Merge pull request #5 from brantje/load-llava-on-demand

Load llava on demand

.github/workflows/main.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.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github Repository secrets and reference it here.

README.md

@@ -4,6 +4,19 @@
 <a href="https://huggingface.co/spaces/AI4Editing/MagicQuill"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)"></a>
 <a href="https://creativecommons.org/licenses/by-sa/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg"></a>
 
+
+# Fixed
+- [x] SD1.5
+
+Todo
+- [ ] SDXL
+- [ ] FLUX 
+
+
+Note:
+[Brushnet](https://github.com/nullquant/ComfyUI-BrushNet) and [ComfyUI ControlNet Aux](https://github.com/Fannovel16/comfyui_controlnet_aux) are required.
+
+
 https://github.com/user-attachments/assets/8ee9663a-fef2-484a-a0b7-8427ab590424
 
 There is an HD video on [Youtube](https://www.youtube.com/watch?v=5DiKfONMnE4).

install.py

@@ -8,13 +8,3 @@ print(models_dir)
 # Download the model from the Hugging Face Hub
 repo_id = "LiuZichen/MagicQuill-models"  # or any other model/dataset ID
 snapshot_download(repo_id=repo_id, local_dir=models_dir)
-
-repos = {
-    'ComfyUI_BrushNet': 'https://github.com/nullquant/ComfyUI-BrushNet',
-    'comfyui_controlnet_aux': 'https://github.com/Fannovel16/comfyui_controlnet_aux'
-}
-
-# for name, url in repos.items():
-#     target_dir = os.path.join(parent_dir, name)
-#     if not os.path.exists(target_dir):
-#         subprocess.run(['git', 'clone', url, target_dir], check=True)

js/extension.js

@@ -28,7 +28,7 @@ app.registerExtension({
         for (const w of this.widgets) {
           if (["collapse_setting", "clear_canvas"].includes(w.name)) {
             // always show these widgets
-          } else if (["image", "original_image", "add_color_image", "add_edge_image", "remove_edge_image", "positive_prompt"].includes(w.name)) {
+          } else if (["image", "original_image", "add_color_image", "add_edge_image", "remove_edge_image"].includes(w.name)) {
             w.type = "hidden";
             w.value = null;
             w.computeSize = () => [0, -4];

js/magicquill_widget.js

@@ -266,7 +266,7 @@ export function MaigcQuillWidget(node, inputName, inputData, app) {
   };
 
   node.onDrawBackground = (ctx) => {
-    const setting_widgets = ["base_model_version", "negative_prompt", "dtype", "grow_size", "stroke_as_edge", "fine_edge", "edge_strength", "color_strength", "inpaint_strength", "seed", "steps", "cfg", "sampler_name", "scheduler"]
+    const setting_widgets = ["base_model_version","positive_prompt", "negative_prompt", "dtype", "grow_size", "stroke_as_edge", "fine_edge", "edge_strength", "color_strength", "inpaint_strength", "seed", "steps", "cfg", "sampler_name", "scheduler",  "optional_original_image_name", "optional_add_color_image_name", "optional_add_edge_image_name", "optional_remove_edge_image_name"]
     if (!this.flags.setting_collapsed) {
       for (const w of this.widgets) {
         if (setting_widgets.includes(w.name)) {

llava_new.py

@@ -23,15 +23,30 @@ import re
 class LLaVAModel:
     def __init__(self):
         # replace the model_path with correct path folder
-        base_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        self.base_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        models_dir = os.path.join(base_path, "models")
+        self.models_dir = os.path.join(self.base_path, "models")
-        model_path = os.path.join(models_dir, "llava-v1.5-7b-finetune-clean")
+        self.model_path = os.path.join(self.models_dir, "llava-v1.5-7b-finetune-clean")
+        self.tokenizer = None
+        self.model = None
+        self.image_processor = None
+        self.context_len = None
+    def load_model(self):
         self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
-            model_path=model_path,
+            model_path=self.model_path,
             model_base=None,
-            model_name=get_model_name_from_path(model_path),
+            model_name=get_model_name_from_path(self.model_path),
             
