96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
from __future__ import annotations
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from typing import Optional
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from pydantic import BaseModel, Field
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class ImageInput(BaseModel):
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image_b64: str = Field(..., description="PNG/JPG 编码后的 base64（不含 data: 前缀）")
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model_name: Optional[str] = Field(None, description="模型 key（来自 /models）")
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class DepthRequest(ImageInput):
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pass
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class SegmentRequest(ImageInput):
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pass
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class SamPromptSegmentRequest(BaseModel):
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image_b64: str = Field(..., description="裁剪后的 RGB 图 base64（PNG/JPG）")
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overlay_b64: Optional[str] = Field(
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None,
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description="与裁剪同尺寸的标记叠加 PNG base64（可选；当前用于校验尺寸一致）",
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)
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point_coords: list[list[float]] = Field(
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...,
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description="裁剪坐标系下的提示点 [[x,y], ...]",
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)
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point_labels: list[int] = Field(
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...,
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description="与 point_coords 等长：1=前景，0=背景",
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)
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box_xyxy: list[float] = Field(
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...,
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description="裁剪内笔画紧包围盒 [x1,y1,x2,y2]（像素）",
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min_length=4,
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max_length=4,
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)
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expand_px: int = Field(
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2,
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ge=0,
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le=20,
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description="对输出 mask 做轻微膨胀的像素半径（0 表示不扩大；建议 1~3）",
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)
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class InpaintRequest(ImageInput):
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prompt: Optional[str] = Field("", description="补全 prompt")
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strength: float = Field(0.8, ge=0.0, le=1.0)
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negative_prompt: Optional[str] = Field("", description="负向 prompt")
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mask_b64: Optional[str] = Field(None, description="mask PNG base64（可选）")
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max_side: int = Field(1024, ge=128, le=8192, description="推理时长边上限；不超过则按输入全分辨率推理后再还原")
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guidance_scale: Optional[float] = Field(None, ge=0.0, le=200.0, description="扩散 CFG；不传则各后端默认")
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num_inference_steps: Optional[int] = Field(None, ge=1, le=150, description="采样步数；不传则各后端默认")
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class AnimateRequest(BaseModel):
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model_name: Optional[str] = Field(None, description="模型 key（来自 /models）")
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prompt: str = Field(..., description="文本提示词")
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negative_prompt: Optional[str] = Field("", description="负向提示词")
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num_inference_steps: int = Field(25, ge=1, le=200)
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guidance_scale: float = Field(8.0, ge=0.0, le=30.0)
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width: int = Field(512, ge=128, le=2048)
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height: int = Field(512, ge=128, le=2048)
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video_length: int = Field(16, ge=1, le=128)
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seed: int = Field(-1, description="-1 表示随机种子")
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class CharacterAnimateRequest(BaseModel):
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image_b64: str = Field(..., description="透明背景角色 PNG 的 base64（不含 data: 前缀）")
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model_name: Optional[str] = Field(None, description="模型 key（来自 /models）")
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prompt: str = Field(..., description="角色动画提示词")
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negative_prompt: Optional[str] = Field("", description="负向提示词")
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background_color: str = Field(
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"#00FF00",
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description="生成时使用的纯色背景，支持 #RRGGBB 或 R,G,B",
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)
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background_tolerance: int = Field(
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40,
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ge=0,
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le=255,
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description="自动剔除背景的 RGB 色差阈值，越大剔除越激进",
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)
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num_inference_steps: int = Field(25, ge=1, le=200)
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guidance_scale: float = Field(8.0, ge=0.0, le=30.0)
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video_length: int = Field(16, ge=1, le=128, description="前端请求生成的帧数")
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seed: int = Field(-1, description="-1 表示随机种子")
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max_side: int = Field(
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768,
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ge=128,
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le=2048,
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description="推理分辨率长边上限；会保持输入比例并对齐到 8 的倍数",
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)
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