Remove repository cloning code from install.py and update README.md to include notes on required repositories.

Api call

.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)

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)
-    original_image = json_data.get("original_image", None)
-    add_edge_image = json_data.get("add_edge_image", None)
+    add_color_image_data = json_data.get("add_color_image", None)
+    original_image_data = json_data.get("original_image", None)
+    add_edge_image_data = json_data.get("add_edge_image", None)
 
-    original_image_path = folder_paths.get_annotated_filepath(original_image)
-    original_image_tensor = load_and_preprocess_image(original_image_path)
-    
-    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")
+    if not original_image_data:
+        return web.json_response({"error": "Original image is required."}, status=400)
 
-    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")

pyproject.toml

@@ -1,35 +1,77 @@
+[tool.poetry]
+name = "ComfyUI-MagicQuill"
+version = "1.0.0"
+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"]
-build-backend = "setuptools.build_meta"
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
 
 [project]
-name = "llava"
-version = "1.2.2.post1"
-description = "Towards GPT-4 like large language and visual assistant."
-readme = "README.md"
-requires-python = ">=3.8"
-classifiers = [
-    "Programming Language :: Python :: 3",
-    "License :: OSI Approved :: Apache Software License",
-]
-dependencies = [
-    "transformers==4.37.2", "tokenizers==0.15.1", "sentencepiece==0.2.0", "shortuuid",
-    "accelerate==0.33.0", "peft", "bitsandbytes",
-    "pydantic", "markdown2[all]", "numpy", "scikit-learn==1.2.2",
-    "gradio==5.4.0", "gradio_client==1.4.2", "requests", "httpx==0.24.1", "uvicorn", "fastapi",
-    "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13",
-]
-
-[project.optional-dependencies]
-train = ["deepspeed==0.12.6", "ninja", "wandb"]
-build = ["build", "twine"]
+name = "comfyui_magicquill_fixed" # Unique identifier for your node. Immutable after creation..
+description = "Fixed version of the original MagicQuill node. Required nodes: ComfyUI-Brushnet and ComfyUI Controlnet AUX"
+version = "1.0.0" # Custom Node version. Must be semantically versioned.
+dependencies  = [
+    'opencv-python',
+    'diffusers',
+    'torchsde',
+    'protobuf',
+    'transformers',
+    'tokenizers',
+    'sentencepiece',
+    'shortuuid',
+    'accelerate',
+    'peft',
+    'bitsandbytes',
+    'pydantic',
+    'markdown2',
+    'scikit-learn',
+    'requests',
+    '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"
-"Bug Tracker" = "https://github.com/haotian-liu/LLaVA/issues"
+Repository = "https://github.com/brantje/ComfyUI_MagicQuill"
 
-[tool.setuptools.packages.find]
-exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
-
-[tool.wheel]
-exclude = ["assets*", "benchmark*", "docs", "dist*", "playground*", "scripts*", "tests*"]
+[tool.comfy]
+PublisherId = "brantje"
+DisplayName = "ComfyUI MagicQuill (fixed)"

requirements.txt

@@ -1,4 +1,3 @@
-webcolors==1.13
 opencv-python
 diffusers
 torchsde==0.2.6
@@ -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

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():
-        r_c, g_c, b_c = webcolors.hex_to_rgb(key)
+    for name in webcolors.names("css3"):
+        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