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| import PIL.Image | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from iopaint.const import KANDINSKY22_NAME | |
| from .base import DiffusionInpaintModel | |
| from iopaint.schema import InpaintRequest | |
| from .utils import get_torch_dtype, enable_low_mem, is_local_files_only | |
| class Kandinsky(DiffusionInpaintModel): | |
| pad_mod = 64 | |
| min_size = 512 | |
| def init_model(self, device: torch.device, **kwargs): | |
| from diffusers import AutoPipelineForInpainting | |
| use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) | |
| model_kwargs = { | |
| "torch_dtype": torch_dtype, | |
| "local_files_only": is_local_files_only(**kwargs), | |
| } | |
| self.model = AutoPipelineForInpainting.from_pretrained( | |
| self.name, **model_kwargs | |
| ).to(device) | |
| enable_low_mem(self.model, kwargs.get("low_mem", False)) | |
| self.callback = kwargs.pop("callback", None) | |
| def forward(self, image, mask, config: InpaintRequest): | |
| """Input image and output image have same size | |
| image: [H, W, C] RGB | |
| mask: [H, W, 1] 255 means area to repaint | |
| return: BGR IMAGE | |
| """ | |
| self.set_scheduler(config) | |
| generator = torch.manual_seed(config.sd_seed) | |
| mask = mask.astype(np.float32) / 255 | |
| img_h, img_w = image.shape[:2] | |
| # kandinsky 没有 strength | |
| output = self.model( | |
| prompt=config.prompt, | |
| negative_prompt=config.negative_prompt, | |
| image=PIL.Image.fromarray(image), | |
| mask_image=mask[:, :, 0], | |
| height=img_h, | |
| width=img_w, | |
| num_inference_steps=config.sd_steps, | |
| guidance_scale=config.sd_guidance_scale, | |
| output_type="np", | |
| callback_on_step_end=self.callback, | |
| generator=generator, | |
| ).images[0] | |
| output = (output * 255).round().astype("uint8") | |
| output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) | |
| return output | |
| class Kandinsky22(Kandinsky): | |
| name = KANDINSKY22_NAME | |