19a5af9682
- Created routers/image_studio/models.py with all 40 Pydantic models - Created routers/image_studio/deps.py with get_studio_manager() and _require_user_id() - Renamed old monolithic image_studio.py -> image_studio_router.py - Updated __init__.py to re-export the router for backward compatibility - Old file now imports models and deps from new modules (no inline definitions) Backward compatibility: from routers.image_studio import router still works. Route count unchanged: 33 routes, prefix /api/image-studio.
373 lines
15 KiB
Python
373 lines
15 KiB
Python
"""Pydantic request/response models for Image Studio API."""
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from typing import Optional, List, Dict, Any, Literal
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from pydantic import BaseModel, Field
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# ==================== Create Studio ====================
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class CreateImageRequest(BaseModel):
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prompt: str = Field(..., description="Image generation prompt")
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template_id: Optional[str] = Field(None, description="Template ID to use")
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provider: Optional[str] = Field("auto", description="Provider: auto, stability, wavespeed, huggingface, gemini")
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model: Optional[str] = Field(None, description="Specific model to use")
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width: Optional[int] = Field(None, description="Image width in pixels")
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height: Optional[int] = Field(None, description="Image height in pixels")
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aspect_ratio: Optional[str] = Field(None, description="Aspect ratio (e.g., '1:1', '16:9')")
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style_preset: Optional[str] = Field(None, description="Style preset")
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quality: str = Field("standard", description="Quality: draft, standard, premium")
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negative_prompt: Optional[str] = Field(None, description="Negative prompt")
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guidance_scale: Optional[float] = Field(None, description="Guidance scale")
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steps: Optional[int] = Field(None, description="Number of inference steps")
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seed: Optional[int] = Field(None, description="Random seed")
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num_variations: int = Field(1, ge=1, le=10, description="Number of variations (1-10)")
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enhance_prompt: bool = Field(True, description="Enhance prompt with AI")
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use_persona: bool = Field(False, description="Use persona for brand consistency")
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persona_id: Optional[str] = Field(None, description="Persona ID")
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class CostEstimationRequest(BaseModel):
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provider: str = Field(..., description="Provider name")
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model: Optional[str] = Field(None, description="Model name")
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operation: str = Field("generate", description="Operation type")
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num_images: int = Field(1, ge=1, description="Number of images")
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width: Optional[int] = Field(None, description="Image width")
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height: Optional[int] = Field(None, description="Image height")
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# ==================== Edit Studio ====================
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class EditImageRequest(BaseModel):
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image_base64: str = Field(..., description="Primary image payload (base64 or data URL)")
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operation: Literal[
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"remove_background",
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"inpaint",
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"outpaint",
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"search_replace",
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"search_recolor",
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"general_edit",
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] = Field(..., description="Edit operation to perform")
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prompt: Optional[str] = Field(None, description="Primary prompt/instruction")
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negative_prompt: Optional[str] = Field(None, description="Negative prompt for providers that support it")
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mask_base64: Optional[str] = Field(None, description="Optional mask image in base64")
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search_prompt: Optional[str] = Field(None, description="Search prompt for replace operations")
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select_prompt: Optional[str] = Field(None, description="Select prompt for recolor operations")
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background_image_base64: Optional[str] = Field(None, description="Reference background image")
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lighting_image_base64: Optional[str] = Field(None, description="Reference lighting image")
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expand_left: Optional[int] = Field(0, description="Outpaint expansion in pixels (left)")
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expand_right: Optional[int] = Field(0, description="Outpaint expansion in pixels (right)")
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expand_up: Optional[int] = Field(0, description="Outpaint expansion in pixels (up)")
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expand_down: Optional[int] = Field(0, description="Outpaint expansion in pixels (down)")
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provider: Optional[str] = Field(None, description="Explicit provider override")
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model: Optional[str] = Field(None, description="Explicit model override")
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style_preset: Optional[str] = Field(None, description="Style preset for Stability helpers")
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guidance_scale: Optional[float] = Field(None, description="Guidance scale for general edits")
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steps: Optional[int] = Field(None, description="Inference steps")
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seed: Optional[int] = Field(None, description="Random seed for reproducibility")
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output_format: str = Field("png", description="Output format for edited image")
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options: Optional[Dict[str, Any]] = Field(None, description="Advanced provider-specific options (e.g., grow_mask)")
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class EditImageResponse(BaseModel):
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success: bool
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operation: str
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provider: str
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image_base64: str
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width: int
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height: int
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metadata: Dict[str, Any]
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class EditOperationsResponse(BaseModel):
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operations: Dict[str, Dict[str, Any]]
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class EditModelsResponse(BaseModel):
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models: List[Dict[str, Any]]
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total: int
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class EditModelRecommendationRequest(BaseModel):
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operation: str
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image_resolution: Optional[Dict[str, int]] = None
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user_tier: Optional[str] = None
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preferences: Optional[Dict[str, Any]] = None
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class EditModelRecommendationResponse(BaseModel):
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recommended_model: str
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reason: str
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alternatives: List[Dict[str, Any]]
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# ==================== Face Swap Studio ====================
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class FaceSwapRequest(BaseModel):
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base_image_base64: str
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face_image_base64: str
