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import os
import random
import cv2
import numpy as np
import torch
import time
from loguru import logger
from iopaint.helper import get_cache_path_by_url, load_jit_model, download_model
from .base import InpaintModel
from iopaint.schema import InpaintRequest
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: InpaintRequest):
"""
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
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