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import random |
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import PIL |
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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 diffusers import DiffusionPipeline |
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from loguru import logger |
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from lama_cleaner.helper import resize_max_size |
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from lama_cleaner.model.base import InpaintModel |
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from lama_cleaner.schema import Config |
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class PaintByExample(InpaintModel): |
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pad_mod = 8 |
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min_size = 512 |
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def init_model(self, device: torch.device, **kwargs): |
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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 = {"local_files_only": kwargs.get('local_files_only', False)} |
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if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False): |
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logger.info("Disable Paint By Example Model NSFW checker") |
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model_kwargs.update(dict( |
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safety_checker=None, |
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requires_safety_checker=False |
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)) |
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self.model = DiffusionPipeline.from_pretrained( |
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"Fantasy-Studio/Paint-by-Example", |
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torch_dtype=torch_dtype, |
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**model_kwargs |
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) |
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self.model.enable_attention_slicing() |
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if kwargs.get('enable_xformers', False): |
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self.model.enable_xformers_memory_efficient_attention() |
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if kwargs.get('cpu_offload', False) and use_gpu: |
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self.model.image_encoder = self.model.image_encoder.to(device) |
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self.model.enable_sequential_cpu_offload(gpu_id=0) |
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else: |
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self.model = self.model.to(device) |
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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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seed = config.paint_by_example_seed |
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random.seed(seed) |
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np.random.seed(seed) |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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output = self.model( |
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image=PIL.Image.fromarray(image), |
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), |
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example_image=config.paint_by_example_example_image, |
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num_inference_steps=config.paint_by_example_steps, |
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output_type='np.array', |
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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 _scaled_pad_forward(self, image, mask, config: Config): |
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longer_side_length = int(config.sd_scale * max(image.shape[:2])) |
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origin_size = image.shape[:2] |
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downsize_image = resize_max_size(image, size_limit=longer_side_length) |
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downsize_mask = resize_max_size(mask, size_limit=longer_side_length) |
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logger.info( |
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f"Resize image to do paint_by_example: {image.shape} -> {downsize_image.shape}" |
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) |
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inpaint_result = self._pad_forward(downsize_image, downsize_mask, config) |
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inpaint_result = cv2.resize( |
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inpaint_result, |
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(origin_size[1], origin_size[0]), |
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interpolation=cv2.INTER_CUBIC, |
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) |
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original_pixel_indices = mask < 127 |
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inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices] |
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return inpaint_result |
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@torch.no_grad() |
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def __call__(self, image, mask, config: Config): |
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""" |
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images: [H, W, C] RGB, not normalized |
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masks: [H, W] |
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return: BGR IMAGE |
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""" |
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if config.use_croper: |
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crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config) |
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crop_image = self._scaled_pad_forward(crop_img, crop_mask, config) |
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inpaint_result = image[:, :, ::-1] |
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inpaint_result[t:b, l:r, :] = crop_image |
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else: |
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inpaint_result = self._scaled_pad_forward(image, mask, config) |
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return inpaint_result |
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def forward_post_process(self, result, image, mask, config): |
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if config.paint_by_example_match_histograms: |
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result = self._match_histograms(result, image[:, :, ::-1], mask) |
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if config.paint_by_example_mask_blur != 0: |
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k = 2 * config.paint_by_example_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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return True |
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