Upload 2 files

Real-ESRGAN/inference_realesrgan.py

@@ -0,0 +1,166 @@
+import argparse
+import cv2
+import glob
+import os
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.download_util import load_file_from_url
+
+from realesrgan import RealESRGANer
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+
+def main():
+    """Inference demo for Real-ESRGAN.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
+    parser.add_argument(
+        '-n',
+        '--model_name',
+        type=str,
+        default='RealESRGAN_x4plus',
+        help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
+              'realesr-animevideov3 | realesr-general-x4v3'))
+    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
+    parser.add_argument(
+        '-dn',
+        '--denoise_strength',
+        type=float,
+        default=0.5,
+        help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
+              'Only used for the realesr-general-x4v3 model'))
+    parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
+    parser.add_argument(
+        '--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
+    parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
+    parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
+    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
+    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
+    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
+    parser.add_argument(
+        '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
+    parser.add_argument(
+        '--alpha_upsampler',
+        type=str,
+        default='realesrgan',
+        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
+    parser.add_argument(
+        '--ext',
+        type=str,
+        default='auto',
+        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
+    parser.add_argument(
+        '-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')
+
+    args = parser.parse_args()
+
+    # determine models according to model names
+    args.model_name = args.model_name.split('.')[0]
+    if args.model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
+    elif args.model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
+    elif args.model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
+    elif args.model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+        netscale = 2
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
+    elif args.model_name == 'realesr-animevideov3':  # x4 VGG-style model (XS size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
+    elif args.model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = [
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
+        ]
+
+    # determine model paths
+    if args.model_path is not None:
+        model_path = args.model_path
+    else:
+        model_path = os.path.join('weights', args.model_name + '.pth')
+        if not os.path.isfile(model_path):
+            ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+            for url in file_url:
+                # model_path will be updated
+                model_path = load_file_from_url(
+                    url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
+
+    # use dni to control the denoise strength
+    dni_weight = None
+    if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
+        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
+        model_path = [model_path, wdn_model_path]
+        dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
+
+    # restorer
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=dni_weight,
+        model=model,
+        tile=args.tile,
+        tile_pad=args.tile_pad,
+        pre_pad=args.pre_pad,
+        half=not args.fp32,
+        gpu_id=args.gpu_id)
+
+    if args.face_enhance:  # Use GFPGAN for face enhancement
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth',
+            upscale=args.outscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)
+    os.makedirs(args.output, exist_ok=True)
+
+    if os.path.isfile(args.input):
+        paths = [args.input]
+    else:
+        paths = sorted(glob.glob(os.path.join(args.input, '*')))
+
+    for idx, path in enumerate(paths):
+        imgname, extension = os.path.splitext(os.path.basename(path))
+        print('Testing', idx, imgname)
+
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        else:
+            img_mode = None
+
+        try:
+            if args.face_enhance:
+                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+            else:
+                output, _ = upsampler.enhance(img, outscale=args.outscale)
+        except RuntimeError as error:
+            print('Error', error)
+            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+        else:
+            if args.ext == 'auto':
+                extension = extension[1:]
+            else:
+                extension = args.ext
+            if img_mode == 'RGBA':  # RGBA images should be saved in png format
+                extension = 'png'
+            if args.suffix == '':
+                save_path = os.path.join(args.output, f'{imgname}.{extension}')
+            else:
+                save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
+            cv2.imwrite(save_path, output)
+
+
+if __name__ == '__main__':
