import PIL import PIL.Image import cv2 import torch from diffusers import DiffusionPipeline from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel from lama_cleaner.model.utils import set_seed from lama_cleaner.schema import Config class PaintByExample(DiffusionInpaintModel): name = "paint_by_example" pad_mod = 8 min_size = 512 def init_model(self, device: torch.device, **kwargs): fp16 = not kwargs.get('no_half', False) use_gpu = device == torch.device('cuda') and torch.cuda.is_available() torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)} if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False): logger.info("Disable Paint By Example Model NSFW checker") model_kwargs.update(dict( safety_checker=None, requires_safety_checker=False )) self.model = DiffusionPipeline.from_pretrained( "Fantasy-Studio/Paint-by-Example", torch_dtype=torch_dtype, **model_kwargs ) self.model.enable_attention_slicing() if kwargs.get('enable_xformers', False): self.model.enable_xformers_memory_efficient_attention() # TODO: gpu_id if kwargs.get('cpu_offload', False) and use_gpu: self.model.image_encoder = self.model.image_encoder.to(device) self.model.enable_sequential_cpu_offload(gpu_id=0) else: self.model = self.model.to(device) def forward(self, image, mask, config: Config): """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 """ output = self.model( image=PIL.Image.fromarray(image), mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), example_image=config.paint_by_example_example_image, num_inference_steps=config.paint_by_example_steps, output_type='np.array', generator=torch.manual_seed(config.paint_by_example_seed) ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output def forward_post_process(self, result, image, mask, config): if config.paint_by_example_match_histograms: result = self._match_histograms(result, image[:, :, ::-1], mask) if config.paint_by_example_mask_blur != 0: k = 2 * config.paint_by_example_mask_blur + 1 mask = cv2.GaussianBlur(mask, (k, k), 0) return result, image, mask @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings return True