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model: Optional[str] = None
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target_face_index: Optional[int] = None
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target_gender: Optional[str] = None
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options: Optional[Dict[str, Any]] = None
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class FaceSwapResponse(BaseModel):
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success: bool
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image_base64: str
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width: int
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height: int
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provider: str
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model: str
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metadata: Dict[str, Any]
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class FaceSwapModelsResponse(BaseModel):
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models: List[Dict[str, Any]]
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total: int
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class FaceSwapModelRecommendationRequest(BaseModel):
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base_image_resolution: Optional[Dict[str, int]] = None
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face_image_resolution: Optional[Dict[str, int]] = None
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user_tier: Optional[str] = None
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preferences: Optional[Dict[str, Any]] = None
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class FaceSwapModelRecommendationResponse(BaseModel):
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recommended_model: str
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reason: str
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alternatives: List[Dict[str, Any]]
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# ==================== Upscale Studio ====================
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class UpscaleImageRequest(BaseModel):
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image_base64: str
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mode: Literal["fast", "conservative", "creative", "auto"] = "auto"
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target_width: Optional[int] = Field(None, description="Target width in pixels")
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target_height: Optional[int] = Field(None, description="Target height in pixels")
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preset: Optional[str] = Field(None, description="Named preset (web, print, social)")
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prompt: Optional[str] = Field(None, description="Prompt for conservative/creative modes")
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class UpscaleImageResponse(BaseModel):
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success: bool
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mode: str
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image_base64: str
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width: int
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height: int
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metadata: Dict[str, Any]
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# ==================== Control Studio ====================
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class ControlImageRequest(BaseModel):
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control_image_base64: str = Field(..., description="Control image (sketch/structure/style) in base64")
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operation: Literal["sketch", "structure", "style", "style_transfer"] = Field(..., description="Control operation")
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prompt: str = Field(..., description="Text prompt for generation")
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style_image_base64: Optional[str] = Field(None, description="Style reference image (for style_transfer only)")
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negative_prompt: Optional[str] = Field(None, description="Negative prompt")
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control_strength: Optional[float] = Field(None, ge=0.0, le=1.0, description="Control strength (sketch/structure)")
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fidelity: Optional[float] = Field(None, ge=0.0, le=1.0, description="Style fidelity (style operation)")
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style_strength: Optional[float] = Field(None, ge=0.0, le=1.0, description="Style strength (style_transfer)")
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composition_fidelity: Optional[float] = Field(None, ge=0.0, le=1.0, description="Composition fidelity (style_transfer)")
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change_strength: Optional[float] = Field(None, ge=0.0, le=1.0, description="Change strength (style_transfer)")
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aspect_ratio: Optional[str] = Field(None, description="Aspect ratio (style operation)")
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style_preset: Optional[str] = Field(None, description="Style preset")
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seed: Optional[int] = Field(None, description="Random seed")
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output_format: str = Field("png", description="Output format")
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class ControlImageResponse(BaseModel):
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success: bool
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operation: str
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provider: str
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image_base64: str
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width: int
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height: int
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metadata: Dict[str, Any]
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class ControlOperationsResponse(BaseModel):
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operations: Dict[str, Dict[str, Any]]
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# ==================== Social Optimizer ====================
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class SocialOptimizeRequest(BaseModel):
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image_base64: str = Field(..., description="Source image in base64 or data URL")
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platforms: List[str] = Field(..., description="List of platforms to optimize for")
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format_names: Optional[Dict[str, str]] = Field(None, description="Specific format per platform")
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show_safe_zones: bool = Field(False, description="Include safe zone overlay in output")
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crop_mode: str = Field("smart", description="Crop mode: smart, center, or fit")
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focal_point: Optional[Dict[str, float]] = Field(None, description="Focal point for smart crop (x, y as 0-1)")
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output_format: str = Field("png", description="Output format (png or jpg)")
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class SocialOptimizeResponse(BaseModel):
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success: bool
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results: List[Dict[str, Any]]
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total_optimized: int
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class PlatformFormatsResponse(BaseModel):
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formats: List[Dict[str, Any]]
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# ==================== Transform Studio ====================
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class TransformImageToVideoRequestModel(BaseModel):
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image_base64: str = Field(..., description="Image in base64 or data URL format")
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prompt: str = Field(..., description="Text prompt describing the video")
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audio_base64: Optional[str] = Field(None, description="Optional audio file (wav/mp3, 3-30s, ≤15MB)")
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resolution: Literal["480p", "720p", "1080p"] = Field("720p", description="Output resolution")
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duration: Literal[5, 10] = Field(5, description="Video duration in seconds")
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negative_prompt: Optional[str] = Field(None, description="Negative prompt")
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seed: Optional[int] = Field(None, description="Random seed for reproducibility")
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enable_prompt_expansion: bool = Field(True, description="Enable prompt optimizer")
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class TalkingAvatarRequestModel(BaseModel):
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image_base64: str = Field(..., description="Person image in base64 or data URL")
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audio_base64: str = Field(..., description="Audio file in base64 or data URL (wav/mp3, max 10 minutes)")