-        )
+        )   
+
+    def unload_model(self):
+        """Unload the model and clear GPU memory."""
+        if self.model is not None:
+            self.model.cpu()
+            del self.model
+            torch.cuda.empty_cache()
+        self.tokenizer = None
+        self.image_processor = None
+        self.context_len = None
 
     def generate_description(self, images, question):
         qs = question

magic_quill.py

@@ -1,6 +1,7 @@
 import hashlib
 import os
 import json
+import random
 from server import PromptServer
 from PIL import Image, ImageOps
 import torch
@@ -89,25 +90,92 @@ async def process_background_img(request):
 @PromptServer.instance.routes.post("/magic_quill/guess_prompt")
 async def guess_prompt_handler(request):
     json_data = await request.json()
-    add_color_image = json_data.get("add_color_image", None)
+    add_color_image_data = json_data.get("add_color_image", None)
-    original_image = json_data.get("original_image", None)
+    original_image_data = json_data.get("original_image", None)
-    add_edge_image = json_data.get("add_edge_image", None)
+    add_edge_image_data = json_data.get("add_edge_image", None)
 
-    original_image_path = folder_paths.get_annotated_filepath(original_image)
+    if not original_image_data:
-    original_image_tensor = load_and_preprocess_image(original_image_path)
+        return web.json_response({"error": "Original image is required."}, status=400)
-    
-    if add_color_image:
-        add_color_image_path = folder_paths.get_annotated_filepath(add_color_image)
-        add_color_image_tensor = load_and_preprocess_image(add_color_image_path)
-    else:
-        add_color_image_tensor = original_image_tensor
-    
-    width, height = original_image_tensor.shape[1], original_image_tensor.shape[2]
-    add_edge_mask = create_alpha_mask(folder_paths.get_annotated_filepath(add_edge_image)) if add_edge_image else torch.zeros((1, height, width), dtype=torch.float32, device="cpu")
 
-    res = MagicQuill.guess_prompt(original_image_tensor, add_color_image_tensor, add_edge_mask)
+    temp_files_to_clean = []
+    input_dir = folder_paths.get_input_directory()
 
-    return web.json_response({"prompt": res, "error": False})
+    def is_base64(s):
+        return isinstance(s, str) and s.startswith("data:image/")
+
+    def handle_image_input(image_data, filename_base):
+        if is_base64(image_data):
+            try:
+                image = read_base64_image(image_data)
+                timestamp = int(time.time())
+                # Use a short hash to minimize collision chance but keep filename reasonable
+                hash_part = hashlib.sha1(str(timestamp).encode() + image_data.encode()).hexdigest()[:8]
+                filename = f"{filename_base}_{timestamp}_{hash_part}.png"
+                filepath = os.path.join(input_dir, filename)
+                image.save(filepath)
+                print(f"Saved temporary image to {filepath}")
+                temp_files_to_clean.append(filepath)
+                return filepath
+            except Exception as e:
+                print(f"Error processing base64 image for {filename_base}: {e}")
+                # Raise or return error? For now, let it raise to signal failure.
+                raise ValueError(f"Invalid base64 data for {filename_base}") from e
+        elif isinstance(image_data, str): # Assume filename
+            return folder_paths.get_annotated_filepath(image_data)
+        else: # Handle None or other invalid types
+            return None
+
+    try:
+        original_image_path = handle_image_input(original_image_data, "guess_original")
+        if not original_image_path or not os.path.exists(original_image_path):
+             return web.json_response({"error": f"Original image not found or invalid: {original_image_data}"}, status=400)
+             
+        original_image_tensor = load_and_preprocess_image(original_image_path)
+        
+        add_color_image_path = None
+        if add_color_image_data:
+            add_color_image_path = handle_image_input(add_color_image_data, "guess_add_color")
+            if not add_color_image_path or not os.path.exists(add_color_image_path):
+                print(f"Warning: Add color image specified but not found or invalid: {add_color_image_data}. Using original image.")
+                add_color_image_path = None # Fallback
+
+        if add_color_image_path:
+            add_color_image_tensor = load_and_preprocess_image(add_color_image_path)
+        else:
+            add_color_image_tensor = original_image_tensor # Fallback to original
+        
+        add_edge_image_path = None
+        if add_edge_image_data:
+            add_edge_image_path = handle_image_input(add_edge_image_data, "guess_add_edge")
+            if not add_edge_image_path or not os.path.exists(add_edge_image_path):
+                 print(f"Warning: Add edge image specified but not found or invalid: {add_edge_image_data}. Ignoring add edge.")
+                 add_edge_image_path = None # Ignore if invalid
+
+        width, height = original_image_tensor.shape[2], original_image_tensor.shape[1] # Corrected order W, H
+        add_edge_mask = create_alpha_mask(add_edge_image_path) if add_edge_image_path else torch.zeros((1, height, width), dtype=torch.float32, device="cpu")
+
+        # Ensure mask dimensions match original image
+        if add_edge_mask.shape[1] != height or add_edge_mask.shape[2] != width:
+             add_edge_mask = F.interpolate(add_edge_mask.unsqueeze(0), size=(height, width), mode='nearest').squeeze(0)
+
+        res = MagicQuill.guess_prompt(original_image_tensor, add_color_image_tensor, add_edge_mask)
+
+        return web.json_response({"prompt": res, "error": False})
+
+    except Exception as e:
+        import traceback
+        traceback_str = traceback.format_exc()
+        print(f"Error in guess_prompt_handler: {str(e)}\nTraceback: {traceback_str}")
+        return web.json_response({"error": str(e), "traceback": traceback_str}, status=500)
+    finally:
+        # Cleanup temporary files
+        for filepath in temp_files_to_clean:
+            try:
+                if os.path.exists(filepath):
+                    os.remove(filepath)
+                    print(f"Cleaned up temporary file: {filepath}")
+            except Exception as e:
+                print(f"Warning: Error cleaning up temporary file {filepath}: {str(e)}")
 
