Add Replicate provider, model selection, and Patch Library features

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 is contained in:
Claude
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# Quick Start Guide
## How to Choose the Right AI Model
### For Body Parts (Hands, Faces, Bodies)
Use **Replicate with `realistic-vision`** model:
```env
AI_PROVIDER=replicate
REPLICATE_API_KEY=your-key-here
REPLICATE_MODEL=realistic-vision
```
**Why:** This model is specifically trained on human anatomy and handles difficult features like:
- ✅ Hands (even complex finger positions)
- ✅ Faces and expressions
- ✅ Skin textures
- ✅ Body proportions
**Cost:** ~$0.020/image
### For Removing Objects
Use **Replicate with `lama`** model:
```env
AI_PROVIDER=replicate
REPLICATE_MODEL=lama
```
**Why:** Designed specifically for inpainting and removal
**Cost:** ~$0.002/image (cheapest!)
### For General Edits (Landscapes, Objects, Creative)
Use **Replicate with `sdxl-inpaint`** model (default):
```env
AI_PROVIDER=replicate
REPLICATE_MODEL=sdxl-inpaint
```
**Cost:** ~$0.025/image
---
## Auto-Model Selection
The system automatically picks the best model based on your prompt:
| Your Prompt | Auto-Selected Model | Why |
|-------------|-------------------|-----|
| "Fix the hand" | realistic-vision | Detects "hand" keyword |
| "Remove person" | lama | Detects "remove" keyword |
| "Change sky to sunset" | sdxl-inpaint | General purpose default |
**You don't need to manually specify models** - the auto-selection is optimized for quality and cost!
---
## Patch Library: Save and Reuse Parts
### What is the Patch Library?
A library where you can save image patches (regions) and reuse them across different images.
**Use Cases:**
- Save a well-generated hand to reuse later
- Save a perfect face for multiple photos
- Build a collection of good body parts
- Save textures, objects, or backgrounds
- Reuse AI-generated elements that came out great
### How to Save a Patch
#### Option 1: Save AI-Generated Result
After an AI edit completes:
```bash
POST /patches/
{
"name": "Perfect Hand",
"description": "Well-formed left hand, palm up",
"source_type": "ai_generated",
"source_edit_id": 123,
"category": "hand",
"tags": "left, palm, realistic"
}
```
This saves the AI-generated output (`patch_out.png`) to your library.
#### Option 2: Save Manual Selection
Select any region from your current image:
```bash
POST /patches/
{
"name": "Good Face",
"description": "Frontal face with good lighting",
"source_type": "manual_selection",
"source_project_id": 456,
"bbox": {"x": 100, "y": 100, "width": 200, "height": 200},
"category": "face",
"tags": "front, smile, female"
}
```
This saves whatever is currently in that region of your image.
#### Option 3: Import from File
Upload an external image:
```bash
POST /patches/
FormData:
name: "Downloaded Hand"
source_type: "imported"
file: [uploaded PNG file]
category: "hand"
```
### How to Apply a Saved Patch
```bash
POST /patches/apply
{
"project_id": 789,
"patch_id": 123,
"bbox": {"x": 300, "y": 400, "width": 200, "height": 200},
"feather_px": 10
}
```
This places the saved patch at the specified location in your image.
### Browse Your Patch Library
```bash
# List all patches
GET /patches/
# Filter by category
GET /patches/?category=hand
# Filter by tags
GET /patches/?tags=realistic
# Get specific patch
GET /patches/123
# Get patch image
GET /patches/123/image
# Get patch thumbnail
GET /patches/123/image?thumbnail=true
```
### Organize Your Patches
**Categories:**
- `hand` - Hand images
- `face` - Facial features
- `body` - Body parts
- `object` - Objects and items
- `texture` - Textures and patterns
- `background` - Backgrounds and scenery
**Tags:** Comma-separated keywords for searching
- "left, palm, realistic"
- "front, smile, female"
- "five fingers, open hand"
---
## Complete Workflow Example
### Scenario: Fix hands in a portrait photo
**Step 1: Create project and upload image**
```bash
POST /projects/ {"name": "Portrait Edit"}
POST /projects/1/upload [upload photo]
```
**Step 2: Try to fix the hand with AI**
```bash
POST /edits/projects/1/fix
{
"prompt": "realistic human hand with five fingers, natural pose",
"mode": "B", # Use full image for context
"selection_type": "rectangle",
"bbox": {"x": 200, "y": 300, "width": 150, "height": 200},
"feather_px": 10
}
```
The system auto-selects `realistic-vision` model because prompt mentions "hand".
