Claude
4b936b7a10
Implemented complete text-to-image functionality across all AI providers: Backend additions: - Added text_to_image() method to AIProvider abstract class - Implemented for all providers: * OpenAI: DALL-E generations API * Stability AI: SDXL text-to-image with negative prompts * Replicate: SDXL with full parameter control * Mock: Placeholder image generation for testing New API endpoints (/generate): - POST /generate/text-to-image * Generate image from prompt * Optional: create new project automatically * Configurable width/height (256-2048px) * Negative prompt support * Provider and model selection - POST /generate/layer/text-to-image * Generate image as layer in existing project * Smaller dimensions for layer composition * Position control (x, y coordinates) * Saves to project layers directory Features: - Full provider support (OpenAI, Stability, Replicate, Mock) - Negative prompts for better control - Auto-project creation option - Layer-based generation for compositing - Dimension validation (256-2048px range) - Model selection per request Use cases: - Create new images from scratch - Generate elements to add as layers - Quick ideation and iteration - Base image creation for further editing Next: Advanced canvas UI with layers and real-time preview
139 lines
2.9 KiB
Python
139 lines
2.9 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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# Text-to-Image schemas
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class TextToImageRequest(BaseModel):
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prompt: str
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width: int = 1024
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height: int = 1024
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negative_prompt: Optional[str] = None
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ai_provider: Optional[str] = None
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ai_model: Optional[str] = None
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create_project: bool = True
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project_name: Optional[str] = None
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class TextToImageResponse(BaseModel):
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status: str
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prompt: str
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width: int
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height: int
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project_id: Optional[int] = None
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image_url: Optional[str] = None
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layer_position: Optional[Dict[str, int]] = None
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ai_provider: str
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ai_model: Optional[str] = None
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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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