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fixed readme
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readme.md
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# ComfyUI LoRA Metadata Scraper
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This is a Python tool
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- Extracts embedded metadata from `.safetensors` files
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- Optionally scrapes CivitAI metadata (tags, descriptions, trained words)
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- Downloads preview images and videos
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- Smart deduplication: skips files that already have metadata or previews
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- Lets you scan only the LoRA folder or your entire ComfyUI setup
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- Lets you choose how many preview images/videos to download (or none)
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- Saves previews in a subdirectory or alongside your model files
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Place everything inside:
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ComfyUI/utils/Scrapertool/
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Example structure:
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ComfyUI/
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βββ utils/
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βββ Scrapertool/
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βββ
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βββ requirements.txt
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βββ
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### Option 1: Interactive Mode
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Run:
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python
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Youβll be prompted:
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E) Save previews in a subdirectory? (Y/N)
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F) How many preview images/videos to download? (A=All, N=None, or a number)
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Example:
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python
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--------------------
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--interactive
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Launch
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--scrape-civitai
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Enable scraping of metadata and previews from CivitAI.
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Set the delay between API calls and image/video downloads (default: 0.5 seconds).
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--force
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Force re-
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--loras-only
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Scan only the ComfyUI/models/loras folder. If omitted, scans all subdirectories.
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Save previews inside a dedicated subdirectory (e.g., model_previews/).
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--no-previews-subdir
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Save previews directly
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--max-media [number]
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Set
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--------------------
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1. First,
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2. If not found, it falls back to looking up the fileβs SHA256 hash using the CivitAI API.
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This ensures
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## Dependencies
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This script requires the following Python packages:
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safetensors
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torch
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requests
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tqdm
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## How to Set Up
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1. Create a virtual environment
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2. Activate the virtual environment:
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3. Install dependencies:
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## requirements.txt
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safetensors
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torch
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requests
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tqdm
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## License
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Creative Commons Attribution 4.0 International (CC BY 4.0)
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# ComfyUI LoRA Metadata Scraper
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This is a Python tool to recursively extract metadata from `.safetensors` files inside your ComfyUI installation. It supports both local metadata extraction and optional CivitAI scraping (for trigger words, tags, descriptions, and preview images/videos).
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Features:
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- Extracts metadata from LoRAs, checkpoints, VAEs, and ControlNets
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- Optionally scrapes CivitAI data (tags, trigger words, preview images/videos)
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- Skips files that are already processed (unless forced)
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- Lets you scan only the LoRAs folder or the entire ComfyUI setup
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- Saves previews in subdirectories or alongside model files
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- Supports downloading all, none, or a specific number of previews
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- Fully interactive OR command-line friendly
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- Gracefully handles CivitAI rate limiting and retries
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Folder Setup:
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Place everything inside:
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ComfyUI/
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βββ utils/
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βββ Scrapertool/
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βββ scrape_metadata_recursive.py
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βββ requirements.txt
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βββ run_scraper.bat (optional launcher)
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Usage:
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Option 1: Interactive Mode
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Run:
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python scrape_metadata_recursive.py --interactive
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Youβll be prompted:
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E) Save previews in a subdirectory? (Y/N)
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F) How many preview images/videos to download? (A=All, N=None, or a number)
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Option 2: Command-Line Arguments
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Example:
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python scrape_metadata_recursive.py --scrape-civitai --delay 0.5 --force --loras-only --previews-subdir --max-media 5
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Command-Line Arguments:
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--interactive
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Launch interactive menu.
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--scrape-civitai
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Enable scraping of metadata and previews from CivitAI.
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Set the delay between API calls and image/video downloads (default: 0.5 seconds).
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--force
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Force re-scrape even if metadata JSON already exists.
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--loras-only
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Scan only the ComfyUI/models/loras folder. If omitted, scans all subdirectories.
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Save previews inside a dedicated subdirectory (e.g., model_previews/).
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--no-previews-subdir
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Save previews directly alongside the model file.
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--max-media [number]
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Set max number of preview images/videos to download (0 = none, default = all).
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How It Finds CivitAI Metadata:
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1. First, the script checks if the `.safetensors` file contains a `civitai_metadata` block (fixed in the latest version to look in the correct place).
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2. If that block is missing, it falls back to computing the SHA256 hash and queries CivitAI using that.
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This ensures compatibility even if metadata is incomplete.
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Dependencies:
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safetensors
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torch
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requests
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tqdm
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Setup Instructions:
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1. Create a virtual environment:
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python -m venv scrapervenv
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2. Activate the virtual environment:
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On Windows:
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scrapervenv\Scripts\activate
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On macOS/Linux:
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source scrapervenv/bin/activate
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3. Install dependencies:
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pip install -r requirements.txt
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requirements.txt:
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safetensors
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torch
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requests
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tqdm
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License:
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Creative Commons Attribution 4.0 International (CC BY 4.0)
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You may use, share, and adapt this tool for any purposeβincluding commercial useβas long as you provide attribution to the original creator.
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Contributing:
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Contributions and improvements are welcome. Please fork, submit pull requests, or open an issue if you'd like to suggest features or report bugs.
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