| import os | |
| import PIL.Image | |
| import cv2 | |
| import torch | |
| from diffusers import AutoencoderKL | |
| from loguru import logger | |
| from iopaint.schema import InpaintRequest, ModelType | |
| from .base import DiffusionInpaintModel | |
| from .helper.cpu_text_encoder import CPUTextEncoderWrapper | |
| from .original_sd_configs import get_config_files | |
| from .utils import ( | |
| handle_from_pretrained_exceptions, | |
| get_torch_dtype, | |
| enable_low_mem, | |
| is_local_files_only, | |
| ) | |
| class SDXL(DiffusionInpaintModel): | |
| name = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1" | |
| pad_mod = 8 | |
| min_size = 512 | |
| lcm_lora_id = "latent-consistency/lcm-lora-sdxl" | |
| model_id_or_path = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1" | |
| def init_model(self, device: torch.device, **kwargs): | |
| from diffusers.pipelines import StableDiffusionXLInpaintPipeline | |
| use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) | |
| if self.model_info.model_type == ModelType.DIFFUSERS_SDXL: | |
| num_in_channels = 4 | |
| else: | |
| num_in_channels = 9 | |
| if os.path.isfile(self.model_id_or_path): | |
| self.model = StableDiffusionXLInpaintPipeline.from_single_file( | |
| self.model_id_or_path, | |
| torch_dtype=torch_dtype, | |
| num_in_channels=num_in_channels, | |
| load_safety_checker=False, | |
| config_files=get_config_files() | |
| ) | |
| else: | |
| model_kwargs = { | |
| **kwargs.get("pipe_components", {}), | |
| "local_files_only": is_local_files_only(**kwargs), | |
| } | |
| if "vae" not in model_kwargs: | |
| vae = AutoencoderKL.from_pretrained( | |
| "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype | |
| ) | |
| model_kwargs["vae"] = vae | |
| self.model = handle_from_pretrained_exceptions( | |
| StableDiffusionXLInpaintPipeline.from_pretrained, | |
| pretrained_model_name_or_path=self.model_id_or_path, | |
| torch_dtype=torch_dtype, | |
| variant="fp16", | |
| **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) | |
| if kwargs["sd_cpu_textencoder"]: | |
| logger.info("Run Stable Diffusion TextEncoder on CPU") | |
| self.model.text_encoder = CPUTextEncoderWrapper( | |
| self.model.text_encoder, torch_dtype | |
| ) | |
| self.model.text_encoder_2 = CPUTextEncoderWrapper( | |
| self.model.text_encoder_2, torch_dtype | |
| ) | |
| self.callback = kwargs.pop("callback", None) | |
| 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 | |
| """ | |
| self.set_scheduler(config) | |
| img_h, img_w = image.shape[:2] | |
| output = self.model( | |
| image=PIL.Image.fromarray(image), | |
| prompt=config.prompt, | |
| negative_prompt=config.negative_prompt, | |
| mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), | |
| num_inference_steps=config.sd_steps, | |
| strength=0.999 if config.sd_strength == 1.0 else config.sd_strength, | |
| guidance_scale=config.sd_guidance_scale, | |
| output_type="np", | |
| callback_on_step_end=self.callback, | |
| height=img_h, | |
| width=img_w, | |
| generator=torch.manual_seed(config.sd_seed), | |
| ).images[0] | |
| output = (output * 255).round().astype("uint8") | |
| output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) | |
| return output | |