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 .utils import handle_from_pretrained_exceptions, get_torch_dtype, enable_low_mem 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, dtype=torch_dtype, num_in_channels=num_in_channels, load_safety_checker=False ) else: model_kwargs = {**kwargs.get("pipe_components", {})} 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