initial release

readme.md

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+# ComfyUI LoRA Metadata Scraper
+
+This is a Python tool that scans a ComfyUI installation and extracts metadata from `.safetensors` files, including LoRAs, checkpoints, VAEs, and ControlNets. It supports both local metadata extraction and optional scraping of CivitAI metadata, including preview images and videos.
+
+What it does:
+- Extracts embedded metadata from `.safetensors` files
+- Optionally scrapes CivitAI metadata (tags, descriptions, trained words)
+- Downloads preview images and videos
+- Smart deduplication: skips files that already have metadata or previews
+- Lets you scan only the LoRA folder or your entire ComfyUI setup
+- Lets you choose how many preview images/videos to download (or none)
+- Saves previews in a subdirectory or alongside your model files
+
+--------------------
+
+## Folder Setup
+
+Place everything inside:
+
+ComfyUI/utils/Scrapertool/
+
+Example structure:
+
+ComfyUI/
+└── utils/
+    └── Scrapertool/
+        ├── scraper_run.py
+        ├── requirements.txt
+        └── start.bat  (optional launcher)
+
+--------------------
+
+## Usage
+
+### Option 1: Interactive Mode
+
+Run:
+
+python scraper_run.py --interactive
+
+You’ll be prompted:
+
+A) Scrape CivitAI? (Y/N)
+B) Use default delay (0.5s), no delay (0), or custom delay?
+C) Force re-scrape if metadata already exists? (Y/N)
+D) Scan only the LoRA folder? (Y/N)
+E) Save previews in a subdirectory? (Y/N)
+F) How many preview images/videos to download? (A=All, N=None, or a number)
+
+### Option 2: Command Line Arguments
+
+Example:
+
+python scraper_run.py --scrape-civitai --delay 0.5 --force --loras-only --previews-subdir --max-media 5
+
+--------------------
+
+## Command-Line Argument Reference
+
+--interactive
+    Launch the interactive menu.
+
+--scrape-civitai
+    Enable scraping of metadata and previews from CivitAI.
+
+--delay [seconds]
+    Set the delay between API calls and image/video downloads (default: 0.5 seconds).
+
+--force
+    Force re-scraping even if a metadata JSON already exists.
+
+--loras-only
+    Scan only the ComfyUI/models/loras folder. If omitted, scans all subdirectories.
+
+--previews-subdir
+    Save previews inside a dedicated subdirectory (e.g., model_previews/).
+
+--no-previews-subdir
+    Save previews directly next to the model file.
+
+--max-media [number]
+    Set how many preview images/videos to download. Use 0 to skip all. (Default: unlimited)
+
+--------------------
+
+## How It Finds CivitAI Metadata
+
+1. First, it checks if the `.safetensors` file includes a `civitai_metadata` block (newly fixed!).
+   - If found, it extracts the `civitai_model_id` from that nested block.
+2. If not found, it falls back to looking up the file’s SHA256 hash using the CivitAI API.
+
+This ensures maximum coverage even if metadata is incomplete.
+
+--------------------
+
+## Dependencies
+
+This script requires the following Python packages:
+
+safetensors
+torch
+requests
+tqdm
+
+--------------------
+
+## How to Set Up
+
+1. Create a virtual environment (recommended):
+
+   python -m venv scrapervenv
+
+2. Activate the virtual environment:
+
+   On Windows:
+   scrapervenv\Scripts\activate
+
+   On macOS/Linux:
+   source scrapervenv/bin/activate
+
+3. Install dependencies:
+
+   pip install -r requirements.txt
+
+--------------------
+
+## requirements.txt
+
+safetensors
+torch
+requests
+tqdm
+
+--------------------
+
+## License
+
+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+This tool is free to use, share, and adapt for any purpose, including commercial use, as long as you provide attribution to the original creator.
+
+--------------------

requirements.txt

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+safetensors
+requests
+tqdm
+torch

