import random import PIL.Image import cv2 import numpy as np import torch from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \ EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler from loguru import logger from lama_cleaner.helper import resize_max_size from lama_cleaner.model.base import InpaintModel from lama_cleaner.model.utils import torch_gc from lama_cleaner.schema import Config, SDSampler 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)] class SD(InpaintModel): 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['sd_disable_nsfw']: logger.info("Disable Stable Diffusion Model NSFW checker") model_kwargs.update(dict( safety_checker=None, )) use_gpu = device == torch.device('cuda') and torch.cuda.is_available() torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 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 ) self.model = self.model.to(device) # 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('sd_enable_xformers', False): self.model.enable_xformers_memory_efficient_attention() if kwargs.get('cpu_offload', False) and torch.cuda.is_available(): # TODO: gpu_id self.model.enable_sequential_cpu_offload(gpu_id=0) else: 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 _scaled_pad_forward(self, image, mask, config: Config): longer_side_length = int(config.sd_scale * max(image.shape[:2])) origin_size = image.shape[:2] downsize_image = resize_max_size(image, size_limit=longer_side_length) downsize_mask = resize_max_size(mask, size_limit=longer_side_length) logger.info( f"Resize image to do sd inpainting: {image.shape} -> {downsize_image.shape}" ) inpaint_result = self._pad_forward(downsize_image, downsize_mask, config) # only paste masked area result inpaint_result = cv2.resize( inpaint_result, (origin_size[1], origin_size[0]), interpolation=cv2.INTER_CUBIC, ) original_pixel_indices = mask < 127 inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices] return inpaint_result 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 if config.sd_sampler == SDSampler.ddim: scheduler = DDIMScheduler.from_config(scheduler_config) elif config.sd_sampler == SDSampler.pndm: scheduler = PNDMScheduler.from_config(scheduler_config) elif config.sd_sampler == SDSampler.k_lms: scheduler = LMSDiscreteScheduler.from_config(scheduler_config) elif config.sd_sampler == SDSampler.k_euler: scheduler = EulerDiscreteScheduler.from_config(scheduler_config) elif config.sd_sampler == SDSampler.k_euler_a: scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config) elif config.sd_sampler == SDSampler.dpm_plus_plus: scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config) else: raise ValueError(config.sd_sampler) self.model.scheduler = scheduler seed = config.sd_seed random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) 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, ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output @torch.no_grad() def __call__(self, image, mask, config: Config): """ images: [H, W, C] RGB, not normalized masks: [H, W] return: BGR IMAGE """ # boxes = boxes_from_mask(mask) if config.use_croper: crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config) crop_image = self._scaled_pad_forward(crop_img, crop_mask, config) inpaint_result = image[:, :, ::-1] inpaint_result[t:b, l:r, :] = crop_image else: inpaint_result = self._scaled_pad_forward(image, mask, config) return inpaint_result 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): model_id_or_path = "runwayml/stable-diffusion-inpainting" class SD2(SD): model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"