rembg 2.0.70+ requires Pillow>=12.1.0,<13.0.0 which conflicted with
the pinned Pillow==10.2.0. Updated to use the version range that
satisfies rembg while remaining compatible with scikit-image and
torchvision (both have no upper bound on Pillow).
https://claude.ai/code/session_01MYpjNQXD1fZE4gCweGU4QQ
- Use onnxruntime>=1.17.0 (fixed executable stack issues, no execstack needed)
- Use rembg>=2.0.70 with BiRefNet model support
- Update all remove-background endpoints to use birefnet-general model
- BiRefNet provides better edge detection and matting quality than u2net
- Falls back to default model if BiRefNet unavailable
- Use onnxruntime 1.14.1 (older version without executable stack requirement)
- Make rembg pre-download optional (won't fail build if onnxruntime has issues)
- Remove Background will be disabled if rembg can't load
- Add torch, torchvision, segment-anything to requirements
- Create download_sam_model.py script to fetch SAM checkpoint
- Update tools.py to use local SAM with Replicate API fallback
- Add SAM model check to entrypoint.sh with helpful instructions
- Model persists in /app/data/models via Docker volume mount
Backend:
- Add /tools router with background removal, smart select, color select
- Add rembg dependency for AI background removal
- Add layer management API (list, flatten)
- Fix transparency preservation in blend_patch (veil collapse fix)
- Preserve alpha channel when reverting/resetting images
Frontend:
- Add AdvancedTools panel with background removal, smart select, color select
- Add Layers panel with drag-to-reorder, visibility toggle, flatten
- Add toolsApi for new backend endpoints
- Make right panel scrollable for additional controls
This adds "Photoshop light" capabilities:
- Remove background and create layer
- Smart object selection (click to select)
- Color selection with tolerance
- Layer system with compositing
This commit implements a full-stack AI photo editing application that
allows users to regenerate only selected areas of images using AI.
Features implemented:
- Frontend (React + Fabric.js):
* Interactive canvas with selection tools (rectangle, ellipse, lasso)
* Real-time selection preview and editing
* Mode toggle (A: patch only, B: patch + context)
* Feather slider for edge blending (0-50px)
* Prompt input for AI instructions
* Edit history viewer with revert capability
* Responsive UI with dark theme
- Backend (FastAPI):
* RESTful API for projects and edits
* SQLite database for metadata storage
* Image processing pipeline with PIL/OpenCV
* AI provider interface (pluggable)
* Support for OpenAI, Stability AI, and mock providers
* Feathered alpha blending for smooth compositing
* Complete edit history tracking
* File-based storage for images and edits
- Image Processing:
* Patch extraction from bounding boxes
* Mask generation for all selection types
* Feathered edge blending
* Patch compositing back to full image
* No pixels modified outside selection
* All edits reversible
- Infrastructure:
* Docker Compose orchestration
* Production and development configurations
* Nginx reverse proxy for frontend
* Hot-reload support for development
* Volume persistence for data
Architecture follows specification exactly:
- Only selected regions are regenerated
- Full image pixels preserved outside mask
- Two-mode operation (cost vs quality)
- Complete edit history and reversibility
- Self-hosted with external AI API calls
All components are fully functional and ready for deployment.
Claude