 @PromptServer.instance.routes.post("/magic_quill/run")
 async def run_magic_quill(request):
@@ -194,6 +262,8 @@ async def run_magic_quill(request):
         clip = out[1]
         vae = out[2]
         
+        random_seed = random.randint(0, 0xffffffffffffffff)
+        
         if not model or not vae or not clip:
             return web.json_response({"error": "Missing required model objects"}, status=400)
             
@@ -207,7 +277,8 @@ async def run_magic_quill(request):
         edge_strength = post.get("edge_strength", 0.5)
         color_strength = post.get("color_strength", 0.5)
         inpaint_strength = post.get("inpaint_strength", 1.0)
-        seed = post.get("seed", 0)
+        
+        seed = post.get("seed", random_seed) #Random seed
         steps = post.get("steps", 20)
         cfg = post.get("cfg", 4.0)
         sampler_name = post.get("sampler_name", "euler_ancestral")
@@ -239,7 +310,7 @@ async def run_magic_quill(request):
             steps=steps,
             cfg=cfg,
             sampler_name=sampler_name,
-            scheduler=scheduler
+            scheduler=scheduler,
         )
 
         # Convert the result tensors to base64
@@ -326,9 +397,18 @@ class MagicQuill(object):
                 "cfg": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "display": "slider"}),
                 "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler_ancestral"}),
                 "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "exponential"}),
+                # "optional_original_image_name": ("STRING", {"default": ""}),
+                # "optional_add_color_image_name": ("STRING", {"default": ""}),
+                # "optional_add_edge_image_name": ("STRING", {"default": ""}),
+                # "optional_remove_edge_image_name": ("STRING", {"default": ""}),
             },
             "optional": {
-             
+                "optional_image": ("IMAGE",),
+                "optional_image_mask": ("MASK",),
+                "optional_original_image": ("IMAGE",),
+                "optional_add_color_image": ("IMAGE",),
+                "optional_add_edge_mask": ("MASK",),
+                "optional_remove_edge_mask": ("MASK",),
             }
         }
 
@@ -341,6 +421,7 @@ class MagicQuill(object):
     @classmethod
     def prepare_images_and_masks(cls, image, original_image, add_color_image, add_edge_image, remove_edge_image):
         # Handle file path inputs
+        print(f"image: {image} original_image: {original_image} add_color_image: {add_color_image} add_edge_image: {add_edge_image} remove_edge_image: {remove_edge_image}")
         image_path = folder_paths.get_annotated_filepath(image)
         image_tensor = load_and_preprocess_image(image_path)
         height, width = image_tensor.shape[1], image_tensor.shape[2]
@@ -373,20 +454,42 @@ class MagicQuill(object):
 
