101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
from enum import Enum
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from PIL.Image import Image
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from pydantic import BaseModel
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class HDStrategy(str, Enum):
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# Use original image size
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ORIGINAL = "Original"
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# Resize the longer side of the image to a specific size(hd_strategy_resize_limit),
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# then do inpainting on the resized image. Finally, resize the inpainting result to the original size.
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# The area outside the mask will not lose quality.
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RESIZE = "Resize"
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# Crop masking area(with a margin controlled by hd_strategy_crop_margin) from the original image to do inpainting
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CROP = "Crop"
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class LDMSampler(str, Enum):
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ddim = "ddim"
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plms = "plms"
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class SDSampler(str, Enum):
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ddim = "ddim"
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pndm = "pndm"
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k_lms = "k_lms"
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k_euler = "k_euler"
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k_euler_a = "k_euler_a"
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dpm_plus_plus = "dpm++"
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uni_pc = "uni_pc"
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class Config(BaseModel):
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class Config:
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arbitrary_types_allowed = True
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# Configs for ldm model
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ldm_steps: int
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ldm_sampler: str = LDMSampler.plms
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# Configs for zits model
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zits_wireframe: bool = True
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# Configs for High Resolution Strategy(different way to preprocess image)
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hd_strategy: str # See HDStrategy Enum
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hd_strategy_crop_margin: int
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# If the longer side of the image is larger than this value, use crop strategy
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hd_strategy_crop_trigger_size: int
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hd_strategy_resize_limit: int
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# Configs for Stable Diffusion 1.5
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prompt: str = ""
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negative_prompt: str = ""
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# Crop image to this size before doing sd inpainting
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# The value is always on the original image scale
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use_croper: bool = False
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croper_x: int = None
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croper_y: int = None
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croper_height: int = None
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croper_width: int = None
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# Resize the image before doing sd inpainting, the area outside the mask will not lose quality.
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# Used by sd models and paint_by_example model
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sd_scale: float = 1.0
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# Blur the edge of mask area. The higher the number the smoother blend with the original image
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sd_mask_blur: int = 0
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# Ignore this value, it's useless for inpainting
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sd_strength: float = 0.75
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# The number of denoising steps. More denoising steps usually lead to a
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# higher quality image at the expense of slower inference.
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sd_steps: int = 50
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# Higher guidance scale encourages to generate images that are closely linked
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# to the text prompt, usually at the expense of lower image quality.
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sd_guidance_scale: float = 7.5
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sd_sampler: str = SDSampler.uni_pc
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# -1 mean random seed
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sd_seed: int = 42
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sd_match_histograms: bool = False
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# Configs for opencv inpainting
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# opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07
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cv2_flag: str = "INPAINT_NS"
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cv2_radius: int = 4
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# Paint by Example
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paint_by_example_steps: int = 50
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paint_by_example_guidance_scale: float = 7.5
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paint_by_example_mask_blur: int = 0
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paint_by_example_seed: int = 42
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paint_by_example_match_histograms: bool = False
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paint_by_example_example_image: Image = None
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# InstructPix2Pix
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p2p_steps: int = 50
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p2p_image_guidance_scale: float = 1.5
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p2p_guidance_scale: float = 7.5
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# ControlNet
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controlnet_conditioning_scale: float = 0.4
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controlnet_method: str = "control_v11p_sd15_canny"
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