scraper_run.py

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+import os
+import json
+import argparse
+import hashlib
+import requests
+import time
+from safetensors import safe_open
+from tqdm import tqdm
+
+MAX_RETRIES = 3
+BACKOFF_BASE = 2
+
+def retry_request(url, timeout=10, desc=""):
+    attempt = 0
+    while attempt < MAX_RETRIES:
+        try:
+            response = requests.get(url, timeout=timeout)
+            if response.status_code == 200:
+                return response
+            elif response.status_code == 429:
+                tqdm.write(f"{desc} - Rate limited (HTTP 429). Retrying ({attempt + 1}/{MAX_RETRIES})...")
+            else:
+                tqdm.write(f"{desc} - HTTP {response.status_code}. Retrying ({attempt + 1}/{MAX_RETRIES})...")
+        except requests.exceptions.RequestException as e:
+            tqdm.write(f"{desc} - Error: {e}. Retrying ({attempt + 1}/{MAX_RETRIES})...")
+
+        attempt += 1
+        time.sleep(BACKOFF_BASE * (2 ** (attempt - 1)))
+    tqdm.write(f"{desc} - Failed after {MAX_RETRIES} attempts.")
+    return None
+
+def compute_sha256(file_path):
+    sha256_hash = hashlib.sha256()
+    with open(file_path, "rb") as f:
+        for byte_block in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(byte_block)
+    return sha256_hash.hexdigest()
+
+def extract_local_metadata(safetensors_file):
+    try:
+        with safe_open(safetensors_file, framework="pt") as f:
+            metadata = f.metadata()
+        return metadata
+    except Exception as e:
+        tqdm.write(f"Error reading {safetensors_file}: {e}")
+        return None
+
+def fetch_civitai_metadata_by_model_id(model_id):
+    url = f"https://civitai.com/api/v1/models/{model_id}"
+    response = retry_request(url, desc=f"Fetching metadata for model ID {model_id}")
+    if response and response.status_code == 200:
+        return response.json()
+    return None
+
+def fetch_civitai_metadata_by_hash(file_hash):
+    url = f"https://civitai.com/api/v1/model-versions/by-hash/{file_hash}"
+    response = retry_request(url, desc=f"Fetching metadata by hash {file_hash[:10]}...")
+    if response and response.status_code == 200:
+        return response.json()
+    return None
+
+def download_preview_images(images_list, save_dir, base_filename, delay=0.5, image_pbar=None, use_subdir=True):
+    if use_subdir:
+        subdir = os.path.join(save_dir, f"{base_filename}_previews")
+        os.makedirs(subdir, exist_ok=True)
+    else:
+        subdir = save_dir  # save in same folder
+
+    for idx, img_data in enumerate(images_list):
+        url = img_data.get('url')
+        if not url:
+            continue
+        ext = os.path.splitext(url)[1].split('?')[0]
+        img_name = f"{base_filename}_preview_{idx+1}{ext}"
+
+        # Paths to check (both flat + subdir)
+        flat_img_path = os.path.join(save_dir, img_name)
+        subdir_img_path = os.path.join(subdir, img_name)
+
+        if os.path.exists(flat_img_path) or os.path.exists(subdir_img_path):
+            tqdm.write(f"Preview already exists: {img_name} (skipping)")
+            if image_pbar:
+                image_pbar.update(1)
+            continue
+
+        desc = f"Downloading media {idx + 1}/{len(images_list)}"
+        response = retry_request(url, desc=desc)
+        if response and response.status_code == 200:
+            img_path = subdir_img_path if use_subdir else flat_img_path
+            with open(img_path, 'wb') as img_file:
+                img_file.write(response.content)
+            tqdm.write(f"Saved preview: {img_path}")
+        if image_pbar:
+            image_pbar.update(1)
+        time.sleep(delay)
+
+def process_directory(root_dir, force=False, scrape_civitai=False, delay=0.5, previews_subdir=True, max_media=None):
+    safetensors_files = []
+    for dirpath, dirnames, filenames in os.walk(root_dir):
+        for filename in filenames:
+            if filename.endswith(".safetensors"):
+                safetensors_files.append(os.path.join(dirpath, filename))
+
