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| import PIL.Image | |
| import cv2 | |
| import torch | |
| from loguru import logger | |
| from iopaint.const import INSTRUCT_PIX2PIX_NAME | |
| from .base import DiffusionInpaintModel | |
| from iopaint.schema import InpaintRequest | |
| from .utils import get_torch_dtype, enable_low_mem, is_local_files_only | |
| class InstructPix2Pix(DiffusionInpaintModel): | |
| name = INSTRUCT_PIX2PIX_NAME | |
| pad_mod = 8 | |
| min_size = 512 | |
| def init_model(self, device: torch.device, **kwargs): | |
| from diffusers import StableDiffusionInstructPix2PixPipeline | |
| use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) | |
| model_kwargs = {"local_files_only": is_local_files_only(**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, | |
| ) | |
| ) | |
| self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained( | |
| self.name, variant="fp16", torch_dtype=torch_dtype, **model_kwargs | |
| ) | |
| enable_low_mem(self.model, kwargs.get("low_mem", False)) | |
| 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 | |