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