# // 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...")
# Usamos --depth 1 para um clone mais rápido, já que não precisamos do histórico
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.
# Mudar para o diretório do repositório. ESSENCIAL para caminhos de arquivos relativos.
os.chdir(repo_dir_name)
print(f"Diretório de trabalho alterado para: {os.getcwd()}")
# Adicionar o diretório ao sys.path. ESSENCIAL para as importações de módulos.
sys.path.insert(0, os.path.abspath('.'))
print(f"Diretório atual adicionado ao sys.path para importações.")
# --- ETAPA 3: Instalação de Dependências e Download de Modelos ---
# Agora que estamos no diretório correto, podemos prosseguir.
import torch
from pathlib import Path
from urllib.parse import urlparse
from torch.hub import download_url_to_file, get_dir
import shlex
# Função de download do original
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
# URLs dos modelos
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',
}
# Criar diretório de checkpoints e baixar modelos
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)
# Baixar vídeos de exemplo
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')
# Instalar dependências de forma robusta
python_executable = sys.executable
subprocess.run([python_executable, "-m", "pip", "install", "flash-attn", "--no-build-isolation"], env={**os.environ, "FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, check=True)
apex_wheel_path = "apex-0.1-cp310-cp310-linux_x86_64.whl"
if os.path.exists(apex_wheel_path):
print("Instalando o Apex a partir do arquivo wheel...")
subprocess.run([python_executable, "-m", "pip", "install", "--force-reinstall", "--no-cache-dir", apex_wheel_path], check=True)
print("✅ Configuração do Apex concluída.")
else:
print(f"AVISO: O arquivo wheel do Apex '{apex_wheel_path}' não foi encontrado no repositório clonado.")
# --- ETAPA 4: Execução do Código Principal da Aplicação ---
# Agora que o ambiente está perfeito, importamos e executamos o resto do script.
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)
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 = cfg_scale
runner.config.diffusion.cfg.rescale = cfg_rescale
runner.config.diffusion.timesteps.sampling.steps = 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: # Assumimos que é uma imagem
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}