+    print(f"\nFound {len(safetensors_files)} .safetensors files.\n")
+
+    with tqdm(total=len(safetensors_files), desc="Total Progress", unit="file") as total_pbar:
+        for safetensors_path in safetensors_files:
+            dirpath = os.path.dirname(safetensors_path)
+            filename = os.path.basename(safetensors_path)
+            base_filename = os.path.splitext(filename)[0]
+            json_filename = f"{base_filename}.metadata.json"
+            json_path = os.path.join(dirpath, json_filename)
+
+            if os.path.exists(json_path) and not force:
+                tqdm.write(f"Skipping (metadata exists): {safetensors_path}")
+                total_pbar.update(1)
+                continue
+
+            tqdm.write(f"\nProcessing: {safetensors_path}")
+            metadata = extract_local_metadata(safetensors_path)
+            combined_metadata = {'local_metadata': metadata if metadata else {}}
+
+            civitai_data = None
+
+            if scrape_civitai:
+                civitai_model_id = None
+                if metadata:
+                    if 'ss_civitai_model_id' in metadata:
+                        civitai_model_id = metadata['ss_civitai_model_id']
+                    elif 'ss_civitai_url' in metadata:
+                        parts = metadata['ss_civitai_url'].split('/')
+                        civitai_model_id = next((part for part in parts if part.isdigit()), None)
+                
+                if civitai_model_id:
+                    tqdm.write(f"Found model ID in metadata: {civitai_model_id}")
+                    civitai_data = fetch_civitai_metadata_by_model_id(civitai_model_id)
+                    time.sleep(delay)
+                else:
+                    tqdm.write("No CivitAI model ID found in metadata. Trying hash lookup...")
+                    file_hash = compute_sha256(safetensors_path)
+                    civitai_data = fetch_civitai_metadata_by_hash(file_hash)
+                    time.sleep(delay)
+
+                if civitai_data:
+                    civitai_meta = {
+                        'civitai_model_id': civitai_data.get('modelId') or civitai_data.get('id'),
+                        'civitai_model_version_id': civitai_data.get('id'),
+                        'civitai_name': civitai_data.get('name'),
+                        'description': civitai_data.get('description'),
+                        'tags': civitai_data.get('tags'),
+                        'trainedWords': civitai_data.get('trainedWords'),
+                        'images': civitai_data.get('images')
+                    }
+                    combined_metadata['civitai_metadata'] = civitai_meta
+
+                    images_list = civitai_meta.get('images', [])
+                    if images_list:
+                        # Apply max_media logic
+                        if max_media == 0:
+                            tqdm.write("Skipping download of preview images/videos (user selected 0).")
+                        else:
+                            if max_media is not None:
+                                images_list = images_list[:max_media]
+                            with tqdm(total=len(images_list), desc="Image/Video Progress", leave=False) as image_pbar:
+                                download_preview_images(
+                                    images_list,
+                                    dirpath,
+                                    base_filename,
+                                    delay=delay,
+                                    image_pbar=image_pbar,
+                                    use_subdir=previews_subdir
+                                )
+                    else:
+                        tqdm.write("No preview images/videos found.")
+                else:
+                    tqdm.write("No CivitAI data found (model ID or hash lookup failed).")
+
+            with open(json_path, "w", encoding="utf-8") as f:
+                json.dump(combined_metadata, f, indent=4, ensure_ascii=False)
+            tqdm.write(f"Saved metadata to: {json_path}")
