import PIL.Image import cv2 import torch from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel from lama_cleaner.schema import InpaintRequest class InstructPix2Pix(DiffusionInpaintModel): name = "timbrooks/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 = {} 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( self.name, variant="fp16", torch_dtype=torch_dtype, **model_kwargs ) 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: InpaintRequest): """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.sd_steps, image_guidance_scale=config.p2p_image_guidance_scale, guidance_scale=config.sd_guidance_scale, output_type="np", generator=torch.manual_seed(config.sd_seed), ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output