import os import cv2 import numpy as np import torch from lama_cleaner.helper import ( norm_img, get_cache_path_by_url, load_jit_model, download_model, ) from lama_cleaner.model.base import InpaintModel from lama_cleaner.schema import InpaintRequest LAMA_MODEL_URL = os.environ.get( "LAMA_MODEL_URL", "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt", ) LAMA_MODEL_MD5 = os.environ.get("LAMA_MODEL_MD5", "e3aa4aaa15225a33ec84f9f4bc47e500") class LaMa(InpaintModel): name = "lama" pad_mod = 8 is_erase_model = True @staticmethod def download(): download_model(LAMA_MODEL_URL, LAMA_MODEL_MD5) def init_model(self, device, **kwargs): self.model = load_jit_model(LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval() @staticmethod def is_downloaded() -> bool: return os.path.exists(get_cache_path_by_url(LAMA_MODEL_URL)) def forward(self, image, mask, config: InpaintRequest): """Input image and output image have same size image: [H, W, C] RGB mask: [H, W] return: BGR IMAGE """ image = norm_img(image) mask = norm_img(mask) mask = (mask > 0) * 1 image = torch.from_numpy(image).unsqueeze(0).to(self.device) mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) inpainted_image = self.model(image, mask) cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy() cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8") cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) return cur_res