import random import PIL.Image import cv2 import numpy as np import torch from diffusers import PNDMScheduler, DDIMScheduler from loguru import logger from lama_cleaner.helper import norm_img from lama_cleaner.model.base import InpaintModel from lama_cleaner.schema import Config, SDSampler # # # def preprocess_image(image): # w, h = image.size # w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 # image = image.resize((w, h), resample=PIL.Image.LANCZOS) # image = np.array(image).astype(np.float32) / 255.0 # image = image[None].transpose(0, 3, 1, 2) # image = torch.from_numpy(image) # # [-1, 1] # return 2.0 * image - 1.0 # # # def preprocess_mask(mask): # mask = mask.convert("L") # w, h = mask.size # w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 # mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST) # mask = np.array(mask).astype(np.float32) / 255.0 # mask = np.tile(mask, (4, 1, 1)) # mask = mask[None].transpose(0, 1, 2, 3) # what does this step do? # mask = 1 - mask # repaint white, keep black # mask = torch.from_numpy(mask) # return mask class SD(InpaintModel): pad_mod = 32 min_size = 512 def init_model(self, device: torch.device, **kwargs): from .sd_pipeline import StableDiffusionInpaintPipeline self.model = StableDiffusionInpaintPipeline.from_pretrained( self.model_id_or_path, revision="fp16" if torch.cuda.is_available() else "main", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, use_auth_token=kwargs["hf_access_token"], ) # https://huggingface.co/docs/diffusers/v0.3.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing self.model.enable_attention_slicing() self.model = self.model.to(device) self.callbacks = kwargs.pop("callbacks", None) @torch.cuda.amp.autocast() 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 """ # image = norm_img(image) # [0, 1] # image = image * 2 - 1 # [0, 1] -> [-1, 1] # resize to latent feature map size # h, w = mask.shape[:2] # mask = cv2.resize(mask, (h // 8, w // 8), interpolation=cv2.INTER_AREA) # mask = norm_img(mask) # # image = torch.from_numpy(image).unsqueeze(0).to(self.device) # mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) if config.sd_sampler == SDSampler.ddim: scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) elif config.sd_sampler == SDSampler.pndm: PNDM_kwargs = { "tensor_format": "pt", "beta_schedule": "scaled_linear", "beta_start": 0.00085, "beta_end": 0.012, "num_train_timesteps": 1000, "skip_prk_steps": True, } scheduler = PNDMScheduler(**PNDM_kwargs) 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] output = self.model( prompt=config.prompt, init_image=PIL.Image.fromarray(image), mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), strength=config.sd_strength, num_inference_steps=config.sd_steps, guidance_scale=config.sd_guidance_scale, output_type="np.array", callbacks=self.callbacks, ).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 """ img_h, img_w = image.shape[:2] # boxes = boxes_from_mask(mask) if config.use_croper: logger.info("use croper") l, t, w, h = ( config.croper_x, config.croper_y, config.croper_width, config.croper_height, ) r = l + w b = t + h l = max(l, 0) r = min(r, img_w) t = max(t, 0) b = min(b, img_h) crop_img = image[t:b, l:r, :] crop_mask = mask[t:b, l:r] crop_image = self._pad_forward(crop_img, crop_mask, config) inpaint_result = image[:, :, ::-1] inpaint_result[t:b, l:r, :] = crop_image else: inpaint_result = self._pad_forward(image, mask, config) return inpaint_result @staticmethod def is_downloaded() -> bool: # model will be downloaded when app start, and can't switch in frontend settings return True class SD14(SD): model_id_or_path = "CompVis/stable-diffusion-v1-4" class SD15(SD): model_id_or_path = "CompVis/stable-diffusion-v1-5"