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| 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 | |
| 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 | |
| def is_downloaded() -> bool: | |
| # model will be downloaded when app start, and can't switch in frontend settings | |
| return True | |