| 4 | |
| 5 | |
| 6 | class GenerateImageRequest(BaseModel): |
| 7 | prompt: str = "" |
| 8 | negative_prompt: str = "" |
| 9 | |
| 10 | seed: int = 42 |
| 11 | width: int = 512 |
| 12 | height: int = 512 |
| 13 | |
| 14 | num_outputs: int = 1 |
| 15 | num_inference_steps: int = 50 |
| 16 | guidance_scale: float = 7.5 |
| 17 | distilled_guidance_scale: float = 3.5 |
| 18 | |
| 19 | init_image: Any = None |
| 20 | init_image_mask: Any = None |
| 21 | ref_images: Any = None # list of base64-encoded reference images for vision-based models |
| 22 | control_image: Any = None |
| 23 | control_alpha: Union[float, List[float]] = None |
| 24 | controlnet_filter: str = None |
| 25 | prompt_strength: float = 0.8 |
| 26 | preserve_init_image_color_profile: bool = False |
| 27 | strict_mask_border: bool = False |
| 28 | |
| 29 | sampler_name: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms" |
| 30 | scheduler_name: str = None |
| 31 | hypernetwork_strength: float = 0 |
| 32 | lora_alpha: Union[float, List[float]] = 0 |
| 33 | tiling: str = None # None, "x", "y", "xy" |
| 34 | |
| 35 | |
| 36 | class FilterImageRequest(BaseModel): |