Delete app.py

app.py

@@ -1,191 +0,0 @@
-import os
-import sys
-import subprocess
-import importlib.util
-
-# --- ETAPA 0: Instalação Final do flash-attn ---
-
-# Verifica se o flash_attn já está instalado. Se não, instala.
-package_name = 'flash_attn'
-spec = importlib.util.find_spec(package_name)
-if spec is None:
-    print(f"Instalando o pacote que faltava: {package_name}. Isso pode levar um minuto...")
-    # Usamos o python executável do ambiente atual para instalar o pacote
-    python_executable = sys.executable
-    subprocess.run(
-        [
-            python_executable, "-m", "pip", "install", 
-            "flash_attn==2.5.9.post1", 
-            "--no-build-isolation"
-        ],
-        check=True
-    )
-    print(f"✅ {package_name} instalado com sucesso.")
-else:
-    print(f"✅ Pacote {package_name} já está instalado.")
-
-
-# A partir daqui, o ambiente está 100% pronto.
-# ---------------------------------------------------------------------
-
-import spaces
-from pathlib import Path
-from urllib.parse import urlparse
-import torch
-from torch.hub import download_url_to_file
-import mediapy
-from einops import rearrange
-from omegaconf import OmegaConf
-import datetime
-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
-
-# --- ETAPA 1: Clonar o Repositório e Mudar para o Diretório ---
-repo_name = "SeedVR"
-if not os.path.exists(repo_name):
-    print(f"Clonando o repositório {repo_name} do GitHub...")
-    subprocess.run(f"git clone https://github.com/ByteDance-Seed/{repo_name}.git", shell=True, check=True)
-
-# Garante que estamos no diretório certo
-if not os.getcwd().endswith(repo_name):
-    os.chdir(repo_name)
-
-sys.path.insert(0, os.path.abspath('.'))
-
-# Importações do projeto SeedVR (só podem ser feitas após o chdir)
-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.seed import set_seed
-from projects.video_diffusion_sr.infer import VideoDiffusionInfer
-from common.distributed.ops import sync_data
-
-print("Ambiente Conda carregado e verificado. Iniciando a aplicação...")
-
-# --- ETAPA 2: Baixar os Modelos Pré-treinados ---
-print("Baixando modelos pré-treinados...")
-
-def load_file_from_url(url, model_dir='.', progress=True, file_name=None):
-    os.makedirs(model_dir, exist_ok=True)
-    if not file_name:
-        parts = urlparse(url)
-        file_name = os.path.basename(parts.path)
-    cached_file = os.path.join(model_dir, file_name)
-    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',
-}
-
-Path('./ckpts').mkdir(exist_ok=True)
-for key, url in pretrain_model_url.items():
-    model_dir = './ckpts' if key in ['vae', 'dit'] else '.'
-    load_file_from_url(url=url, model_dir=model_dir)
-
-# --- ETAPA 3: Executar a Aplicação Principal ---
-os.environ["MASTER_ADDR"] = "127.0.0.1"
-os.environ["MASTER_PORT"] = "12355"
-os.environ["RANK"] = str(0)
-os.environ["WORLD_SIZE"] = str(1)
-
-def configure_runner():
-    config = load_config('configs_3b/main.yaml')
-    runner = VideoDiffusionInfer(config)
-    OmegaConf.set_readonly(runner.config, False)
-    init_torch(cudnn_benchmark=False, timeout=datetime.timedelta(seconds=3600))
-    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("cuda") for i in x]
-    noises, aug_noises = [torch.randn_like(l) for l in cond_latents], [torch.randn_like(l) for l in cond_latents]
-    noises, aug_noises, cond_latents = sync_data((noises, aug_noises, cond_latents), 0)
-    noises, aug_noises, cond_latents = map(_move_to_cuda, (noises, aug_noises, cond_latents))
-    def _add_noise(x, aug_noise):
-        t = torch.tensor([100.0], device="cuda")
-        shape = torch.tensor(x.shape[1:], device="cuda")[None]
-        t = runner.timestep_transform(t, shape)
-        return runner.schedule.forward(x, aug_noise, t)
-    conditions = [runner.get_condition(n, task="sr", latent_blur=_add_noise(l, an)) for n, an, l 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, **text_embeds_dict)
-    return [rearrange(v, "c t h w -> t c h w") for v in video_tensors]
-
-@spaces.GPU
-def generation_loop(video_path, seed=666, fps_out=24):
-    if video_path is None: return None, None, None
-    runner = configure_runner()
-    text_embeds = {
-        "texts_pos": [torch.load('pos_emb.pt', weights_only=True).to("cuda")],
-        "texts_neg": [torch.load('neg_emb.pt', weights_only=True).to("cuda")]
-    }
-    runner.configure_diffusion()
-    set_seed(int(seed))
-    os.makedirs("output", exist_ok=True)
-    res_h, res_w = 1280, 720
-    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")
-    ])
-    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_path = os.path.join("output", f"{uuid.uuid4()}.mp4")
-    else:
-        video = T.ToTensor()(Image.open(video_path).convert("RGB")).unsqueeze(0)
-        output_path = os.path.join("output", f"{uuid.uuid4()}.png")
-    cond_latents = [transform(video.to("cuda"))]
-    ori_length = cond_latents[0].size(2)
-    cond_latents = runner.vae_encode(cond_latents)
-    samples = generation_step(runner, text_embeds, cond_latents)
-    sample = samples[0][:ori_length].cpu()
-    sample = rearrange(sample, "t c h w -> t h w c").clip(-1, 1).add(1).mul(127.5).byte().numpy()
-    if is_video:
-        mediapy.write_video(output_path, sample, fps=fps_out)
-        return None, output_path, output_path
-    else:
-        mediapy.write_image(output_path, sample[0])
-        return output_path, None, output_path
-
-with gr.Blocks(title="SeedVR") as demo:
-    gr.HTML(f"""
-        <p><b>Demonstração oficial do Gradio</b> para
-        <a href='https://github.com/ByteDance-Seed/SeedVR' target='-blank'>
-        <b>SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training</b></a>.<br>
-        🔥 <b>SeedVR2</b> é um algoritmo de restauração de imagem e vídeo em um passo para conteúdo do mundo real e AIGC.
-        </p>
-    """)
-    with gr.Row():
-        input_file = gr.File(label="Carregar Imagem ou Vídeo")
-        with gr.Column():
-            seed = gr.Number(label="Seed", value=42)
-            fps = gr.Number(label="FPS de Saída", value=24)
-    run_button = gr.Button("Executar")
-    output_image = gr.Image(label="Imagem de Saída")
-    output_video = gr.Video(label="Vídeo de Saída")
-    download_link = gr.File(label="Baixar Resultado")
-    run_button.click(fn=generation_loop, inputs=[input_file, seed, fps], outputs=[output_image, output_video, download_link])
-
-demo.queue().launch(share=True)