import os import PIL.Image import cv2 import numpy as np import torch from diffusers import AutoencoderKL from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel from lama_cleaner.schema import Config class SDXL(DiffusionInpaintModel): name = "sdxl" 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 fp16 = not kwargs.get("no_half", False) use_gpu = device == torch.device("cuda") and torch.cuda.is_available() torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 if os.path.isfile(self.model_id_or_path): self.model = StableDiffusionXLInpaintPipeline.from_single_file( self.model_id_or_path, torch_dtype=torch_dtype ) else: vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) self.model = StableDiffusionXLInpaintPipeline.from_pretrained( self.model_id_or_path, revision="main", torch_dtype=torch_dtype, use_auth_token=kwargs["hf_access_token"], vae=vae, ) # https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing self.model.enable_attention_slicing() # https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention 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) if kwargs["sd_cpu_textencoder"]: logger.warning("Stable Diffusion XL not support run TextEncoder on CPU") self.callback = kwargs.pop("callback", None) @staticmethod def download(): from diffusers import AutoPipelineForInpainting AutoPipelineForInpainting.from_pretrained( "diffusers/stable-diffusion-xl-1.0-inpainting-0.1" ) 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 """ self.set_scheduler(config) if config.sd_mask_blur != 0: k = 2 * config.sd_mask_blur + 1 mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis] 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=self.callback, height=img_h, width=img_w, generator=torch.manual_seed(config.sd_seed), callback_steps=1, ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings return True