123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
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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# TODO: gpu_id
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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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# only paste masked area result
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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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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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