**Step 3: If result is good, save it for later**
```bash
POST /patches/
{
"name": "Good Left Hand",
"source_type": "ai_generated",
"source_edit_id": 1,
"category": "hand",
"tags": "left, natural, realistic, five fingers"
}
```
**Step 4: Use saved hand on another photo**
```bash
# On a different project
POST /patches/apply
{
"project_id": 2,
"patch_id": 1,
"bbox": {"x": 150, "y": 250, "width": 150, "height": 200},
"feather_px": 15
}
```
---
## Cost Comparison
### Example: Fixing 10 hands in different photos
**Option A: Generate each hand with AI**
- 10 edits × $0.020 = **$0.20**
**Option B: Generate one good hand, save it, reuse it**
- 1 AI generation: $0.020
- 9 patch applications: $0.00 (no AI cost)
- **Total: $0.020** (90% savings!)
### When to Use Saved Patches vs AI
**Use Saved Patches When:**
- You have a perfect result you want to reuse
- Same angle/lighting/style needed
- Want to maintain consistency across images
- Want to avoid AI generation costs
**Use AI Generation When:**
- Need unique/different result each time
- Different angle or perspective needed
- Want variation and creativity
- Patch doesn't fit the context
---
## Pro Tips
### Building a Good Patch Library
1. **Save your best AI results** - When AI generates something great, save it immediately
2. **Organize with categories** - Use consistent categories for easy finding
3. **Tag descriptively** - Include orientation (left/right), pose, lighting, etc.
4. **Create variations** - Save multiple versions of common needs (left hand, right hand, etc.)
5. **Build gradually** - Your library becomes more valuable over time
### Maximizing Quality
1. **For hands:** Always use `realistic-vision` model or save good results
2. **For faces:** Use Mode B (full image context) for better matching
3. **Use high feather values** (15-20px) when applying saved patches
4. **Test positioning** before finalizing - patches work best when lighting/angle matches
### Saving Money
1. **Build a patch library** of common needs
2. **Use `lama` for removals** instead of expensive models
3. **Let auto-selection work** - it picks the cheapest appropriate model
4. **Reuse successful patches** instead of regenerating
---
## API Quick Reference
```bash
# List available patches
GET /patches/
# Get patch details
GET /patches/{id}
# Get patch image
GET /patches/{id}/image
GET /patches/{id}/image?thumbnail=true
# Create patch from AI edit
POST /patches/
{
"name": "My Patch",
"source_type": "ai_generated",
"source_edit_id": 123,
"category": "hand"
}
# Create patch from manual selection
POST /patches/
{
"name": "My Patch",
"source_type": "manual_selection",
"source_project_id": 456,
"bbox": {"x": 100, "y": 100, "width": 200, "height": 200}
}
# Apply saved patch
POST /patches/apply
{
"project_id": 789,
"patch_id": 123,
"bbox": {"x": 300, "y": 400, "width": 200, "height": 200},
"feather_px": 10
}
# Delete patch
DELETE /patches/{id}
# Update patch metadata
PUT /patches/{id}
{
"name": "Updated Name",
"tags": "new, tags",
"category": "hand"
}
```
---
## Summary
**For hands/faces/bodies:** Use `realistic-vision` model
**For removal:** Use `lama` model
**For general edits:** Use `sdxl-inpaint` (default)
**Auto-selection works great** - just write natural prompts
**Save good AI results** to patch library for reuse
**Save manual selections** from any image
**Reuse patches across images** to save money and maintain consistency
**You now have the best of both worlds:**
- AI generation when you need something new
- Saved patches when you need consistency or want to save money