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resolution: Literal["480p", "720p"] = Field("720p", description="Output resolution")
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prompt: Optional[str] = Field(None, description="Optional prompt for expression/style")
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mask_image_base64: Optional[str] = Field(None, description="Optional mask for animatable regions")
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seed: Optional[int] = Field(None, description="Random seed")
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class TransformVideoResponse(BaseModel):
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success: bool
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video_url: Optional[str] = None
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video_base64: Optional[str] = None
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duration: float
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resolution: str
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width: int
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height: int
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file_size: int
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cost: float
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provider: str
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model: str
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metadata: Dict[str, Any]
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class TransformCostEstimateRequest(BaseModel):
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operation: Literal["image-to-video", "talking-avatar"] = Field(..., description="Operation type")
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resolution: str = Field(..., description="Output resolution")
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duration: Optional[int] = Field(None, description="Video duration in seconds (for image-to-video)")
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class TransformCostEstimateResponse(BaseModel):
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estimated_cost: float
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breakdown: Dict[str, Any]
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currency: str
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provider: str
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model: str
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# ==================== Compression ====================
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class CompressImageRequest(BaseModel):
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image_base64: str = Field(..., description="Image in base64 or data URL format")
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quality: int = Field(85, ge=1, le=100, description="Compression quality (1-100)")
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format: str = Field("jpeg", description="Output format: jpeg, png, webp")
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target_size_kb: Optional[int] = Field(None, ge=10, description="Target file size in KB")
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strip_metadata: bool = Field(True, description="Remove EXIF metadata")
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progressive: bool = Field(True, description="Progressive JPEG encoding")
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optimize: bool = Field(True, description="Optimize encoding")
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class CompressImageResponse(BaseModel):
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success: bool
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image_base64: str
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original_size_kb: float
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compressed_size_kb: float
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compression_ratio: float
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format: str
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width: int
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height: int
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quality_used: int
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metadata_stripped: bool
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class CompressBatchRequest(BaseModel):
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images: List[CompressImageRequest] = Field(..., description="List of images to compress")
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class CompressBatchResponse(BaseModel):
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success: bool
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results: List[CompressImageResponse]
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total_images: int
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successful: int
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failed: int
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class CompressionEstimateRequest(BaseModel):
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image_base64: str = Field(..., description="Image in base64 or data URL format")
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format: str = Field("jpeg", description="Output format")
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quality: int = Field(85, ge=1, le=100, description="Quality level")
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class CompressionEstimateResponse(BaseModel):
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original_size_kb: float
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estimated_size_kb: float
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estimated_reduction_percent: float
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width: int
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height: int
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format: str
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class CompressionFormatsResponse(BaseModel):
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formats: List[Dict[str, Any]]
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class CompressionPresetsResponse(BaseModel):
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presets: List[Dict[str, Any]]
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# ==================== Format Converter ====================
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class ConvertFormatRequest(BaseModel):
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image_base64: str = Field(..., description="Image in base64 or data URL format")
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target_format: str = Field(..., description="Target format: png, jpeg, jpg, webp, gif, bmp, tiff")
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preserve_transparency: bool = Field(True, description="Preserve transparency when possible")
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quality: Optional[int] = Field(None, ge=1, le=100, description="Quality for lossy formats (1-100)")
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color_space: Optional[str] = Field(None, description="Color space: sRGB, Adobe RGB")
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strip_metadata: bool = Field(False, description="Remove EXIF metadata")
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optimize: bool = Field(True, description="Optimize encoding")
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progressive: bool = Field(True, description="Progressive JPEG encoding")
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class ConvertFormatResponse(BaseModel):
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success: bool
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image_base64: str
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original_format: str
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target_format: str
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original_size_kb: float
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converted_size_kb: float
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width: int
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height: int
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transparency_preserved: bool
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metadata_preserved: bool
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color_space: Optional[str] = None
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class ConvertFormatBatchRequest(BaseModel):
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images: List[ConvertFormatRequest] = Field(..., description="List of images to convert")
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class ConvertFormatBatchResponse(BaseModel):
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success: bool
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results: List[ConvertFormatResponse]
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total_images: int
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successful: int
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failed: int
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class SupportedFormatsResponse(BaseModel):
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formats: List[Dict[str, Any]]
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class FormatRecommendationsResponse(BaseModel):
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recommendations: List[Dict[str, Any]]
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