+    main()

Real-ESRGAN/inference_realesrgan_video.py

@@ -0,0 +1,398 @@
+import argparse
+import cv2
+import glob
+import mimetypes
+import numpy as np
+import os
+import shutil
+import subprocess
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.download_util import load_file_from_url
+from os import path as osp
+from tqdm import tqdm
+
+from realesrgan import RealESRGANer
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+try:
+    import ffmpeg
+except ImportError:
+    import pip
+    pip.main(['install', '--user', 'ffmpeg-python'])
+    import ffmpeg
+
+
+def get_video_meta_info(video_path):
+    ret = {}
+    probe = ffmpeg.probe(video_path)
+    video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
+    has_audio = any(stream['codec_type'] == 'audio' for stream in probe['streams'])
+    ret['width'] = video_streams[0]['width']
+    ret['height'] = video_streams[0]['height']
+    ret['fps'] = eval(video_streams[0]['avg_frame_rate'])
+    ret['audio'] = ffmpeg.input(video_path).audio if has_audio else None
+    ret['nb_frames'] = int(video_streams[0]['nb_frames'])
+    return ret
+
+
+def get_sub_video(args, num_process, process_idx):
+    if num_process == 1:
+        return args.input
+    meta = get_video_meta_info(args.input)
+    duration = int(meta['nb_frames'] / meta['fps'])
+    part_time = duration // num_process
+    print(f'duration: {duration}, part_time: {part_time}')
+    os.makedirs(osp.join(args.output, f'{args.video_name}_inp_tmp_videos'), exist_ok=True)
+    out_path = osp.join(args.output, f'{args.video_name}_inp_tmp_videos', f'{process_idx:03d}.mp4')
+    cmd = [
+        args.ffmpeg_bin, f'-i {args.input}', '-ss', f'{part_time * process_idx}',
+        f'-to {part_time * (process_idx + 1)}' if process_idx != num_process - 1 else '', '-async 1', out_path, '-y'
+    ]
+    print(' '.join(cmd))
+    subprocess.call(' '.join(cmd), shell=True)
+    return out_path
+
+
+class Reader:
+
+    def __init__(self, args, total_workers=1, worker_idx=0):
+        self.args = args
+        input_type = mimetypes.guess_type(args.input)[0]
+        self.input_type = 'folder' if input_type is None else input_type
+        self.paths = []  # for image&folder type
+        self.audio = None
+        self.input_fps = None
+        if self.input_type.startswith('video'):
+            video_path = get_sub_video(args, total_workers, worker_idx)
+            self.stream_reader = (
+                ffmpeg.input(video_path).output('pipe:', format='rawvideo', pix_fmt='bgr24',
+                                                loglevel='error').run_async(
+                                                    pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
+            meta = get_video_meta_info(video_path)
+            self.width = meta['width']
+            self.height = meta['height']
+            self.input_fps = meta['fps']
+            self.audio = meta['audio']
+            self.nb_frames = meta['nb_frames']
+
+        else:
+            if self.input_type.startswith('image'):
+                self.paths = [args.input]
+            else:
+                paths = sorted(glob.glob(os.path.join(args.input, '*')))
+                tot_frames = len(paths)
+                num_frame_per_worker = tot_frames // total_workers + (1 if tot_frames % total_workers else 0)
+                self.paths = paths[num_frame_per_worker * worker_idx:num_frame_per_worker * (worker_idx + 1)]
+
+            self.nb_frames = len(self.paths)
+            assert self.nb_frames > 0, 'empty folder'
+            from PIL import Image
+            tmp_img = Image.open(self.paths[0])
+            self.width, self.height = tmp_img.size
+        self.idx = 0
+
+    def get_resolution(self):
+        return self.height, self.width
+
+    def get_fps(self):
+        if self.args.fps is not None:
+            return self.args.fps
+        elif self.input_fps is not None:
+            return self.input_fps
+        return 24
+
+    def get_audio(self):
+        return self.audio
+
+    def __len__(self):
+        return self.nb_frames
+
+    def get_frame_from_stream(self):
+        img_bytes = self.stream_reader.stdout.read(self.width * self.height * 3)  # 3 bytes for one pixel
+        if not img_bytes:
+            return None
+        img = np.frombuffer(img_bytes, np.uint8).reshape([self.height, self.width, 3])
+        return img
+
+    def get_frame_from_list(self):
+        if self.idx >= self.nb_frames:
+            return None
+        img = cv2.imread(self.paths[self.idx])
+        self.idx += 1
+        return img
+
+    def get_frame(self):
+        if self.input_type.startswith('video'):
+            return self.get_frame_from_stream()
+        else:
+            return self.get_frame_from_list()
+
+    def close(self):
+        if self.input_type.startswith('video'):
+            self.stream_reader.stdin.close()
+            self.stream_reader.wait()
+
+
+class Writer:
+
+    def __init__(self, args, audio, height, width, video_save_path, fps):
+        out_width, out_height = int(width * args.outscale), int(height * args.outscale)
+        if out_height > 2160:
+            print('You are generating video that is larger than 4K, which will be very slow due to IO speed.',
+                  'We highly recommend to decrease the outscale(aka, -s).')
