# // 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 sys
# --- ETAPA 1: Preparação do Ambiente ---
# Clonar o repositório para garantir que todas as pastas de código (data, common, etc.) existam.
repo_dir_name = "SeedVR2-3B"
if not os.path.exists(repo_dir_name):
print(f"Clonando o repositório {repo_dir_name} para obter todo o código-fonte...")
subprocess.run(f"git clone --depth 1 https://huggingface.co/spaces/ByteDance-Seed/{repo_dir_name}", shell=True, check=True)
# --- ETAPA 2: Configuração dos Caminhos ---
# Mudar para o diretório do repositório e adicioná-lo ao path do Python.
os.chdir(repo_dir_name)
print(f"Diretório de trabalho alterado para: {os.getcwd()}")
sys.path.insert(0, os.path.abspath('.'))
print(f"Diretório atual adicionado ao sys.path para importações.")
# --- ETAPA 3: Instalação de Dependências (NA ORDEM CORRETA) ---
python_executable = sys.executable
# **CORREÇÃO CRÍTICA: Instalar requisitos PRIMEIRO para ter o PyTorch disponível**
print("Instalando dependências a partir do requirements.txt (isso inclui o PyTorch)...")
subprocess.run([python_executable, "-m", "pip", "install", "-r", "requirements.txt"], check=True)
print("✅ Dependências básicas (incluindo PyTorch) instaladas.")
# **Compilar dependências otimizadas para a GPU L40S (Ada Lovelace)**
print("Instalando flash-attn compilando do zero...")
subprocess.run([python_executable, "-m", "pip", "install", "--force-reinstall", "--no-cache-dir", "flash-attn"], check=True)
print("Clonando e compilando o Apex do zero (isso pode demorar um pouco)...")
if not os.path.exists("apex"):
subprocess.run("git clone https://github.com/NVIDIA/apex", shell=True, check=True)
# Instala o Apex a partir da fonte clonada. Agora o PyTorch já existe e a compilação funcionará.
# As flags --cpp_ext e --cuda_ext são essenciais para a compilação.
subprocess.run(
[python_executable, "-m", "pip", "install", "-v", "--disable-pip-version-check", "--no-cache-dir", "--global-option=--cpp_ext", "--global-option=--cuda_ext", "./apex"],
check=True
)
print("✅ Configuração do Apex concluída.")
# **Download dos modelos e dados de exemplo**
import torch
from pathlib import Path
from urllib.parse import urlparse
from torch.hub import download_url_to_file, get_dir
def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
if model_dir is None:
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'Baixando: "{url}" para {cached_file}\n')
download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
return cached_file
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',
}
ckpt_dir = Path('./ckpts'); ckpt_dir.mkdir(exist_ok=True)
for key, url in pretrain_model_url.items():
filename = os.path.basename(url)
model_dir = './ckpts' if key in ['vae', 'dit'] else '.'
target_path = os.path.join(model_dir, filename)
if not os.path.exists(target_path):
load_file_from_url(url=url, model_dir=model_dir, progress=True, file_name=filename)
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')
print("✅ Modelos e dados de exemplo baixados.")
# --- ETAPA 4: Execução do Código Principal da Aplicação ---
import mediapy
from einops import rearrange
from omegaconf import OmegaConf
import datetime
from tqdm import tqdm
import gc
from PIL import Image
import gradio as gr
import uuid
import mimetypes
import torchvision.transforms as T
from torchvision.transforms import Compose, Lambda, Normalize
from torchvision.io.video import read_video
from data.image.transforms.divisible_crop import DivisibleCrop
from data.image.transforms.na_resize import NaResize
from data.video.transforms.rearrange import Rearrange
from common.config import load_config
from common.distributed import init_torch
from common.distributed.advanced import init_sequence_parallel
from common.seed import set_seed
from common.partition import partition_by_size
from projects.video_diffusion_sr.infer import VideoDiffusionInfer
from common.distributed.ops import sync_data
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "12355"
os.environ["RANK"] = str(0)
os.environ["WORLD_SIZE"] = str(1)
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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('Atenção!!!!!! A correção de cor não está disponível!')
