Claude
549a1a4e82
- Fix My Library: Add CSS styling for library browser, items now visible - Integrate My Library into Shapes tool with tabbed interface - Improve AI Inpaint: Add transform mode for scaling/sizing selections - Add helpful guidance explaining inpaint vs transform modes - Add U2net as alternative background removal (avoids rembg issues) - Create U2net model definition and download script - Improve Caddyfile with multiple options and troubleshooting guide Note: Brush Select (AI Paint) tool was already implemented and working. https://claude.ai/code/session_01CLedz6CanT9t46KBvng3vz
134 lines
4.1 KiB
Python
134 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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Download U2Net model for background removal.
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U2Net is a deep learning model for salient object detection,
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commonly used for background removal tasks.
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Usage:
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python download_u2net_model.py [model_type]
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Model types:
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u2net - Full U2Net model (~176MB, best quality)
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u2netp - Lightweight U2Net (~4MB, faster, good quality)
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u2net_human_seg - Optimized for human segmentation (~176MB)
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Default: u2netp (good balance of quality and speed)
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"""
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import os
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import sys
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import urllib.request
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from pathlib import Path
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# Model URLs (from official U2Net repository releases)
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MODEL_URLS = {
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'u2net': {
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'url': 'https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx',
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'filename': 'u2net.onnx',
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'size_mb': 176
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},
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'u2netp': {
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'url': 'https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx',
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'filename': 'u2netp.onnx',
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'size_mb': 4
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},
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'u2net_human_seg': {
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'url': 'https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx',
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'filename': 'u2net_human_seg.onnx',
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'size_mb': 176
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}
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}
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def download_with_progress(url: str, dest_path: Path, expected_size_mb: int):
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"""Download file with progress indicator."""
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print(f"Downloading from: {url}")
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print(f"Expected size: ~{expected_size_mb}MB")
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def progress_hook(count, block_size, total_size):
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if total_size > 0:
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percent = min(100, count * block_size * 100 // total_size)
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downloaded_mb = count * block_size / (1024 * 1024)
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total_mb = total_size / (1024 * 1024)
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sys.stdout.write(f"\rProgress: {percent}% ({downloaded_mb:.1f}/{total_mb:.1f} MB)")
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sys.stdout.flush()
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try:
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urllib.request.urlretrieve(url, str(dest_path), progress_hook)
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print("\nDownload complete!")
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return True
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except Exception as e:
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print(f"\nDownload failed: {e}")
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return False
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def main():
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# Determine model type
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model_type = 'u2netp' # Default to lightweight model
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if len(sys.argv) > 1:
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model_type = sys.argv[1].lower()
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if model_type not in MODEL_URLS:
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print(f"Unknown model type: {model_type}")
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print(f"Available models: {', '.join(MODEL_URLS.keys())}")
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sys.exit(1)
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model_info = MODEL_URLS[model_type]
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# Determine models directory
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# Check if running in Docker container
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if os.path.exists('/app/data/models'):
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models_dir = Path('/app/data/models')
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else:
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# Local development
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script_dir = Path(__file__).parent
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models_dir = script_dir.parent / 'data' / 'models'
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models_dir.mkdir(parents=True, exist_ok=True)
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dest_path = models_dir / model_info['filename']
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# Check if already downloaded
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if dest_path.exists():
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print(f"Model already exists at: {dest_path}")
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print("Delete the file to re-download.")
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return
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print(f"Downloading U2Net model: {model_type}")
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print(f"Destination: {dest_path}")
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print("")
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success = download_with_progress(
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model_info['url'],
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dest_path,
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model_info['size_mb']
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)
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if success:
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# Create symlink for easier access
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symlink_path = models_dir / 'u2net.onnx'
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if not symlink_path.exists() or symlink_path.is_symlink():
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if symlink_path.is_symlink():
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symlink_path.unlink()
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try:
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symlink_path.symlink_to(dest_path.name)
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print(f"Created symlink: {symlink_path} -> {dest_path.name}")
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except OSError:
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# Symlinks may not work on all systems
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pass
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print(f"\nU2Net model ({model_type}) downloaded successfully!")
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print(f"Location: {dest_path}")
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print("\nYou can now use background removal in the application.")
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else:
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print("\nFailed to download model. Please try again or download manually from:")
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print(f" {model_info['url']}")
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print(f" Save to: {dest_path}")
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sys.exit(1)
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if __name__ == '__main__':
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main()
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