#https://github.com/SkyTNT/anime-segmentation/tree/main #Only adapt isnet_is (https://huggingface.co/skytnt/anime-seg/blob/main/isnetis.ckpt) import torch.nn as nn import torch from .isnet import ISNetDIS import numpy as np import cv2 from comfy.model_management import get_torch_device DEVICE = get_torch_device() class AnimeSegmentation: def __init__(self, ckpt_path): super(AnimeSegmentation).__init__() sd = torch.load(ckpt_path, map_location="cpu") self.net = ISNetDIS() #gt_encoder isn't used during inference self.net.load_state_dict({k.replace("net.", ''):v for k, v in sd.items() if k.startswith("net.")}) self.net = self.net.to(DEVICE) self.net.eval() def get_mask(self, input_img, s=640): input_img = (input_img / 255).astype(np.float32) if s == 0: img_input = np.transpose(input_img, (2, 0, 1)) img_input = img_input[np.newaxis, :] tmpImg = torch.from_numpy(img_input).float().to(DEVICE) with torch.no_grad(): pred = self.net(tmpImg)[0][0].sigmoid() #https://github.com/SkyTNT/anime-segmentation/blob/main/train.py#L92C20-L92C47 pred = pred.cpu().numpy()[0] pred = np.transpose(pred, (1, 2, 0)) #pred = pred[:, :, np.newaxis] return pred h, w = h0, w0 = input_img.shape[:-1] h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) ph, pw = s - h, s - w img_input = np.zeros([s, s, 3], dtype=np.float32) img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h)) img_input = np.transpose(img_input, (2, 0, 1)) img_input = img_input[np.newaxis, :] tmpImg = torch.from_numpy(img_input).float().to(DEVICE) with torch.no_grad(): pred = self.net(tmpImg)[0][0].sigmoid() #https://github.com/SkyTNT/anime-segmentation/blob/main/train.py#L92C20-L92C47 pred = pred.cpu().numpy()[0] pred = np.transpose(pred, (1, 2, 0)) pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] #pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis] pred = cv2.resize(pred, (w0, h0)) return pred def __call__(self, np_img, img_size): mask = self.get_mask(np_img, int(img_size)) np_img = (mask * np_img + 255 * (1 - mask)).astype(np.uint8) mask = (mask * 255).astype(np.uint8) #np_img = np.concatenate([np_img, mask], axis=2, dtype=np.uint8) #mask = mask.repeat(3, axis=2) return mask, np_img