Claude
fc394d76cf
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.
115 lines
2.3 KiB
Python
115 lines
2.3 KiB
Python
from pydantic import BaseModel, EmailStr
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from typing import Optional, List, Dict, Any
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from datetime import datetime
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# User schemas
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class UserCreate(BaseModel):
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email: EmailStr
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password: str
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class UserResponse(BaseModel):
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id: int
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email: str
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created_at: datetime
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class Config:
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from_attributes = True
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# Project schemas
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class ProjectCreate(BaseModel):
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name: str
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class ProjectResponse(BaseModel):
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id: int
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user_id: int
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name: str
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created_at: datetime
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updated_at: datetime
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class Config:
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from_attributes = True
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# Edit schemas
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class EditRequest(BaseModel):
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prompt: str
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mode: str = "A" # "A" or "B"
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selection_type: str # "rectangle", "ellipse", "lasso"
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bbox: Dict[str, int] # {x, y, width, height}
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feather_px: int = 0
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selection_data: Optional[Dict[str, Any]] = None
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class EditResponse(BaseModel):
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id: int
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project_id: int
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created_at: datetime
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mode: str
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prompt: str
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selection_type: str
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bbox_json: str
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feather_px: int
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ai_provider: str
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status: str
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error_message: Optional[str] = None
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class Config:
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from_attributes = True
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# Image upload
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class UploadResponse(BaseModel):
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project_id: int
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original_url: str
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current_url: str
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# Patch Library schemas
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class PatchCreate(BaseModel):
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name: str
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description: Optional[str] = None
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source_type: str # "ai_generated", "manual_selection", "imported"
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source_project_id: Optional[int] = None
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source_edit_id: Optional[int] = None
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category: Optional[str] = None
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tags: Optional[str] = None
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bbox: Optional[Dict[str, int]] = None
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class PatchResponse(BaseModel):
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id: int
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name: str
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description: Optional[str]
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created_at: datetime
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source_type: str
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source_project_id: Optional[int]
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source_edit_id: Optional[int]
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width: int
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height: int
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tags: Optional[str]
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category: Optional[str]
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is_public: bool
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file_path: str
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thumbnail_path: Optional[str]
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class Config:
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from_attributes = True
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class PatchApply(BaseModel):
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project_id: int
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patch_id: int
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bbox: Dict[str, int]
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feather_px: int = 5
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# Generic responses
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class StatusResponse(BaseModel):
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status: str
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message: Optional[str] = None
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data: Optional[Any] = None
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