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