Each operation (inpaint, txt2img, img2img, outpaint) can now use a different
provider. Resolution order: per-op override → global AI_PROVIDER default.
Example: txt2img→openai, inpaint→invokeai, everything else→invokeai default.
Backend:
- config.py: add AI_PROVIDER_INPAINT / TXT2IMG / IMG2IMG / OUTPAINT settings
- remote_provider.py: get_remote_provider(operation) resolves override then default;
_build_provider() extracted as shared factory; _OP_FIELD maps op→setting name
- ai_tools.py: each endpoint passes its operation to _require_remote();
GET /api/config runs per-op health checks concurrently, returns operations map
and overrides; POST /api/config accepts and applies per-op override fields
Frontend:
- ai_provider_settings.js: four new selects (inpaint/txt2img/img2img/outpaint);
persists to localStorage and sends per-op fields to POST /api/config
- provider-badge.js: shows override summary (e.g. "invokeai · txt2img→openai")
and per-op health in tooltip
- .env.example: document per-op override env vars with examples
https://claude.ai/code/session_01B58MaJCU1R6KwBDJCp8AfN
- Fix database path mismatch: download_sample_eyes.py now uses
ai_photo_edit.db instead of photoedit.db
- Add init_database.py script to initialize DB before eye import
- Add AUTO_DOWNLOAD_SAM=true environment variable (default: enabled)
- Update entrypoint.sh to:
1. Initialize database first
2. Auto-download SAM model (~375MB) on first startup
3. Then import eyes (now works since DB exists)
- Update path detection to work in both Docker and local environments
- Create entrypoint.sh that auto-downloads sample eyes on first run
- Update Dockerfile to use entrypoint script
- Rewrite .env.example with step-by-step setup instructions
- Add detailed troubleshooting section
- Clarify which models work for inpainting vs text-to-image
Major additions:
1. Replicate AI Provider
- Support for multiple models (SDXL, LaMa, Realistic Vision)
- Auto-model selection based on prompt keywords
- Best for human features: realistic-vision (~$0.020/image)
- Best for removal: lama (~$0.002/image)
- Best general purpose: sdxl-inpaint (~$0.025/image)
- Smart keyword detection for automatic model selection
2. Enhanced Stability AI Provider
- Optimized parameters for better quality
- Support for multiple engines (SDXL, SD 1.5, SD 2.1)
- Increased steps and CFG scale for improved results
3. Model Selection System
- Per-edit model override capability
- Global default model configuration
- Provider-specific model options
- Auto-selection based on prompt analysis
4. Patch Library Feature
- Save AI-generated patches for reuse
- Save manually selected regions
- Import external images as patches
- Organize with categories and tags
- Browse and filter patch library
- Apply saved patches to new images
- Thumbnail generation for quick preview
- Cost savings by reusing good results
5. Comprehensive Documentation
- MODEL_SELECTION_GUIDE.md: Detailed guide for choosing models
* Best models for hands, faces, bodies
* Quality comparison table
* Cost optimization strategies
* Troubleshooting common issues
- QUICK_START.md: How-to guide for new features
* Model selection examples
* Patch library workflow
* API reference
* Pro tips and cost comparisons
6. Configuration Updates
- Added Replicate API key support
- Model selection settings
- Per-edit override toggle
- Updated .env.example with all options
Benefits:
- Better quality for human features (hands, faces)
- 90% cost reduction using lama for removals
- Reusable patch library saves money and ensures consistency
- Auto-model selection optimizes quality and cost
- Flexibility to choose provider and model per edit
All backend changes are fully functional and ready for use.
Frontend UI for patch library pending.
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