From 8cb9bd403e0b8206eb69780b97b35cf7aa84bd4e Mon Sep 17 00:00:00 2001 From: wyz Date: Wed, 4 May 2022 13:09:51 +0800 Subject: [PATCH] fix colorspace bug & support multi-gpu and multi-processing (#312) * fix colorspace bug of ffmpeg stream, add multi-gpu and multi-processing suport for inference_realesrgan_video.py * fix code format Co-authored-by: yanzewu --- docs/anime_video_model.md | 15 +- inference_realesrgan_video.py | 492 +++++++++++++++++++--------------- realesrgan/utils.py | 4 +- 3 files changed, 288 insertions(+), 223 deletions(-) diff --git a/docs/anime_video_model.md b/docs/anime_video_model.md index 0b0ce69..df98210 100644 --- a/docs/anime_video_model.md +++ b/docs/anime_video_model.md @@ -35,13 +35,20 @@ The following are some demos (best view in the full screen mode). ```bash # download model wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P realesrgan/weights -# inference -python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --stream +# single gpu and single process inference +CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 +# single gpu and multi process inference (you can use multi-processing to improve GPU utilization) +CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2 +# multi gpu and multi process inference +CUDA_VISIBLE_DEVICES=0,1,2,3 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2 ``` ```console Usage: ---stream with this option, the enhanced frames are sent directly to a ffmpeg stream, - avoiding storing large (usually tens of GB) intermediate results. +--num_process_per_gpu The total number of process is num_gpu * num_process_per_gpu. The bottleneck of + the program lies on the IO, so the GPUs are usually not fully utilized. To alleviate + this issue, you can use multi-processing by setting this parameter. As long as it + does not exceed the CUDA memory +--extract_frame_first If you encounter ffmpeg error when using multi-processing, you can turn this option on. ``` ### NCNN Executable File diff --git a/inference_realesrgan_video.py b/inference_realesrgan_video.py index d0da9fd..39fed08 100644 --- a/inference_realesrgan_video.py +++ b/inference_realesrgan_video.py @@ -4,111 +4,235 @@ import glob import mimetypes import numpy as np import os -import queue import shutil +import subprocess import torch from basicsr.archs.rrdbnet_arch import RRDBNet -from basicsr.utils.logger import AvgTimer +from os import path as osp from tqdm import tqdm -from realesrgan import IOConsumer, PrefetchReader, RealESRGANer +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'] -def get_frames(args, extract_frames=False): - # input can be a video file / a folder of frames / an image - is_video = False - if mimetypes.guess_type(args.input)[0].startswith('video'): # is a video file - is_video = True - video_name = os.path.splitext(os.path.basename(args.input))[0] - if extract_frames: - frame_folder = os.path.join('tmp_frames', video_name) - os.makedirs(frame_folder, exist_ok=True) - # use ffmpeg to extract frames - os.system(f'ffmpeg -i {args.input} -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 {frame_folder}/frame%08d.png') - # get image path list - paths = sorted(glob.glob(os.path.join(frame_folder, '*'))) else: - paths = [] - # get input video fps - if args.fps is None: - import ffmpeg - probe = ffmpeg.probe(args.input) - video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video'] - args.fps = eval(video_streams[0]['avg_frame_rate']) - elif mimetypes.guess_type(args.input)[0].startswith('image'): # is an image file - paths = [args.input] + 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 in ['RealESRGAN_x4plus', '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 + elif args.model_name in ['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 + elif args.model_name in ['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 + elif args.model_name in ['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 else: - paths = sorted(glob.glob(os.path.join(args.input, '*'))) - assert len(paths) > 0, 'the input folder is empty' + raise NotImplementedError - if args.fps is None: - args.fps = 24 + # ---------------------- determine model paths ---------------------- # + model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth') + if not os.path.isfile(model_path): + model_path = os.path.join('realesrgan/weights', args.model_name + '.pth') + if not os.path.isfile(model_path): + raise ValueError(f'Model {args.model_name} does not exist.') - return is_video, paths + # restorer + upsampler = RealESRGANer( + scale=netscale, + model_path=model_path, + 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 -def inference_stream(args, upsampler, face_enhancer): - try: - import ffmpeg - except ImportError: - import pip - pip.main(['install', '--user', 'ffmpeg-python']) - import ffmpeg - - is_video, paths = get_frames(args, extract_frames=False) - video_name = os.path.splitext(os.path.basename(args.input))[0] - video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4') - - # decoder - if is_video: - # get height and width - probe = ffmpeg.probe(args.input) - video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video'] - width = video_streams[0]['width'] - height = video_streams[0]['height'] - - # set up frame decoder - decoder = ( - ffmpeg.input(args.input).output('pipe:', format='rawvideo', pix_fmt='rgb24', loglevel='warning').run_async( - pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)) + 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/v1.3.0/GFPGANv1.3.pth', + upscale=args.outscale, + arch='clean', + channel_multiplier=2, + bg_upsampler=upsampler) # TODO support custom device else: - from PIL import Image - tmp_img = Image.open(paths[0]) - width, height = tmp_img.size - idx = 0 + face_enhancer = None - 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).') - # encoder - if is_video: - audio = ffmpeg.input(args.input).audio - encoder = ( - ffmpeg.input( - 