     @classmethod
     def guess_prompt(cls, original_image_tensor, add_color_image_tensor, add_edge_mask):
+        cls.llavaModel.load_model()
         description, ans1, ans2 = cls.llavaModel.process(original_image_tensor, add_color_image_tensor, add_edge_mask)
         ans_list = []
         if ans1 and ans1 != "":
             ans_list.append(ans1)
         if ans2 and ans2 != "":
             ans_list.append(ans2)
-
+        cls.llavaModel.unload_model()
         return ", ".join(ans_list)
 
     @classmethod
-    def painter_execute(cls, image, original_image, add_color_image, add_edge_image, remove_edge_image, model, vae, clip, base_model_version, positive_prompt, negative_prompt, dtype, grow_size, stroke_as_edge, fine_edge, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler):
+    def painter_execute(cls, image, original_image, add_color_image, add_edge_image, remove_edge_image, model, vae, clip, base_model_version, positive_prompt, negative_prompt, dtype, grow_size, stroke_as_edge, fine_edge, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler, optional_image = None, optional_image_mask = None, optional_original_image = None, optional_original_image_mask = None, optional_add_color_image = None, optional_add_color_image_mask = None, optional_add_edge_mask = None, optional_add_edge_mask_mask = None, optional_remove_edge_mask = None, optional_remove_edge_mask_mask = None):
         print(f"model: {model} vae: {vae} clip: {clip} base_model_version: {base_model_version} positive_prompt: {positive_prompt} negative_prompt: {negative_prompt} dtype: {dtype} grow_size: {grow_size} stroke_as_edge: {stroke_as_edge} fine_edge: {fine_edge} edge_strength: {edge_strength} color_strength: {color_strength} inpaint_strength: {inpaint_strength} seed: {seed} steps: {steps} cfg: {cfg} sampler_name: {sampler_name} scheduler: {scheduler}")
         print(f"original_image: {original_image} add_color_image: {add_color_image} add_edge_image: {add_edge_image} remove_edge_image: {remove_edge_image}")
-        add_color_image, original_image, total_mask, add_edge_mask, remove_edge_mask = cls.prepare_images_and_masks(image, original_image, add_color_image, add_edge_image, remove_edge_image)
+        print(f"optional_image: {optional_image} optional_image_mask: {optional_image_mask} optional_original_image: {optional_original_image} optional_original_image_mask: {optional_original_image_mask} optional_add_color_image: {optional_add_color_image} optional_add_color_image_mask: {optional_add_color_image_mask} optional_add_edge_mask: {optional_add_edge_mask} optional_add_edge_mask_mask: {optional_add_edge_mask_mask} optional_remove_edge_mask: {optional_remove_edge_mask} optional_remove_edge_mask_mask: {optional_remove_edge_mask_mask}")
+        # check if optional_original_image is tensor
+        if isinstance(optional_image, torch.Tensor):
+            image = optional_image
+        if isinstance(optional_original_image, torch.Tensor):
+            original_image = optional_original_image
+        if isinstance(optional_add_color_image, torch.Tensor):
+            add_color_image = optional_add_color_image
+        if isinstance(optional_add_edge_mask, torch.Tensor):
+            add_edge_mask = optional_add_edge_mask
+        if isinstance(optional_remove_edge_mask, torch.Tensor):
+            remove_edge_mask = optional_remove_edge_mask       
+          
+        if isinstance(optional_image, torch.Tensor) and isinstance(optional_image_mask, torch.Tensor):
+            #if if not the same size, resize the mask
+            if optional_image_mask.shape[1] != optional_image.shape[1] or optional_image_mask.shape[2] != optional_image.shape[2]:
+                print("resizing mask")
+                optional_image_mask = F.interpolate(optional_image_mask.unsqueeze(0), size=(optional_image.shape[1], optional_image.shape[2]), mode='nearest').squeeze(0)
+            total_mask = optional_image_mask
+          
+        if not isinstance(image, torch.Tensor) and not isinstance(original_image, torch.Tensor) and not isinstance(add_color_image, torch.Tensor) and not isinstance(add_edge_image, torch.Tensor) and not isinstance(remove_edge_image, torch.Tensor):
+            add_color_image, original_image, total_mask, add_edge_mask, remove_edge_mask = cls.prepare_images_and_masks(image, original_image, add_color_image, add_edge_image, remove_edge_image)
 
         if torch.sum(remove_edge_mask).item() > 0 and torch.sum(add_edge_mask).item() == 0:
             if positive_prompt == "":
@@ -425,9 +528,5 @@ class MagicQuill(object):
         return m.digest().hex()
 
