92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
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import PIL.Image
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import cv2
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import numpy as np
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import torch
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from lama_cleaner.model.base import DiffusionInpaintModel
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from lama_cleaner.model.utils import get_scheduler
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from lama_cleaner.schema import Config
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class Kandinsky(DiffusionInpaintModel):
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pad_mod = 64
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from diffusers import AutoPipelineForInpainting
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fp16 = not kwargs.get("no_half", False)
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use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
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torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
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model_kwargs = {
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"local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"]),
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"torch_dtype": torch_dtype,
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}
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# self.pipe_prior = KandinskyPriorPipeline.from_pretrained(
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# self.prior_name, **model_kwargs
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# ).to("cpu")
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#
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# self.model = KandinskyInpaintPipeline.from_pretrained(
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# self.model_name, **model_kwargs
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# ).to(device)
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self.model = AutoPipelineForInpainting.from_pretrained(
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self.model_name, **model_kwargs
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).to(device)
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self.callback = kwargs.pop("callback", None)
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def forward(self, image, mask, config: Config):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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scheduler_config = self.model.scheduler.config
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scheduler = get_scheduler(config.sd_sampler, scheduler_config)
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self.model.scheduler = scheduler
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generator = torch.manual_seed(config.sd_seed)
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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mask = mask.astype(np.float32) / 255
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img_h, img_w = image.shape[:2]
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output = self.model(
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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image=PIL.Image.fromarray(image),
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mask_image=mask[:, :, 0],
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height=img_h,
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width=img_w,
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num_inference_steps=config.sd_steps,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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callback=self.callback,
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).images[0]
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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def forward_post_process(self, result, image, mask, config):
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if config.sd_match_histograms:
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result = self._match_histograms(result, image[:, :, ::-1], mask)
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)
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return result, image, mask
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@staticmethod
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def is_downloaded() -> bool:
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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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class Kandinsky22(Kandinsky):
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name = "kandinsky2.2"
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model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
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