+            total_pbar.update(1)
+
+def interactive_menu():
+    print("\n=== LoRA Metadata Scraper Config ===\n")
+    scrape_civitai = input("A) Scrape CivitAI? (Y/N) [Default: N]: ").strip().lower() == 'y'
+    delay_choice = input("B) Use default delay (0.5s), no delay (0), or custom? (D/N/C) [Default: D]: ").strip().lower()
+    if delay_choice == 'n':
+        delay = 0.0
+    elif delay_choice == 'c':
+        delay = float(input("Enter delay in seconds (e.g., 0.5): ").strip())
+    else:
+        delay = 0.5
+    force = input("C) Force re-scrape if metadata exists? (Y/N) [Default: N]: ").strip().lower() == 'y'
+    loras_only = input("D) Scan only the LoRAs folder? (Y/N) [Default: Y]: ").strip().lower() != 'n'
+    previews_subdir = input("E) Save preview images in a subdirectory? (Y/N) [Default: Y]: ").strip().lower() != 'n'
+    media_choice = input("F) How many preview images/videos to download? (A=All [default], N=None, X=Number): ").strip().lower()
+    if media_choice == 'n':
+        max_media = 0
+    elif media_choice == 'a' or media_choice == '':
+        max_media = None
+    else:
+        try:
+            max_media = int(media_choice)
+        except ValueError:
+            max_media = None  # fallback to all
+    print("\n=== Starting with your selected options ===\n")
+    return force, scrape_civitai, delay, loras_only, previews_subdir, max_media
+
+if __name__ == "__main__":
+    print(">>> Script started")
+
+    parser = argparse.ArgumentParser(description="Scrape and save metadata for .safetensors files.")
+    parser.add_argument("--force", action="store_true", help="Force re-scrape even if metadata file exists.")
+    parser.add_argument("--scrape-civitai", action="store_true", help="Enable scraping CivitAI metadata + images.")
+    parser.add_argument("--delay", type=float, default=0.5, help="Delay time (seconds) between API/image steps (default: 0.5s).")
+    parser.add_argument("--interactive", action="store_true", help="Run in interactive mode.")
+    parser.add_argument("--loras-only", action="store_true", help="Scan only the LoRAs folder (models/loras).")
+    parser.add_argument("--previews-subdir", dest="previews_subdir", action="store_true", help="Save preview images in a subdirectory.")
+    parser.add_argument("--no-previews-subdir", dest="previews_subdir", action="store_false", help="Save preview images in the same folder.")
+    parser.add_argument("--max-media", type=int, default=None, help="Max number of preview images/videos to download (0 = none).")
+    parser.set_defaults(previews_subdir=True)
+
+    args = parser.parse_args()
+
+    if args.interactive:
+        force, scrape_civitai, delay, loras_only, previews_subdir, max_media = interactive_menu()
+    else:
+        force, scrape_civitai, delay, loras_only, previews_subdir, max_media = (
+            args.force,
+            args.scrape_civitai,
+            args.delay,
+            args.loras_only,
+            args.previews_subdir,
+            args.max_media
+        )
+
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    if loras_only:
+        comfyui_dir = os.path.abspath(os.path.join(script_dir, "..", "..", "models", "loras"))
+    else:
+        comfyui_dir = os.path.abspath(os.path.join(script_dir, "..", ".."))
+
+    tqdm.write(f"Scanning directory: {comfyui_dir}")
+
+    process_directory(
+        comfyui_dir,
+        force=force,
+        scrape_civitai=scrape_civitai,
+        delay=delay,
+        previews_subdir=previews_subdir,
+        max_media=max_media
+    )

start.bat

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+@echo off
+SET VENV_DIR=scrapervenv
+
+echo [INFO] Checking virtual environment...
+if not exist %VENV_DIR% (
+    echo [INFO] Creating virtual environment...
+    python -m venv %VENV_DIR%
+)
+
+echo [INFO] Activating virtual environment...
+call %VENV_DIR%\Scripts\activate
+
+echo [INFO] Installing dependencies...
+pip install -r requirements.txt
+
+echo [INFO] Running scraper (interactive mode)...
+
+python scraper_run.py --interactive
+
+echo [INFO] Done.
+pause