app.py

import gradio as gr
import torch
import numpy as np
import tempfile
import os
import yaml
import json
from pathlib import Path
import random 
# Importações de Hugging Face
from huggingface_hub import snapshot_download, HfFolder
from transformers import T5EncoderModel, T5TokenizerFast
from diffusers import LTXLatentUpsamplePipeline
from diffusers.models import AutoencoderKLLTXVideo, LTXVideoTransformer3DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler

# Nossa pipeline customizada e utilitários
from pipeline_ltx_condition_control import LTXConditionPipeline, LTXVideoCondition
from diffusers.utils import export_to_video
from PIL import Image, ImageOps
import imageio

# --- Configuração de Logging e Avisos ---
import warnings
warnings.filterwarnings("ignore", category=UserWarning) # Correto: UserWarning é uma classe
warnings.filterwarnings("ignore", category=FutureWarning) # Correto: FutureWarning é uma classe
warnings.filterwarnings("ignore", message=".*")

# --- CARREGAMENTO DIRETO DOS MODELOS (SEM CLASSE) ---

print("=== [Inicialização da Aplicação] ===")

# 1. Carregar Configuração do Arquivo YAML
CONFIG_PATH = Path("ltxv-13b-0.9.8-distilled.yaml")
if not CONFIG_PATH.exists():
    raise FileNotFoundError(f"Arquivo de configuração '{CONFIG_PATH}' não encontrado.")
with open(CONFIG_PATH, "r") as f:
    config = yaml.safe_load(f)
print(f"Configuração carregada de: {CONFIG_PATH}")
print(json.dumps(config, indent=2))

# Parâmetros Globais
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16

base_repo="Lightricks/LTX-Video"
checkpoint_path="ltxv-13b-0.9.8-distilled.safetensors" 
upscaler_repo="Lightricks/ltxv-spatial-upscaler-0.9.7"

FPS = 24

# 2. Baixar os arquivos do modelo base
print(f"=== Baixando snapshot do repositório base: {base_repo} ===")
local_repo_path = snapshot_download(
    repo_id=base_repo,
    token=os.getenv("HF_TOKEN") or HfFolder.get_token(),
    resume_download=True
)

# 3. Carregar cada componente da pipeline explicitamente
print("=== Carregando componentes da pipeline... ===")
vae = AutoencoderKLLTXVideo.from_pretrained(local_repo_path, subfolder="vae", torch_dtype=torch_dtype)
text_encoder = T5EncoderModel.from_pretrained(local_repo_path, subfolder="text_encoder", torch_dtype=torch_dtype)
tokenizer = T5TokenizerFast.from_pretrained(local_repo_path, subfolder="tokenizer")
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(local_repo_path, subfolder="scheduler")

# Correção para o erro 'mu': desativar explicitamente o dynamic shifting
if hasattr(scheduler.config, 'use_dynamic_shifting') and scheduler.config.use_dynamic_shifting:
    print("[Config] Desativando 'use_dynamic_shifting' no scheduler.")
    scheduler.config.use_dynamic_shifting = False

print(f"Carregando pesos do Transformer de: {checkpoint_path}")
transformer = LTXVideoTransformer3DModel.from_pretrained(
    local_repo_path, subfolder="transformer", weight_name=checkpoint_path, torch_dtype=torch_dtype
)

# 4. Montar a pipeline principal
print("Montando a LTXConditionPipeline...")
pipeline = LTXConditionPipeline(
    vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, 
    scheduler=scheduler, transformer=transformer
)
pipeline.to(device)
pipeline.vae.enable_tiling()

# 5. Carregar a pipeline de upscale
print(f"Carregando o upsampler espacial de: {upscaler_repo}")
pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained(
    upscaler_repo, vae=vae, torch_dtype=torch_dtype
)
pipe_upsample.to(device)

print("=== [Inicialização Concluída] Aplicação pronta. ===")


# --- Lógica Principal da Geração de Vídeo ---

def round_to_nearest_resolution_acceptable_by_vae(height, width, vae_temporal_compression_ratio):
    height = height - (height % vae_temporal_compression_ratio)
    width = width - (width % vae_temporal_compression_ratio)
    return height, width

def prepare_and_generate_video(
    condition_image_1, condition_strength_1, condition_frame_index_1,
    condition_image_2, condition_strength_2, condition_frame_index_2,
    prompt, duration, negative_prompt,
    height, width, guidance_scale, seed, randomize_seed,
    progress=gr.Progress(track_tqdm=True)
):
    try:
        # Lógica para agrupar as condições *dentro* da função
        # Cálculo de frames e resolução
        num_frames = int(duration * FPS) + 1
        temporal_compression = pipeline.vae_temporal_compression_ratio
        num_frames = ((num_frames - 1) // temporal_compression) * temporal_compression + 1
        
        downscale_factor = 2 / 3
        downscaled_height = int(height * downscale_factor)
        downscaled_width = int(width * downscale_factor)
        downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(
            downscaled_height, downscaled_width, pipeline.vae_temporal_compression_ratio
        )
        
        
        
        conditions = []
        if condition_image_1 is not None:
            condition_image_1 = ImageOps.fit(condition_image_1, (downscaled_width, downscaled_height), Image.LANCZOS)
            conditions.append(LTXVideoCondition(
                image=condition_image_1,
                strength=condition_strength_1,
                frame_index=int(condition_frame_index_1)
            ))
        if condition_image_2 is not None:
            condition_image_2 = ImageOps.fit(condition_image_2, (downscaled_width, downscaled_height), Image.LANCZOS)
            conditions.append(LTXVideoCondition(
                image=condition_image_2,
                strength=condition_strength_2,
                frame_index=int(condition_frame_index_2)
            ))

        pipeline_args = {}
        if conditions:
            pipeline_args["conditions"] = conditions

