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