253 lines
6.8 KiB
Markdown
253 lines
6.8 KiB
Markdown
# Image Studio Editing - Completion Summary
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**Date**: Current Session
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**Status**: ✅ **Backend Complete** - Ready for Frontend Integration
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**Progress**: 5 Models Integrated, APIs Ready, Auto-Detection Implemented
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---
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## ✅ Completed Backend Implementation
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### **1. Model Integration** ✅ (5/14 Models)
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**Integrated Models**:
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1. ✅ **Qwen Image Edit** ($0.02) - Basic, single-image
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2. ✅ **Qwen Image Edit Plus** ($0.02) - Multi-image, ControlNet
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3. ✅ **Google Nano Banana Pro Edit Ultra** ($0.15-0.18) - 4K/8K, premium
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4. ✅ **Bytedance Seedream V4.5 Edit** ($0.04) - Reference-faithful, 4K
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5. ✅ **FLUX Kontext Pro** ($0.04) - Typography, guidance scale
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**Remaining**: 9 models (waiting for documentation)
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---
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### **2. Backend APIs** ✅ **COMPLETE**
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#### **2.1 Get Available Models** ✅
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**Endpoint**: `GET /api/image-studio/edit/models`
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**Query Parameters**:
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- `operation` (optional): Filter by operation type
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- `tier` (optional): Filter by tier (budget, mid, premium)
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**Response**:
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```json
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{
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"models": [
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{
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"id": "qwen-edit-plus",
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"name": "Qwen Image Edit Plus",
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"description": "...",
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"cost": 0.02,
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"tier": "budget",
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"max_resolution": [1536, 1536],
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"capabilities": ["general_edit", "multi_image"],
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"use_cases": ["Quick edits", "Batch editing"],
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"features": ["ControlNet support", "Bilingual (CN/EN)"],
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"supports_multi_image": true,
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"supports_controlnet": true,
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"languages": ["en", "zh"]
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}
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],
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"total": 5
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}
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```
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#### **2.2 Get Model Recommendations** ✅
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**Endpoint**: `POST /api/image-studio/edit/recommend`
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**Request Body**:
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```json
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{
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"operation": "general_edit",
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"image_resolution": { "width": 1024, "height": 1024 },
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"user_tier": "free",
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"preferences": {
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"prioritize_cost": true,
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"prioritize_quality": false
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}
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}
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```
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**Response**:
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```json
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{
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"recommended_model": "qwen-edit",
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"reason": "Lowest cost option, Supports 1024×1024 resolution, Budget-friendly for free tier",
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"alternatives": [
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{
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"model_id": "qwen-edit-plus",
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"name": "Qwen Image Edit Plus",
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"cost": 0.02,
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"reason": "Alternative: Budget tier, higher quality"
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}
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]
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}
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```
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---
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### **3. Auto-Detection & Routing** ✅ **COMPLETE**
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**Implementation**: `EditStudioService._handle_general_edit()`
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**Logic**:
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1. **If model specified**: Use that model (WaveSpeed or HuggingFace)
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2. **If no model specified** (general_edit operation):
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- Auto-detect image resolution
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- Call recommendation logic
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- Auto-select recommended WaveSpeed model
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- Fall back to HuggingFace if no WaveSpeed model matches
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**Features**:
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- ✅ Automatic model selection based on image resolution
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- ✅ Cost-optimized by default (prioritize_cost: true)
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- ✅ Logs auto-selection reason for transparency
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- ✅ Graceful fallback to HuggingFace if needed
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---
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### **4. Recommendation Algorithm** ✅ **COMPLETE**
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**Scoring Factors**:
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1. **Cost** (weighted by `prioritize_cost` preference)
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2. **Quality** (max resolution, weighted by `prioritize_quality`)
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3. **User Tier** (free users → budget models, pro → premium)
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4. **Image Resolution** (filters models that don't support input size)
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**Scoring Formula**:
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```python
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score = (
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(1.0 / cost) * cost_weight + # Lower cost = higher score
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max_resolution / resolution_weight + # Higher res = higher score
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tier_bonus # Based on user tier
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)
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```