        # Manipulação da seed
        if randomize_seed:
            seed = random.randint(0, 2**32 - 1)

                
        # ETAPA 1: Geração do vídeo em baixa resolução
        latents = pipeline(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=downscaled_width,
            height=downscaled_height,
            num_frames=num_frames,
            timesteps=[1000, 993, 987, 981, 975, 909, 725, 0.03],
            decode_timestep=0.05,
            decode_noise_scale=0.025,
            image_cond_noise_scale=0.0,
            guidance_scale=guidance_scale,
            guidance_rescale=0.7,
            generator=torch.Generator().manual_seed(seed),
            output_type="latent",
            **pipeline_args
        ).frames

        # ETAPA 2: Upscale dos latentes
        upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2
        upscaled_latents = pipe_upsample(
            latents=latents,
            output_type="latent"
        ).frames



        conditions = []
        if condition_image_1 is not None:
            condition_image_1 = ImageOps.fit(condition_image_1, (upscaled_width, upscaled_height), Image.LANCZOS)
            conditions.append(LTXVideoCondition(
                image=condition_image_1,
                strength=condition_strength_1,
                frame_index=int(condition_frame_index_1)
            ))
        if condition_image_2 is not None:
            condition_image_2 = ImageOps.fit(condition_image_2, (upscaled_width, upscaled_height), Image.LANCZOS)
            conditions.append(LTXVideoCondition(
                image=condition_image_2,
                strength=condition_strength_2,
                frame_index=int(condition_frame_index_2)
            ))

        pipeline_args = {}
        if conditions:
            pipeline_args["conditions"] = conditions
            
        

        # ETAPA 3: Denoise final em alta resolução
        final_video_frames_np = pipeline(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=upscaled_width,
            height=upscaled_height,
            num_frames=num_frames,
            denoise_strength=0.999,
            timesteps=[1000, 909, 725, 421, 0],
            latents=upscaled_latents,
            decode_timestep=0.05,
            decode_noise_scale=0.025,
            image_cond_noise_scale=0.0,
            guidance_scale=guidance_scale,
            guidance_rescale=0.7,
            generator=torch.Generator(device="cuda").manual_seed(seed),
            output_type="np",
            **pipeline_args
        ).frames[0]

        # Exportação para arquivo MP4
        video_uint8_frames = [(frame * 255).astype(np.uint8) for frame in final_video_frames_np]
        output_filename = "output.mp4"
        with imageio.get_writer(output_filename, fps=FPS, quality=8, macro_block_size=1) as writer:
             for frame_idx, frame_data in enumerate(video_uint8_frames):
                progress((frame_idx + 1) / len(video_uint8_frames), desc="Codificando frames do vídeo...")
                writer.append_data(frame_data)

        return output_filename, seed
        
    except Exception as e:
        print(f"Ocorreu um erro: {e}")
        return None, seed

# --- Interface Gráfica com Gradio ---
with gr.Blocks(theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Lexend Deca"), "sans-serif"]), delete_cache=(60, 900)) as demo:
    gr.Markdown("# Geração de Vídeo com LTX\n**Crie vídeos a partir de texto e imagens de condição.**")
    
    with gr.Row():
        with gr.Column(scale=1):
            prompt = gr.Textbox(label="Prompt", placeholder="Descreva o vídeo que você quer gerar...", lines=3, value="O Coringa dançando em um quarto escuro, iluminação dramática.")
            
            with gr.Accordion("Imagem de Condição 1", open=True):
                condition_image_1 = gr.Image(label="Imagem 1", type="pil")
                with gr.Row():
                    condition_strength_1 = gr.Slider(label="Peso", minimum=0.0, maximum=1.0, step=0.05, value=1.0)
                    condition_frame_index_1 = gr.Number(label="Frame", value=0, precision=0)

            with gr.Accordion("Imagem de Condição 2", open=False):
                condition_image_2 = gr.Image(label="Imagem 2", type="pil")
                with gr.Row():
                    condition_strength_2 = gr.Slider(label="Peso", minimum=0.0, maximum=1.0, step=0.05, value=1.0)
                    condition_frame_index_2 = gr.Number(label="Frame", value=0, precision=0)
            
            duration = gr.Slider(label="Duração (s)", minimum=1.0, maximum=10.0, step=0.5, value=2)
            
            with gr.Accordion("Configurações Avançadas", open=False):
                negative_prompt = gr.Textbox(label="Prompt Negativo", lines=2, value="pior qualidade, embaçado, tremido, distorcido")
                with gr.Row():
                    height = gr.Slider(label="Altura", minimum=256, maximum=1536, step=32, value=768)
                    width = gr.Slider(label="Largura", minimum=256, maximum=1536, step=32, value=1152)
                with gr.Row():
                    guidance_scale = gr.Slider(label="Guidance", minimum=1.0, maximum=5.0, step=0.1, value=1.0)
                    randomize_seed = gr.Checkbox(label="Seed Aleatória", value=True)
                    seed = gr.Number(label="Seed", value=0, precision=0)
            
            generate_btn = gr.Button("Gerar Vídeo", variant="primary", size="lg")
        
        with gr.Column(scale=1):          
            output_video = gr.Video(label="Vídeo Gerado", height=400)
            generated_seed = gr.Number(label="Seed Utilizada", interactive=False)
            
    generate_btn.click(
        fn=prepare_and_generate_video,
        inputs=[
            condition_image_1, condition_strength_1, condition_frame_index_1,
            condition_image_2, condition_strength_2, condition_frame_index_2,
            prompt, duration, negative_prompt,
            height, width, guidance_scale, seed, randomize_seed,
        ],
        outputs=[output_video, generated_seed]
    )

if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0", server_port=7860, debug=True, show_error=True)