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| import PIL.Image | |
| import cv2 | |
| import torch | |
| 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 InstructPix2Pix(DiffusionInpaintModel): | |
| name = "instruct_pix2pix" | |
| pad_mod = 8 | |
| min_size = 512 | |
| def init_model(self, device: torch.device, **kwargs): | |
| from diffusers import StableDiffusionInstructPix2PixPipeline | |
| fp16 = not kwargs.get('no_half', False) | |
| model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)} | |
| if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False): | |
| logger.info("Disable Stable Diffusion Model NSFW checker") | |
| model_kwargs.update(dict( | |
| safety_checker=None, | |
| feature_extractor=None, | |
| requires_safety_checker=False | |
| )) | |
| use_gpu = device == torch.device('cuda') and torch.cuda.is_available() | |
| torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 | |
| self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
| "timbrooks/instruct-pix2pix", | |
| revision="fp16" if use_gpu and fp16 else "main", | |
| torch_dtype=torch_dtype, | |
| **model_kwargs | |
| ) | |
| self.model.enable_attention_slicing() | |
| if kwargs.get('enable_xformers', False): | |
| self.model.enable_xformers_memory_efficient_attention() | |
| if kwargs.get('cpu_offload', False) and use_gpu: | |
| logger.info("Enable sequential cpu offload") | |
| 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 | |
| edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0] | |
| """ | |
| output = self.model( | |
| image=PIL.Image.fromarray(image), | |
| prompt=config.prompt, | |
| negative_prompt=config.negative_prompt, | |
| num_inference_steps=config.p2p_steps, | |
| image_guidance_scale=config.p2p_image_guidance_scale, | |
| guidance_scale=config.p2p_guidance_scale, | |
| output_type="np.array", | |
| generator=torch.manual_seed(config.sd_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.sd_match_histograms: | |
| # result = self._match_histograms(result, image[:, :, ::-1], mask) | |
| # | |
| # if config.sd_mask_blur != 0: | |
| # k = 2 * config.sd_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 | |