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**Result**: Returns best matching model with explanation and alternatives
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---
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### **5. Service Layer Methods** ✅ **COMPLETE**
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**Added to `EditStudioService`**:
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- ✅ `get_available_models()` - List models with metadata
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- ✅ `recommend_model()` - Smart recommendation algorithm
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- ✅ `_get_use_cases_for_model()` - Generate use cases from capabilities
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- ✅ `_get_features_for_model()` - Generate feature list
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**Added to `ImageStudioManager`**:
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- ✅ `get_edit_models()` - Expose model listing
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- ✅ `recommend_edit_model()` - Expose recommendations
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---
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## 📋 Frontend Integration (Pending)
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### **Required Components**
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1. **ModelSelector Component**
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- Dropdown/select with search
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- Group by tier
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- Show cost and features
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- Display recommendations
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2. **ModelInfoCard Component**
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- Model details
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- Use cases
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- Features
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- Cost information
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3. **ModelComparisonDialog Component**
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- Side-by-side comparison
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- Filterable table
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- Quick select
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4. **ModelRecommendationBadge Component**
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- Show recommendation reason
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- Dismissible
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### **Integration Points**
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1. **EditStudio.tsx**
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- Add model selector to UI
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- Call `/api/image-studio/edit/models` on load
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- Call `/api/image-studio/edit/recommend` for auto-selection
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- Display model info and cost
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- Pass selected model to request
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2. **useImageStudio Hook**
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- Add `loadEditModels()` function
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- Add `getModelRecommendation()` function
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- Add model selection state
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---
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## 🎯 Current Status
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| Component | Status | Notes |
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|-----------|--------|-------|
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| **Backend Models** | ✅ 5/14 | Qwen Edit, Qwen Edit Plus, Nano Banana, Seedream, FLUX Kontext Pro |
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| **Backend APIs** | ✅ Complete | `/edit/models`, `/edit/recommend` |
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| **Auto-Detection** | ✅ Complete | Smart routing when model not specified |
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| **Recommendation** | ✅ Complete | Algorithm with scoring |
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| **Service Layer** | ✅ Complete | All methods implemented |
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| **Frontend UI** | ⏸️ Pending | Components need to be built |
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---
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## 📝 Next Steps
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### **Immediate (Frontend)**
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1. Create `ModelSelector` component
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2. Create `ModelInfoCard` component
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3. Create `ModelComparisonDialog` component
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4. Integrate into `EditStudio.tsx`
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5. Add API calls to `useImageStudio` hook
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### **Future (More Models)**
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1. Add remaining 9 editing models (once docs provided)
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2. Enhance recommendation algorithm with usage history
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3. Add model performance metrics
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4. Add user feedback/rating system
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---
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## 🔧 API Usage Examples
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### **Get Available Models**
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```bash
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curl -X GET "http://localhost:8000/api/image-studio/edit/models?operation=general_edit&tier=budget" \
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-H "Authorization: Bearer ${TOKEN}"
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```
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### **Get Recommendation**
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```bash
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curl -X POST "http://localhost:8000/api/image-studio/edit/recommend" \
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-H "Authorization: Bearer ${TOKEN}" \
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-H "Content-Type: application/json" \
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-d '{
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"operation": "general_edit",
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"image_resolution": { "width": 1024, "height": 1024 },
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"user_tier": "free",
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"preferences": { "prioritize_cost": true }
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}'
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```
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### **Process Edit (with auto-detection)**
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```bash
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curl -X POST "http://localhost:8000/api/image-studio/edit/process" \
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-H "Authorization: Bearer ${TOKEN}" \
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-H "Content-Type: application/json" \
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-d '{
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"image_base64": "...",
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"operation": "general_edit",
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"prompt": "Change background to beach"
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// model not specified - will auto-detect
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}'
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```
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---
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*Backend complete - Ready for frontend integration*
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