+
+        if audio is not None:
+            self.stream_writer = (
+                ffmpeg.input('pipe:', format='rawvideo', pix_fmt='bgr24', s=f'{out_width}x{out_height}',
+                             framerate=fps).output(
+                                 audio,
+                                 video_save_path,
+                                 pix_fmt='yuv420p',
+                                 vcodec='libx264',
+                                 loglevel='error',
+                                 acodec='copy').overwrite_output().run_async(
+                                     pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
+        else:
+            self.stream_writer = (
+                ffmpeg.input('pipe:', format='rawvideo', pix_fmt='bgr24', s=f'{out_width}x{out_height}',
+                             framerate=fps).output(
+                                 video_save_path, pix_fmt='yuv420p', vcodec='libx264',
+                                 loglevel='error').overwrite_output().run_async(
+                                     pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin))
+
+    def write_frame(self, frame):
+        frame = frame.astype(np.uint8).tobytes()
+        self.stream_writer.stdin.write(frame)
+
+    def close(self):
+        self.stream_writer.stdin.close()
+        self.stream_writer.wait()
+
+
+def inference_video(args, video_save_path, device=None, total_workers=1, worker_idx=0):
+    # ---------------------- determine models according to model names ---------------------- #
+    args.model_name = args.model_name.split('.pth')[0]
+    if args.model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
+    elif args.model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
+    elif args.model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
+    elif args.model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+        netscale = 2
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
+    elif args.model_name == 'realesr-animevideov3':  # x4 VGG-style model (XS size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
+    elif args.model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = [
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
+        ]
+
+    # ---------------------- determine model paths ---------------------- #
+    model_path = os.path.join('weights', args.model_name + '.pth')
+    if not os.path.isfile(model_path):
+        ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+        for url in file_url:
+            # model_path will be updated
+            model_path = load_file_from_url(
+                url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
+
+    # use dni to control the denoise strength
+    dni_weight = None
+    if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
+        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
+        model_path = [model_path, wdn_model_path]
+        dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
+
+    # restorer
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=dni_weight,
+        model=model,
+        tile=args.tile,
+        tile_pad=args.tile_pad,
+        pre_pad=args.pre_pad,
+        half=not args.fp32,
+        device=device,
+    )
+
+    if 'anime' in args.model_name and args.face_enhance:
+        print('face_enhance is not supported in anime models, we turned this option off for you. '
+              'if you insist on turning it on, please manually comment the relevant lines of code.')
+        args.face_enhance = False
+
+    if args.face_enhance:  # Use GFPGAN for face enhancement
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth',
+            upscale=args.outscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)  # TODO support custom device
+    else:
+        face_enhancer = None
+
+    reader = Reader(args, total_workers, worker_idx)
+    audio = reader.get_audio()
+    height, width = reader.get_resolution()
+    fps = reader.get_fps()
+    writer = Writer(args, audio, height, width, video_save_path, fps)
+
+    pbar = tqdm(total=len(reader), unit='frame', desc='inference')
+    while True:
+        img = reader.get_frame()
+        if img is None:
+            break
+
+        try:
+            if args.face_enhance:
+                _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+            else:
+                output, _ = upsampler.enhance(img, outscale=args.outscale)
+        except RuntimeError as error:
+            print('Error', error)
+            print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+        else:
+            writer.write_frame(output)
+
+        torch.cuda.synchronize(device)
+        pbar.update(1)
+
+    reader.close()
+    writer.close()
+
+
+def run(args):
+    args.video_name = osp.splitext(os.path.basename(args.input))[0]