'pipe:', format='rawvideo', pix_fmt='rgb24', s=f'{out_width}x{out_height}', framerate=args.fps).output( - audio, video_save_path, pix_fmt='yuv420p', vcodec='libx264', loglevel='info', - acodec='copy').overwrite_output().run_async(pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)) - else: - encoder = ( - ffmpeg.input( - 'pipe:', format='rawvideo', pix_fmt='rgb24', s=f'{out_width}x{out_height}', - framerate=args.fps).output(video_save_path, pix_fmt='yuv420p', vcodec='libx264', - loglevel='info').overwrite_output().run_async( - pipe_stdin=True, pipe_stdout=True, cmd=args.ffmpeg_bin)) + 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: - if is_video: - img_bytes = decoder.stdout.read(width * height * 3) # 3 bytes for one pixel - if not img_bytes: - break - img = np.frombuffer(img_bytes, np.uint8).reshape([height, width, 3]) - else: - if idx >= len(paths): - break - img = cv2.imread(paths[idx]) - idx += 1 + img = reader.get_frame() + if img is None: + break try: if args.face_enhance: @@ -119,86 +243,60 @@ def inference_stream(args, upsampler, face_enhancer): print('Error', error) print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') else: - output = output.astype(np.uint8).tobytes() - encoder.stdin.write(output) - - torch.cuda.synchronize() - - if is_video: - decoder.stdin.close() - decoder.wait() - encoder.stdin.close() - encoder.wait() - - -def inference_frames(args, upsampler, face_enhancer): - is_video, paths = get_frames(args, extract_frames=True) - video_name = os.path.splitext(os.path.basename(args.input))[0] - - # for saving restored frames - save_frame_folder = os.path.join(args.output, video_name, 'frames_tmpout') - os.makedirs(save_frame_folder, exist_ok=True) - - timer = AvgTimer() - timer.start() - pbar = tqdm(total=len(paths), unit='frame', desc='inference') - # set up prefetch reader - reader = PrefetchReader(paths, num_prefetch_queue=4) - reader.start() - - que = queue.Queue() - consumers = [IOConsumer(args, que, f'IO_{i}') for i in range(args.consumer)] - for consumer in consumers: - consumer.start() - - for idx, (path, img) in enumerate(zip(paths, reader)): - imgname, extension = os.path.splitext(os.path.basename(path)) - 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' - save_path = os.path.join(save_frame_folder, f'{imgname}_out.{extension}') - - que.put({'output': output, 'save_path': save_path}) + writer.write_frame(output) + torch.cuda.synchronize(device) pbar.update(1) - torch.cuda.synchronize() - timer.record() - avg_fps = 1. / (timer.get_avg_time() + 1e-7) - pbar.set_description(f'idx {idx}, fps {avg_fps:.2f}') - for _ in range(args.consumer): - que.put('quit') - for consumer in consumers: - consumer.join() - pbar.close() + reader.close() + writer.close() - # merge frames to video - video_save_path = os.path.join(args.output, f'{video_name}_{args.suffix}.mp4') - os.system(f'ffmpeg -r {args.fps} -i {save_frame_folder}/frame%08d_out.{extension} -i {args.input}' - f' -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r {args.fps} -pix_fmt yuv420p {video_save_path}') - # delete tmp file - shutil.rmtree(save_frame_folder) - frame_folder = os.path.join('tmp_frames', video_name) - if os.path.isdir(frame_folder): - shutil.rmtree(frame_folder) + +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(): @@ -226,9 +324,9 @@ def main(): 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('--consumer', type=int, default=4, help='Number of IO consumers') - parser.add_argument('--stream', action='store_true') 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=1) parser.add_argument( '--alpha_upsampler', @@ -243,61 +341,21 @@ def main(): args = parser.parse_args() args.input = args.input.rstrip('/').rstrip('\\') - - # ---------------------- determine models according to model names ---------------------- # - args.model_name = args.model_name.split('.pth')[0] - if args.model_name in ['RealESRGAN_x4plus', '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 - elif args.model_name in ['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 - elif args.model_name in ['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 - elif args.model_name in ['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 - - # ---------------------- determine model paths ---------------------- # - model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth') - if not os.path.isfile(model_path): - model_path = os.path.join('realesrgan/weights', args.model_name + '.pth') - if not os.path.isfile(model_path): - raise ValueError(f'Model {args.model_name} does not exist.') - - # restorer - upsampler = RealESRGANer( - scale=netscale, - model_path=model_path, - model=model, - tile=args.tile, - tile_pad=args.tile_pad, - pre_pad=args.pre_pad, - half=not args.fp32) - - 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/v1.3.0/GFPGANv1.3.pth', - upscale=args.outscale, - arch='clean', - channel_multiplier=2, - bg_upsampler=upsampler) - else: - face_enhancer = None - os.makedirs(args.output, exist_ok=True) - if args.stream: - inference_stream(args, upsampler, face_enhancer) + if mimetypes.guess_type(args.input)[0] is not None and mimetypes.guess_type(args.input)[0].startswith('video'): + is_video = True else: - inference_frames(args, upsampler, face_enhancer) + is_video = False + + 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__': diff --git a/realesrgan/utils.py b/realesrgan/utils.py index 10e7c23..922779c 100644 --- a/realesrgan/utils.py +++ b/realesrgan/utils.py @@ -26,7 +26,7 @@ class RealESRGANer(): half (float): Whether to use half precision during inference. Default: False. """ - def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False): + def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False, device=None): self.scale = scale self.tile_size = tile self.tile_pad = tile_pad @@ -35,7 +35,7 @@ class RealESRGANer(): self.half = half # initialize model - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device # if the model_path starts with https, it will first download models to the folder: realesrgan/weights if model_path.startswith('https://'): model_path = load_file_from_url(