     @classmethod
-    def VALIDATE_INPUTS(self, image, original_image, add_color_image, add_edge_image, remove_edge_image, model, vae, clip, base_model_version, positive_prompt, negative_prompt, dtype, grow_size, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler):
+    def VALIDATE_INPUTS(self, image, original_image, add_color_image, add_edge_image, remove_edge_image, model, vae, clip, base_model_version, positive_prompt, negative_prompt, dtype, grow_size, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler, optional_image = None):
-        if not folder_paths.exists_annotated_filepath(image):
-            print(image)
-            return "Invalid image file: {}".format(image)
-
         return True

pyproject.toml

@@ -1,35 +1,77 @@
+[tool.poetry]
+name = "ComfyUI-MagicQuill"
+version = "1.0.4"
+description = "Fixed version of the original MagicQuill node."
+authors = ["brantje <brantje@gmail.com>"]
+license = { text = "MIT License" }
+readme = "README.md"
+
+[tool.poetry.dependencies]
+
+pynvml = "^11.4.0"
+
+
 [build-system]
-requires = ["setuptools>=61.0"]
+requires = ["poetry-core"]
-build-backend = "setuptools.build_meta"
+build-backend = "poetry.core.masonry.api"
 
 [project]
-name = "llava"
+name = "comfyui_magicquill_fixed" # Unique identifier for your node. Immutable after creation..
-version = "1.2.2.post1"
+description = "Fixed version of the original MagicQuill node. Required nodes: ComfyUI-Brushnet and ComfyUI Controlnet AUX"
-description = "Towards GPT-4 like large language and visual assistant."
+version = "1.0.4" # Custom Node version. Must be semantically versioned.
-readme = "README.md"
+dependencies  = [
-requires-python = ">=3.8"
+    'opencv-python',
-classifiers = [
+    'diffusers',
-    "Programming Language :: Python :: 3",
+    'torchsde',
-    "License :: OSI Approved :: Apache Software License",
+    'protobuf',
-]
+    'transformers',
-dependencies = [
+    'tokenizers',
-    "transformers==4.37.2", "tokenizers==0.15.1", "sentencepiece==0.2.0", "shortuuid",
+    'sentencepiece',
-    "accelerate==0.33.0", "peft", "bitsandbytes",
+    'shortuuid',
-    "pydantic", "markdown2[all]", "numpy", "scikit-learn==1.2.2",
+    'accelerate',
-    "gradio==5.4.0", "gradio_client==1.4.2", "requests", "httpx==0.24.1", "uvicorn", "fastapi",
+    'peft',
-    "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13",
+    'bitsandbytes',
-]
+    'pydantic',
-
+    'markdown2',
-[project.optional-dependencies]
+    'scikit-learn',
-train = ["deepspeed==0.12.6", "ninja", "wandb"]
+    'requests',
-build = ["build", "twine"]
+    'httpx',
+    'uvicorn',
+    'fastapi',
+    'einops',
+    'einops-exts',
+    'timm',
+    'webcolors',
+    'torch',
+    'importlib_metadata',
+    'huggingface_hub',
+    'scipy',
+    'opencv-python',
+    'filelock',
+    'numpy',
+    'Pillow',
+    'einops',
+    'torchvision',
+    'pyyaml',
+    'scikit-image',
+    'python-dateutil',
+    'mediapipe',
+    'svglib',
+    'fvcore',
+    'yapf',
+    'omegaconf',
+    'ftfy',
+    'addict',
+    'yacs',
+    'trimesh[easy]',
+    'albumentations',
+    'scikit-learn',
+    'matplotlib'
+] # Filled in from requirements.txt
 
