# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates # // # // Licensed under the Apache License, Version 2.0 (the "License"); # // you may not use this file except in compliance with the License. # // You may obtain a copy of the License at # // # // http://www.apache.org/licenses/LICENSE-2.0 # // # // Unless required by applicable law or agreed to in writing, software # // distributed under the License is distributed on an "AS IS" BASIS, # // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # // See the License for the specific language governing permissions and # // limitations under the License. import spaces import subprocess import os import torch import mediapy from einops import rearrange from omegaconf import OmegaConf print(os.getcwd()) import datetime from tqdm import tqdm import gc from data.image.transforms.divisible_crop import DivisibleCrop from data.image.transforms.na_resize import NaResize from data.video.transforms.rearrange import Rearrange if os.path.exists("./projects/video_diffusion_sr/color_fix.py"): from projects.video_diffusion_sr.color_fix import wavelet_reconstruction use_colorfix=True else: use_colorfix = False print('Note!!!!!! Color fix is not avaliable!') from torchvision.transforms import Compose, Lambda, Normalize from torchvision.io.video import read_video import argparse from common.distributed import ( get_device, init_torch, ) from common.distributed.advanced import ( get_data_parallel_rank, get_data_parallel_world_size, get_sequence_parallel_rank, get_sequence_parallel_world_size, init_sequence_parallel, ) from projects.video_diffusion_sr.infer import VideoDiffusionInfer from common.config import load_config from common.distributed.ops import sync_data from common.seed import set_seed from common.partition import partition_by_groups, partition_by_size import gradio as gr from pathlib import Path from urllib.parse import urlparse from torch.hub import download_url_to_file, get_dir import shlex import uuid os.environ["MASTER_ADDR"] = "127.0.0.1" os.environ["MASTER_PORT"] = "12355" os.environ["RANK"] = str(0) os.environ["WORLD_SIZE"] = str(1) subprocess.run( "pip install flash-attn --no-build-isolation", env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, shell=True, ) def load_file_from_url(url, model_dir=None, progress=True, file_name=None): """Load file form http url, will download models if necessary. Reference: https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py Args: url (str): URL to be downloaded. model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. Default: None. progress (bool): Whether to show the download progress. Default: True. file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. Returns: str: The path to the downloaded file. """ if model_dir is None: # use the pytorch hub_dir hub_dir = get_dir() model_dir = os.path.join(hub_dir, 'checkpoints') os.makedirs(model_dir, exist_ok=True) parts = urlparse(url) filename = os.path.basename(parts.path) if file_name is not None: filename = file_name cached_file = os.path.abspath(os.path.join(model_dir, filename)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) return cached_file # os.system("pip freeze") ckpt_dir = Path('./ckpts') if not ckpt_dir.exists(): ckpt_dir.mkdir() pretrain_model_url = { 'vae': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/ema_vae.pth', 'dit': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/seedvr2_ema_3b.pth', 'pos_emb': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/pos_emb.pt', 'neg_emb': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/neg_emb.pt', 'apex': 'https://huggingface.co/ByteDance-Seed/SeedVR2-3B/resolve/main/apex-0.1-cp310-cp310-linux_x86_64.whl' } # download weights if not os.path.exists('./ckpts/seedvr2_ema_3b.pth'): load_file_from_url(url=pretrain_model_url['dit'], model_dir='./ckpts/', progress=True, file_name=None) if not os.path.exists('./ckpts/ema_vae.pth'): load_file_from_url(url=pretrain_model_url['vae'], model_dir='./ckpts/', progress=True, file_name=None) if not os.path.exists('./pos_emb.pt'): load_file_from_url(url=pretrain_model_url['pos_emb'], model_dir='./', progress=True, file_name=None) if not os.path.exists('./neg_emb.pt'): load_file_from_url(url=pretrain_model_url['neg_emb'], model_dir='./', progress=True, file_name=None) if not os.path.exists('./apex-0.1-cp310-cp310-linux_x86_64.whl'): load_file_from_url(url=pretrain_model_url['apex'], model_dir='./', progress=True, file_name=None) subprocess.run(shlex.split("pip install apex-0.1-cp310-cp310-linux_x86_64.whl")) print(f"✅ setup completed Apex") # download images torch.hub.download_url_to_file( 'https://huggingface.co/datasets/Iceclear/SeedVR_VideoDemos/resolve/main/seedvr_videos_crf23/aigc1k/23_1_lq.mp4', '01.mp4') torch.hub.download_url_to_file( 'https://huggingface.co/datasets/Iceclear/SeedVR_VideoDemos/resolve/main/seedvr_videos_crf23/aigc1k/28_1_lq.mp4', '02.mp4') torch.hub.download_url_to_file( 'https://huggingface.co/datasets/Iceclear/SeedVR_VideoDemos/resolve/main/seedvr_videos_crf23/aigc1k/2_1_lq.mp4', '03.mp4') def configure_sequence_parallel(sp_size): if sp_size > 1: init_sequence_parallel(sp_size) @spaces.GPU(duration=100) def configure_runner(sp_size): config_path = os.path.join('./configs_3b', 'main.yaml') config = load_config(config_path) runner = VideoDiffusionInfer(config) OmegaConf.set_readonly(runner.config, False) init_torch(cudnn_benchmark=False, timeout=datetime.timedelta(seconds=3600)) configure_sequence_parallel(sp_size) runner.configure_dit_model(device="cuda", checkpoint='./ckpts/seedvr2_ema_3b.pth') runner.configure_vae_model() # Set memory limit. if hasattr(runner.vae, "set_memory_limit"): runner.vae.set_memory_limit(**runner.config.vae.memory_limit) return runner @spaces.GPU(duration=100) def generation_step(runner, text_embeds_dict, cond_latents): def _move_to_cuda(x): return [i.to(torch.device("cuda")) for i in x] noises = [torch.randn_like(latent) for latent in cond_latents] aug_noises = [torch.randn_like(latent) for latent in cond_latents] print(f"Generating with noise shape: {noises[0].size()}.") noises, aug_noises, cond_latents = sync_data((noises, aug_noises, cond_latents), 0) noises, aug_noises, cond_latents = list( map(lambda x: _move_to_cuda(x), (noises, aug_noises, cond_latents)) ) cond_noise_scale = 0.1 def _add_noise(x, aug_noise): t = ( torch.tensor([1000.0], device=torch.device("cuda")) * cond_noise_scale ) shape = torch.tensor(x.shape[1:], device=torch.device("cuda"))[None] t = runner.timestep_transform(t, shape) print( f"Timestep shifting from" f" {1000.0 * cond_noise_scale} to {t}." ) x = runner.schedule.forward(x, aug_noise, t) return x conditions = [ runner.get_condition( noise, task="sr", latent_blur=_add_noise(latent_blur, aug_noise), ) for noise, aug_noise, latent_blur in zip(noises, aug_noises, cond_latents) ] with torch.no_grad(), torch.autocast("cuda", torch.bfloat16, enabled=True): video_tensors = runner.inference( noises=noises, conditions=conditions, dit_offload=False, **text_embeds_dict, ) samples = [ ( rearrange(video[:, None], "c t h w -> t c h w") if video.ndim == 3 else rearrange(video, "c t h w -> t c h w") ) for video in video_tensors ] del video_tensors return samples @spaces.GPU(duration=100) def generation_loop(video_path='./test_videos', seed=666, fps_out=12, batch_size=1, cfg_scale=1.0, cfg_rescale=0.0, sample_steps=1, res_h=1280, res_w=720, sp_size=1): runner = configure_runner(1) output_dir = 'output/' + str(uuid.uuid4()) + '.mp4' def _build_pos_and_neg_prompt(): # read positive prompt positive_text = "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, \ hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, \ skin pore detailing, hyper sharpness, perfect without deformations." # read negative prompt negative_text = "painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, \ CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, \ signature, jpeg artifacts, deformed, lowres, over-smooth" return positive_text, negative_text def _build_test_prompts(video_path): positive_text, negative_text = _build_pos_and_neg_prompt() original_videos = [] prompts = {} video_list = os.listdir(video_path) for f in video_list: # if f.endswith(".mp4"): original_videos.append(f) prompts[f] = positive_text print(f"Total prompts to be generated: {len(original_videos)}") return original_videos, prompts, negative_text def _extract_text_embeds(): # Text encoder forward. positive_prompts_embeds = [] for texts_pos in tqdm(original_videos_local): text_pos_embeds = torch.load('pos_emb.pt') text_neg_embeds = torch.load('neg_emb.pt') positive_prompts_embeds.append( {"texts_pos": [text_pos_embeds], "texts_neg": [text_neg_embeds]} ) gc.collect() torch.cuda.empty_cache() return positive_prompts_embeds def cut_videos(videos, sp_size): if videos.size(1) > 121: videos = videos[:, :121] t = videos.size(1) if t <= 4 * sp_size: print(f"Cut input video size: {videos.size()}") padding = [videos[:, -1].unsqueeze(1)] * (4 * sp_size - t + 1) padding = torch.cat(padding, dim=1) videos = torch.cat([videos, padding], dim=1) return videos if (t - 1) % (4 * sp_size) == 0: return videos else: padding = [videos[:, -1].unsqueeze(1)] * ( 4 * sp_size - ((t - 1) % (4 * sp_size)) ) padding = torch.cat(padding, dim=1) videos = torch.cat([videos, padding], dim=1) assert (videos.size(1) - 1) % (4 * sp_size) == 0 return videos # classifier-free guidance runner.config.diffusion.cfg.scale = cfg_scale runner.config.diffusion.cfg.rescale = cfg_rescale # sampling steps runner.config.diffusion.timesteps.sampling.steps = sample_steps runner.configure_diffusion() # set random seed set_seed(seed, same_across_ranks=True) os.makedirs('output/', exist_ok=True) tgt_path = 'output/' # get test prompts original_videos = [video_path.split('/')[-1]] # divide the prompts into different groups original_videos_group = original_videos # store prompt mapping original_videos_local = original_videos_group original_videos_local = partition_by_size(original_videos_local, batch_size) # pre-extract the text embeddings positive_prompts_embeds = _extract_text_embeds() video_transform = Compose( [ NaResize( resolution=( res_h * res_w ) ** 0.5, mode="area", # Upsample image, model only trained for high res. downsample_only=False, ), Lambda(lambda x: torch.clamp(x, 0.0, 1.0)), DivisibleCrop((16, 16)), Normalize(0.5, 0.5), Rearrange("t c h w -> c t h w"), ] ) # generation loop for videos, text_embeds in tqdm(zip(original_videos_local, positive_prompts_embeds)): # read condition latents cond_latents = [] for video in videos: video = ( read_video( os.path.join(video_path), output_format="TCHW" )[0] / 255.0 ) print(f"Read video size: {video.size()}") cond_latents.append(video_transform(video.to(torch.device("cuda")))) ori_lengths = [video.size(1) for video in cond_latents] input_videos = cond_latents cond_latents = [cut_videos(video, sp_size) for video in cond_latents] # runner.dit.to("cpu") print(f"Encoding videos: {list(map(lambda x: x.size(), cond_latents))}") # runner.vae.to(torch.device("cuda")) cond_latents = runner.vae_encode(cond_latents) # runner.vae.to("cpu") # runner.dit.to(torch.device("cuda")) for i, emb in enumerate(text_embeds["texts_pos"]): text_embeds["texts_pos"][i] = emb.to(torch.device("cuda")) for i, emb in enumerate(text_embeds["texts_neg"]): text_embeds["texts_neg"][i] = emb.to(torch.device("cuda")) samples = generation_step(runner, text_embeds, cond_latents=cond_latents) # runner.dit.to("cpu") del cond_latents # dump samples to the output directory for path, input, sample, ori_length in zip( videos, input_videos, samples, ori_lengths ): if ori_length < sample.shape[0]: sample = sample[:ori_length] # color fix input = ( rearrange(input[:, None], "c t h w -> t c h w") if input.ndim == 3 else rearrange(input, "c t h w -> t c h w") ) if use_colorfix: sample = wavelet_reconstruction( sample.to("cpu"), input[: sample.size(0)].to("cpu") ) else: sample = sample.to("cpu") sample = ( rearrange(sample[:, None], "t c h w -> t h w c") if sample.ndim == 3 else rearrange(sample, "t c h w -> t h w c") ) sample = sample.clip(-1, 1).mul_(0.5).add_(0.5).mul_(255).round() sample = sample.to(torch.uint8).numpy() mediapy.write_video( output_dir, sample, fps=fps_out ) # print(f"Generated video size: {sample.shape}") gc.collect() torch.cuda.empty_cache() return output_dir, output_dir with gr.Blocks(title="SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training") as demo: # Top logo and title gr.HTML("""
SeedVR logo

