import random import PIL import PIL.Image import cv2 import numpy as np import torch from diffusers import DiffusionPipeline from loguru import logger from lama_cleaner.helper import resize_max_size from lama_cleaner.model.base import InpaintModel from lama_cleaner.schema import Config class PaintByExample(InpaintModel): 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 """ seed = config.paint_by_example_seed random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) 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', ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output def _scaled_pad_forward(self, image, mask, config: Config): longer_side_length = int(config.sd_scale * max(image.shape[:2])) origin_size = image.shape[:2] downsize_image = resize_max_size(image, size_limit=longer_side_length) downsize_mask = resize_max_size(mask, size_limit=longer_side_length) logger.info( f"Resize image to do paint_by_example: {image.shape} -> {downsize_image.shape}" ) inpaint_result = self._pad_forward(downsize_image, downsize_mask, config) # only paste masked area result inpaint_result = cv2.resize( inpaint_result, (origin_size[1], origin_size[0]), interpolation=cv2.INTER_CUBIC, ) original_pixel_indices = mask < 127 inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices] return inpaint_result @torch.no_grad() def __call__(self, image, mask, config: Config): """ images: [H, W, C] RGB, not normalized masks: [H, W] return: BGR IMAGE """ if config.use_croper: crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config) crop_image = self._scaled_pad_forward(crop_img, crop_mask, config) inpaint_result = image[:, :, ::-1] inpaint_result[t:b, l:r, :] = crop_image else: inpaint_result = self._scaled_pad_forward(image, mask, config) return inpaint_result 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