 [project.urls]
-"Homepage" = "https://llava-vl.github.io"
+Repository = "https://github.com/brantje/ComfyUI_MagicQuill"
-"Bug Tracker" = "https://github.com/haotian-liu/LLaVA/issues"
 
-[tool.setuptools.packages.find]
+[tool.comfy]
-exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
+PublisherId = "brantje"
-
+DisplayName = "ComfyUI MagicQuill (fixed)"
-[tool.wheel]
-exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]

requirements.txt

@@ -1,9 +1,8 @@
-webcolors==1.13
 opencv-python
 diffusers
 torchsde==0.2.6
 protobuf==4.25.4
-transformers==4.37.2 
+transformers==4.38.0 
 tokenizers==0.15.1 
 sentencepiece==0.2.0
 shortuuid
@@ -20,7 +19,7 @@ fastapi
 einops==0.6.1
 einops-exts==0.0.4
 timm==0.6.13
-
+webcolors==24.11.1
 torch
 importlib_metadata
 huggingface_hub

scribble_color_edit.py

@@ -4,14 +4,46 @@ import torch
 import sys
 import torch.utils._pytree as pytree
 import numpy as np
-
+import subprocess
 current_dir = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(current_dir)
-sys.path.append(os.path.abspath(os.path.join(current_dir, '..')))
+custom_nodes_dir = os.path.abspath(os.path.join(current_dir, '..'))
+sys.path.append(custom_nodes_dir)
 sys.path.append(os.path.abspath(os.path.join(current_dir, '..', '..', 'comfy_extras')))
-print(sys.path)
 
-from ComfyUI_BrushNet.brushnet_nodes import BrushNetLoader, BrushNet, BlendInpaint, get_files_with_extension
+
+brushnet_hyphen_dir = os.path.join(custom_nodes_dir, 'comfyui-brushnet')
+brushnet_underscore_dir = os.path.join(custom_nodes_dir, 'comfyui_brushnet')
+
+if not os.path.exists(brushnet_underscore_dir):
+    print(f"Creating symlink from {brushnet_hyphen_dir} to {brushnet_underscore_dir}")
+    
+    # Create the symlink - use different methods based on OS
+    if os.name == 'nt':  # Windows
+        # Requires admin privileges or developer mode
+        subprocess.run(['mklink', '/D', brushnet_underscore_dir, brushnet_hyphen_dir], shell=True)
+    else:  # Unix/Linux/Mac
+        os.symlink(brushnet_hyphen_dir, brushnet_underscore_dir)
+    
+    print(f"Symlink created: {os.path.exists(brushnet_underscore_dir)}")
+
+# Now try importing from the symlinked directory
+try:
+    # Add to path
+    sys.path.append(custom_nodes_dir)
+    
+    # Import from symlinked directory
+    from comfyui_brushnet.brushnet_nodes import BrushNetLoader, BrushNet, BlendInpaint, get_files_with_extension
+    print("Successfully imported from symlinked directory")
+except ImportError as e:
+    print(f"Import from symlink failed: {e}")
+    try:
+        from ComfyUI_BrushNet.brushnet_nodes import BrushNetLoader, BrushNet, BlendInpaint, get_files_with_extension
+    except ImportError as e:
+        print(f"Import from ComfyUI_BrushNet failed: {e}")
+        raise ImportError("Failed to import even with ComfyUI_BrushNet. Please check file permissions and structure.")
+
+
 from comfyui_controlnet_aux.node_wrappers.lineart import LineArt_Preprocessor
 from comfyui_controlnet_aux.node_wrappers.pidinet import PIDINET_Preprocessor
 from comfyui_controlnet_aux.node_wrappers.color import Color_Preprocessor

utils.py

@@ -34,8 +34,8 @@ def get_colored_contour(img1, img2, threshold=10):
 
 def closest_colour(requested_colour):
     min_colours = {}
-    for key, name in webcolors.CSS3_HEX_TO_NAMES.items():
+    for name in webcolors.names("css3"):
-        r_c, g_c, b_c = webcolors.hex_to_rgb(key)
+        r_c, g_c, b_c = webcolors.name_to_rgb(name)
         rd = (r_c - requested_colour[0].item()) ** 2
         gd = (g_c - requested_colour[1].item()) ** 2
         bd = (b_c - requested_colour[2].item()) ** 2