def configure_sequence_parallel(sp_size):
if sp_size > 1:
init_sequence_parallel(sp_size)
def configure_runner(sp_size):
config_path = '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()
if hasattr(runner.vae, "set_memory_limit"):
runner.vae.set_memory_limit(**runner.config.vae.memory_limit)
return runner
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]
noises, aug_noises, cond_latents = sync_data((noises, aug_noises, cond_latents), 0)
noises, aug_noises, cond_latents = list(map(_move_to_cuda, (noises, aug_noises, cond_latents)))
def _add_noise(x, aug_noise):
t = torch.tensor([1000.0], device=torch.device("cuda")) * 0.1
shape = torch.tensor(x.shape[1:], device=torch.device("cuda"))[None]
t = runner.timestep_transform(t, shape)
return runner.schedule.forward(x, aug_noise, t)
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)
return [rearrange(video, "c t h w -> t c h w") for video in video_tensors]
@spaces.GPU
def generation_loop(video_path, seed=666, fps_out=24, batch_size=1, cfg_scale=1.0, cfg_rescale=0.0, sample_steps=1, res_h=1280, res_w=720, sp_size=1):
if video_path is None: return None, None, None
runner = configure_runner(1)
def _extract_text_embeds():
positive_prompts_embeds = []
for _ in original_videos_local:
positive_prompts_embeds.append({"texts_pos": [torch.load('pos_emb.pt')], "texts_neg": [torch.load('neg_emb.pt')]})
gc.collect(); torch.cuda.empty_cache()
return positive_prompts_embeds
runner.config.diffusion.cfg.scale, runner.config.diffusion.cfg.rescale, runner.config.diffusion.timesteps.sampling.steps = cfg_scale, cfg_rescale, sample_steps
runner.configure_diffusion()
set_seed(int(seed) % (2**32), same_across_ranks=True)
os.makedirs("output", exist_ok=True)
original_videos = [os.path.basename(video_path)]
original_videos_local = partition_by_size(original_videos, batch_size)
positive_prompts_embeds = _extract_text_embeds()
video_transform = Compose([NaResize(resolution=(res_h * res_w) ** 0.5, mode="area", 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")])
for videos, text_embeds in tqdm(zip(original_videos_local, positive_prompts_embeds)):
media_type, _ = mimetypes.guess_type(video_path)
is_video = media_type and media_type.startswith("video")
if is_video:
video, _, _ = read_video(video_path, output_format="TCHW"); video = video[:121] / 255.0; output_dir = os.path.join("output", f"{uuid.uuid4()}.mp4")
else:
video = T.ToTensor()(Image.open(video_path).convert("RGB")).unsqueeze(0); output_dir = os.path.join("output", f"{uuid.uuid4()}.png")
cond_latents = [video_transform(video.to("cuda"))]
ori_lengths = [v.size(1) for v in cond_latents]
cond_latents = runner.vae_encode(cond_latents)
for key in ["texts_pos", "texts_neg"]:
for i, emb in enumerate(text_embeds[key]): text_embeds[key][i] = emb.to("cuda")
samples = generation_step(runner, text_embeds, cond_latents=cond_latents)
del cond_latents
for sample, ori_length in zip(samples, ori_lengths):
sample = sample[:ori_length].to("cpu")
sample = rearrange(sample, "t c h w -> t h w c").clip(-1, 1).mul_(0.5).add_(0.5).mul_(255).round().to(torch.uint8).numpy()
if is_video: mediapy.write_video(output_dir, sample, fps=fps_out)
else: mediapy.write_image(output_dir, sample[0])
gc.collect(); torch.cuda.empty_cache()
return (None, output_dir, output_dir) if is_video else (output_dir, None, output_dir)
with gr.Blocks(title="SeedVR2: Restauração de Vídeo em Um Passo") as demo:
gr.HTML(f"""
Demonstração oficial do Gradio para
SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training.
🔥 SeedVR2 é um algoritmo de restauração de imagem e vídeo em um passo para conteúdo do mundo real e AIGC.
""")
with gr.Row():
input_file = gr.File(label="Carregar imagem ou vídeo")
with gr.Column():
seed = gr.Number(label="Seed", value=666)
fps = gr.Number(label="FPS de Saída (para vídeo)", value=24)
run_button = gr.Button("Executar")
with gr.Row():
output_image = gr.Image(label="Imagem de Saída")
output_video = gr.Video(label="Vídeo de Saída")
download_link = gr.File(label="Baixar o resultado")
run_button.click(fn=generation_loop, inputs=[input_file, seed, fps], outputs=[output_image, output_video, download_link])
gr.Examples(
examples=[
["01.mp4", 4, 24],
["02.mp4", 4, 24],
["03.mp4", 4, 24],
],
inputs=[input_file, seed, fps]
)
gr.HTML("""
Se você achou o SeedVR útil, por favor ⭐ o
repositório no GitHub:
Aviso
Esta demonstração suporta até 720p e 121 frames para vídeos ou imagens 2k.
Para outros casos de uso, verifique o repositório no GitHub.
Limitações
Pode falhar em degradações pesadas ou em clipes AIGC com pouco movimento, causando excesso de nitidez ou restauração inadequada.
""")
demo.queue().launch(share=True) ![SeedVR logo](file/{os.path.abspath("assets/seedvr_logo.png")}){kind=logo}