+
+def print_last_helds(id=None):
+    print("\n" + "-" * 40)  # Print an empty line followed by a separator line
+    if id is not None:
+        id = str(id)  # Convert ID to string
+        print(f"Node-specific Last Helds (node_id:{int(id)})")
+    else:
+        print(f"Global Last Helds:")
+    for key in ["results", "latent", "images", "vae_decode"]:
+        entries_with_id = last_helds[key] if id is None else [entry for entry in last_helds[key] if id == entry[-1]]
+        if not entries_with_id:  # If no entries with the chosen ID, print None and skip this key
+            continue
+        print(f"{key.capitalize()}:")
+        for i, entry in enumerate(entries_with_id, 1):  # Start numbering from 1
+            if isinstance(entry[0], bool):  # Special handling for boolean types
+                output = entry[0]
+            else:
+                output = len(entry[0])
+            if id is None:
+                print(f"  [{i}] Output: {output} (id: {entry[-1]})")
+            else:
+                print(f"  [{i}] Output: {output}")
+    print("-" * 40)  # Print a separator line
+    print("\n")  # Print an empty line

workflows/SimpleEval_Node_Examples.txt

@@ -0,0 +1,56 @@
+The Evaluate Integers, Floats, and Strings nodes 
+now employ the SimpleEval library, enabling secure 
+creation and execution of custom Python expressions.
+
+(https://github.com/danthedeckie/simpleeval)
+
+Below is a short list of what is possible.
+______________________________________________
+
+"EVALUATE INTEGERS/FLOATS" NODE EXPRESSION EXAMPLES:
+
+Addition: a + b + c
+Subtraction: a - b - c
+Multiplication: a * b * c
+Division: a / b / c
+Modulo: a % b % c
+Exponentiation: a ** b ** c
+Floor Division: a // b // c
+Absolute Value: abs(a) + abs(b) + abs(c)
+Maximum: max(a, b, c)
+Minimum: min(a, b, c)
+Sum of Squares: a**2 + b**2 + c**2
+Bitwise And: a & b & c
+Bitwise Or: a | b | c
+Bitwise Xor: a ^ b ^ c
+Left Shift: a << 1 + b << 1 + c << 1
+Right Shift: a >> 1 + b >> 1 + c >> 1
+Greater Than Comparison: a > b > c
+Less Than Comparison: a < b < c
+Equal To Comparison: a == b == c
+Not Equal To Comparison: a != b != c
+______________________________________________
+
+"EVALUATE STRINGS" NODE EXPRESSION EXAMPLES:
+
+Concatenate: a + b + c
+Format: f'{a} {b} {c}'
+Length: len(a) + len(b) + len(c)
+Uppercase: a.upper() + b.upper() + c.upper()
+Lowercase: a.lower() + b.lower() + c.lower()
+Capitalize: a.capitalize() + b.capitalize() + c.capitalize()
+Title Case: a.title() + b.title() + c.title()
+Strip: a.strip() + b.strip() + c.strip()
+Find Substring: a.find('sub') + b.find('sub') + c.find('sub')
+Replace Substring: a.replace('old', 'new') + b.replace('old', 'new') + c.replace('old', 'new')
+Count Substring: a.count('sub') + b.count('sub') + c.count('sub')
+Check Numeric: a.isnumeric() + b.isnumeric() + c.isnumeric()
+Check Alphabetic: a.isalpha() + b.isalpha() + c.isalpha()
+Check Alphanumeric: a.isalnum() + b.isalnum() + c.isalnum()
+Check Start: a.startswith('prefix') + b.startswith('prefix') + c.startswith('prefix')
+Check End: a.endswith('suffix') + b.endswith('suffix') + c.endswith('suffix')
+Split: a.split(' ') + b.split(' ') + c.split(' ')
+Zero Fill: a.zfill(5) + b.zfill(5) + c.zfill(5)
+Slice: a[:5] + b[:5] + c[:5]
+Reverse: a[::-1] + b[::-1] + c[::-1]
+______________________________________________

workflows/XYplot/Manual Entry Notes.txt

@@ -0,0 +1,43 @@
+EFFICIENCY NODES - XY Plot Manual Entry Syntax Notes
+______________________________________________
+Type: Sampler
+
+if not defined by X/Y_value syntax, the default_scheduler = Ksampler (Efficient)'s Scheduler
+
+1. sampler_1;sampler_2 					= sampler_1(default_scheduler), sampler_2(default_scheduler)
+2. sampler_1,scheduler_1;sampler_2; 			= sampler_1(scheduler_1), sampler_2(default_scheduler)
+3. sampler_1;sampler_2;,default_scheduler		= sampler_1(default_scheduler), sampler_2(default_scheduler)
+4. sampler_1;sampler_2,scheduler_2;,default_scheduler	= sampler_1(default_scheduler), sampler_2(scheduler_2)	
+
+______________________________________________
+Type: Checkpoint
+
+if not defined by X/Y_value syntax, the default_clip_skip = Efficient Loader's Clip Skip
+
+1. ckpt_1;ckpt_2 					= ckpt_1(default_clip_skip), ckpt_2(default_clip_skip)
+2. ckpt_1_1,clip_skip_1;ckpt_2; 			= ckpt_1(clip_skip_1), ckpt_2(default_clip_skip)
+3. ckpt_1;ckpt_2;,default_clip_skip			= ckpt_1(default_clip_skip), ckpt_2(default_clip_skip)
+4. ckpt_1;ckpt_2,clip_skip_2;,default_clip_skip		= ckpt_1(default_clip_skip), ckpt_2(clip_skip_2)
+
+______________________________________________
+
+Type: LoRA
+
+if not defined by X/Y_value syntax, the default_lora_model_str = default_lora_clip_str = 1
+
+def_mod_str = default_lora_model_str
+def_clip_str =  default_lora_clip_str
+
+1. lora_1;lora_2 					= lora_1(default_clip_skip), lora_2(default_clip_skip)
+2. lora_1,mod_str_1;lora_2; 				= lora_1(mod_str_1,1), lora_2(1,1)
+3. lora_1;lora_2;,def_mod_str				= lora_1(def_mod_str,1), lora_2(def_mod_str,1)
+4. lora_1;lora_2,mod_str_2;,def_mod_str,def_clip_str	= lora_1(def_mod_str,def_clip_str), lora_2(mod_str_2,def_clip_str)
+
+______________________________________________
+
+Notes:
+- Trailing semicolons, new entry lines, and spaces are removed when parsing input strings.
+- Any parameter not defined by the XY Plot's inputs are defined by the Efficient Loader and Ksampler (Efficient).
+  For example, if the scheduler isn't specified when plotting Samplers, the script will use
+  the scheduler set by the Ksampler (Efficient).
+- LoRA stacking through the XY Plot Manual Entry node is currently not supported.