import os import random import cv2 import numpy as np import torch import time from loguru import logger from lama_cleaner.helper import get_cache_path_by_url, load_jit_model, download_model from lama_cleaner.model.base import InpaintModel from lama_cleaner.schema import Config MANGA_INPAINTOR_MODEL_URL = os.environ.get( "MANGA_INPAINTOR_MODEL_URL", "https://github.com/Sanster/models/releases/download/manga/manga_inpaintor.jit", ) MANGA_INPAINTOR_MODEL_MD5 = os.environ.get( "MANGA_INPAINTOR_MODEL_MD5", "7d8b269c4613b6b3768af714610da86c" ) MANGA_LINE_MODEL_URL = os.environ.get( "MANGA_LINE_MODEL_URL", "https://github.com/Sanster/models/releases/download/manga/erika.jit", ) MANGA_LINE_MODEL_MD5 = os.environ.get( "MANGA_LINE_MODEL_MD5", "0c926d5a4af8450b0d00bc5b9a095644" ) class Manga(InpaintModel): name = "manga" pad_mod = 16 is_erase_model = True def init_model(self, device, **kwargs): self.inpaintor_model = load_jit_model( MANGA_INPAINTOR_MODEL_URL, device, MANGA_INPAINTOR_MODEL_MD5 ) self.line_model = load_jit_model( MANGA_LINE_MODEL_URL, device, MANGA_LINE_MODEL_MD5 ) self.seed = 42 @staticmethod def download(): download_model(MANGA_INPAINTOR_MODEL_URL, MANGA_INPAINTOR_MODEL_MD5) download_model(MANGA_LINE_MODEL_URL, MANGA_LINE_MODEL_MD5) @staticmethod def is_downloaded() -> bool: model_paths = [ get_cache_path_by_url(MANGA_INPAINTOR_MODEL_URL), get_cache_path_by_url(MANGA_LINE_MODEL_URL), ] return all([os.path.exists(it) for it in model_paths]) def forward(self, image, mask, config: Config): """ image: [H, W, C] RGB mask: [H, W, 1] return: BGR IMAGE """ seed = self.seed random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) gray_img = torch.from_numpy( gray_img[np.newaxis, np.newaxis, :, :].astype(np.float32) ).to(self.device) start = time.time() lines = self.line_model(gray_img) torch.cuda.empty_cache() lines = torch.clamp(lines, 0, 255) logger.info(f"erika_model time: {time.time() - start}") mask = torch.from_numpy(mask[np.newaxis, :, :, :]).to(self.device) mask = mask.permute(0, 3, 1, 2) mask = torch.where(mask > 0.5, 1.0, 0.0) noise = torch.randn_like(mask) ones = torch.ones_like(mask) gray_img = gray_img / 255 * 2 - 1.0 lines = lines / 255 * 2 - 1.0 start = time.time() inpainted_image = self.inpaintor_model(gray_img, lines, mask, noise, ones) logger.info(f"image_inpaintor_model time: {time.time() - start}") cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy() cur_res = (cur_res * 127.5 + 127.5).astype(np.uint8) cur_res = cv2.cvtColor(cur_res, cv2.COLOR_GRAY2BGR) return cur_res