import gc import PIL.Image import cv2 import numpy as np import torch from loguru import logger from lama_cleaner.model.base import DiffusionInpaintModel from lama_cleaner.model.utils import torch_gc, get_scheduler from lama_cleaner.schema import Config class CPUTextEncoderWrapper: def __init__(self, text_encoder, torch_dtype): self.config = text_encoder.config self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True) self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True) self.torch_dtype = torch_dtype del text_encoder torch_gc() def __call__(self, x, **kwargs): input_device = x.device return [ self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0] .to(input_device) .to(self.torch_dtype) ] @property def dtype(self): return self.torch_dtype def load_from_local_model(local_model_path, torch_dtype, disable_nsfw=True): from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( load_pipeline_from_original_stable_diffusion_ckpt, ) from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline logger.info(f"Converting {local_model_path} to diffusers pipeline") pipe = load_pipeline_from_original_stable_diffusion_ckpt( local_model_path, num_in_channels=9, from_safetensors=local_model_path.endswith("safetensors"), device="cpu", ) inpaint_pipe = StableDiffusionInpaintPipeline( vae=pipe.vae, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer, unet=pipe.unet, scheduler=pipe.scheduler, safety_checker=None if disable_nsfw else pipe.safety_checker, feature_extractor=None if disable_nsfw else pipe.safety_checker, requires_safety_checker=not disable_nsfw, ) del pipe gc.collect() return inpaint_pipe.to(torch_dtype) class SD(DiffusionInpaintModel): pad_mod = 8 min_size = 512 def init_model(self, device: torch.device, **kwargs): from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline fp16 = not kwargs.get("no_half", False) model_kwargs = { "local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"]) } 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 if kwargs.get("sd_local_model_path", None): self.model = load_from_local_model( kwargs["sd_local_model_path"], torch_dtype=torch_dtype, ) else: self.model = StableDiffusionInpaintPipeline.from_pretrained( self.model_id_or_path, revision="fp16" if use_gpu and fp16 else "main", torch_dtype=torch_dtype, use_auth_token=kwargs["hf_access_token"], **model_kwargs, ) # 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: # TODO: gpu_id 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.callback = kwargs.pop("callback", None) 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 """ scheduler_config = self.model.scheduler.config scheduler = get_scheduler(config.sd_sampler, scheduler_config) self.model.scheduler = scheduler 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, guidance_scale=config.sd_guidance_scale, output_type="np.array", callback=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 def forward_post_process(self, result, image, mask, config): if config.sd_match_histograms: result = self._match_histograms(result, image[:, :, ::-1], mask) if config.sd_mask_blur != 0: k = 2 * config.sd_mask_blur + 1 mask = cv2.GaussianBlur(mask, (k, k), 0) return result, image, mask @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings return True class SD15(SD): name = "sd1.5" model_id_or_path = "runwayml/stable-diffusion-inpainting" class Anything4(SD): name = "anything4" model_id_or_path = "Sanster/anything-4.0-inpainting" class RealisticVision14(SD): name = "realisticVision1.4" model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting" class SD2(SD): name = "sd2" model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"