+    video_save_path = osp.join(args.output, f'{args.video_name}_{args.suffix}.mp4')
+
+    if args.extract_frame_first:
+        tmp_frames_folder = osp.join(args.output, f'{args.video_name}_inp_tmp_frames')
+        os.makedirs(tmp_frames_folder, exist_ok=True)
+        os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0  {tmp_frames_folder}/frame%08d.png')
+        args.input = tmp_frames_folder
+
+    num_gpus = torch.cuda.device_count()
+    num_process = num_gpus * args.num_process_per_gpu
+    if num_process == 1:
+        inference_video(args, video_save_path)
+        return
+
+    ctx = torch.multiprocessing.get_context('spawn')
+    pool = ctx.Pool(num_process)
+    os.makedirs(osp.join(args.output, f'{args.video_name}_out_tmp_videos'), exist_ok=True)
+    pbar = tqdm(total=num_process, unit='sub_video', desc='inference')
+    for i in range(num_process):
+        sub_video_save_path = osp.join(args.output, f'{args.video_name}_out_tmp_videos', f'{i:03d}.mp4')
+        pool.apply_async(
+            inference_video,
+            args=(args, sub_video_save_path, torch.device(i % num_gpus), num_process, i),
+            callback=lambda arg: pbar.update(1))
+    pool.close()
+    pool.join()
+
+    # combine sub videos
+    # prepare vidlist.txt
+    with open(f'{args.output}/{args.video_name}_vidlist.txt', 'w') as f:
+        for i in range(num_process):
+            f.write(f'file \'{args.video_name}_out_tmp_videos/{i:03d}.mp4\'\n')
+
+    cmd = [
+        args.ffmpeg_bin, '-f', 'concat', '-safe', '0', '-i', f'{args.output}/{args.video_name}_vidlist.txt', '-c',
+        'copy', f'{video_save_path}'
+    ]
+    print(' '.join(cmd))
+    subprocess.call(cmd)
+    shutil.rmtree(osp.join(args.output, f'{args.video_name}_out_tmp_videos'))
+    if osp.exists(osp.join(args.output, f'{args.video_name}_inp_tmp_videos')):
+        shutil.rmtree(osp.join(args.output, f'{args.video_name}_inp_tmp_videos'))
+    os.remove(f'{args.output}/{args.video_name}_vidlist.txt')
+
+
+def main():
+    """Inference demo for Real-ESRGAN.
+    It mainly for restoring anime videos.
+
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', type=str, default='inputs', help='Input video, image or folder')
+    parser.add_argument(
+        '-n',
+        '--model_name',
+        type=str,
+        default='realesr-animevideov3',
+        help=('Model names: realesr-animevideov3 | RealESRGAN_x4plus_anime_6B | RealESRGAN_x4plus | RealESRNet_x4plus |'
+              ' RealESRGAN_x2plus | realesr-general-x4v3'
+              'Default:realesr-animevideov3'))
+    parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
+    parser.add_argument(
+        '-dn',
+        '--denoise_strength',
+        type=float,
+        default=0.5,
+        help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
+              'Only used for the realesr-general-x4v3 model'))
+    parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
+    parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored video')
+    parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
+    parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
+    parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
+    parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
+    parser.add_argument(
+        '--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
+    parser.add_argument('--fps', type=float, default=None, help='FPS of the output video')
+    parser.add_argument('--ffmpeg_bin', type=str, default='ffmpeg', help='The path to ffmpeg')
+    parser.add_argument('--extract_frame_first', action='store_true')
+    parser.add_argument('--num_process_per_gpu', type=int, default=4)
+
+    parser.add_argument(
+        '--alpha_upsampler',
+        type=str,
+        default='realesrgan',
+        help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
+    parser.add_argument(
+        '--ext',
+        type=str,
+        default='auto',
+        help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
+    args = parser.parse_args()
+
+    args.input = args.input.rstrip('/').rstrip('\\')
+    os.makedirs(args.output, exist_ok=True)
+
+    if mimetypes.guess_type(args.input)[0] is not None and mimetypes.guess_type(args.input)[0].startswith('video'):
+        is_video = True
+    else:
+        is_video = False
+
+    if is_video and args.input.endswith('.flv'):
+        mp4_path = args.input.replace('.flv', '.mp4')
+        os.system(f'ffmpeg -i {args.input} -codec copy {mp4_path}')
+        args.input = mp4_path
+
+    if args.extract_frame_first and not is_video:
+        args.extract_frame_first = False
+
+    run(args)
+
+    if args.extract_frame_first:
+        tmp_frames_folder = osp.join(args.output, f'{args.video_name}_inp_tmp_frames')
+        shutil.rmtree(tmp_frames_folder)
+
+
+if __name__ == '__main__':
+    main()