Official Gradio demo for SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training.
🔥 SeedVR2 is a one-step image and video restoration algorithm for real-world and AIGC content.

""") # Interface with gr.Row(): input_video = gr.Video(label="Upload a video") seed = gr.Number(label="Seeds", value=666) fps = gr.Number(label="fps", value=24) with gr.Row(): output_video = gr.Video(label="Output") download_link = gr.File(label="Download the output") run_button = gr.Button("Run") run_button.click(fn=generation_loop, inputs=[input_video, seed, fps], outputs=[output_video, download_link]) # Examples gr.Examples( examples=[ ["./01.mp4", 4, 24], ["./02.mp4", 4, 24], ["./03.mp4", 4, 24], ], inputs=[input_video, seed, fps] ) # Article/Footer gr.HTML("""

If you find SeedVR helpful, please ⭐ the GitHub repository:

GitHub Stars

Notice

This demo supports up to 720p and 121 frames. For other use cases (image restoration, video resolutions beyond 720p, etc), check the GitHub repo.

Limitations

May fail on heavy degradations or small-motion AIGC clips, causing oversharpening or poor restoration.

Citation

        @article{wang2025seedvr2,
            title={SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training},
            author={Wang, Jianyi and Lin, Shanchuan and Lin, Zhijie and Ren, Yuxi and Wei, Meng and Yue, Zongsheng and Zhou, Shangchen and Chen, Hao and Zhao, Yang and Yang, Ceyuan and Xiao, Xuefeng and Loy, Chen Change and Jiang, Lu},
            booktitle={arXiv preprint arXiv:2506.05301},
            year={2025}
        }

        @inproceedings{wang2025seedvr,
            title={SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration},
            author={Wang, Jianyi and Lin, Zhijie and Wei, Meng and Zhao, Yang and Yang, Ceyuan and Loy, Chen Change and Jiang, Lu},
            booktitle={CVPR},
            year={2025}
        }

License

Licensed under the Apache 2.0 License.

Contact

Email: iceclearwjy@gmail.com

Twitter Follow GitHub Follow

visitors

""") demo.queue() demo.launch()