api/ltx_server_refactored.py

# ltx_server_refactored.py — VideoService (Modular Version with Simple Overlap Chunking)
# Em api/ltx_server_refactored.py

import warnings
from huggingface_hub import logging
import os, subprocess, shlex, tempfile
import torch
import json
import numpy as np
import random
import os
import shlex
import yaml
from typing import List, Dict
from pathlib import Path
import imageio
from PIL import Image
import tempfile
from huggingface_hub import hf_hub_download
import sys
import subprocess
import gc
import shutil
import contextlib
import time
import traceback
from api.gpu_manager import gpu_manager
from einops import rearrange
import torch.nn.functional as F
from managers.vae_manager import vae_manager_singleton
from tools.video_encode_tool import video_encode_tool_singleton

DEPS_DIR = Path("/data")
LTX_VIDEO_REPO_DIR = DEPS_DIR / "LTX-Video"
logging.set_verbosity_error()
logging.set_verbosity_warning()
logging.set_verbosity_info()
logging.set_verbosity_debug()
LTXV_DEBUG=1
LTXV_FRAME_LOG_EVERY=8
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", message=".*")

# (Todas as funções de setup, helpers e inicialização da classe permanecem inalteradas)
# ... (run_setup, add_deps_to_path, _query_gpu_processes_via_nvml, etc.)
def run_setup():
    setup_script_path = "setup.py"
    if not os.path.exists(setup_script_path):
        print("[DEBUG] 'setup.py' não encontrado. Pulando clonagem de dependências.")
        return
    try:
        print("[DEBUG] Executando setup.py para dependências...")
        subprocess.run([sys.executable, setup_script_path], check=True)
        print("[DEBUG] Setup concluído com sucesso.")
    except subprocess.CalledProcessError as e:
        print(f"[DEBUG] ERRO no setup.py (code {e.returncode}). Abortando.")
        sys.exit(1)
if not LTX_VIDEO_REPO_DIR.exists():
    print(f"[DEBUG] Repositório não encontrado em {LTX_VIDEO_REPO_DIR}. Rodando setup...")
    run_setup()
def add_deps_to_path():
    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
    if str(LTX_VIDEO_REPO_DIR.resolve()) not in sys.path:
        sys.path.insert(0, repo_path)
        print(f"[DEBUG] Repo adicionado ao sys.path: {repo_path}")
def calculate_padding(orig_h, orig_w, target_h, target_w):
    pad_h = target_h - orig_h
    pad_w = target_w - orig_w
    pad_top = pad_h // 2
    pad_bottom = pad_h - pad_top
    pad_left = pad_w // 2
    pad_right = pad_w - pad_left
    return (pad_left, pad_right, pad_top, pad_bottom)
def log_tensor_info(tensor, name="Tensor"):
    if not isinstance(tensor, torch.Tensor):
        print(f"\n[INFO] '{name}' não é tensor.")
        return
    print(f"\n--- Tensor: {name} ---")
    print(f"  - Shape: {tuple(tensor.shape)}")
    print(f"  - Dtype: {tensor.dtype}")
    print(f"  - Device: {tensor.device}")
    if tensor.numel() > 0:
        try:
            print(f"  - Min: {tensor.min().item():.4f}  Max: {tensor.max().item():.4f}  Mean: {tensor.mean().item():.4f}")
        except Exception:
            pass
    print("------------------------------------------\n")

add_deps_to_path()
from ltx_video.pipelines.pipeline_ltx_video import ConditioningItem, LTXMultiScalePipeline
from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
from ltx_video.models.autoencoders.vae_encode import un_normalize_latents, normalize_latents
from ltx_video.pipelines.pipeline_ltx_video import adain_filter_latent
from api.ltx.inference import (
    create_ltx_video_pipeline,
    create_latent_upsampler,
    load_image_to_tensor_with_resize_and_crop,
    seed_everething,
)

class VideoService:
    def _load_config(self):
        base = LTX_VIDEO_REPO_DIR / "configs"
        config_path = base / "ltxv-13b-0.9.8-distilled-fp8.yaml"
        with open(config_path, "r") as file:
            return yaml.safe_load(file)

    def finalize(self, keep_paths=None, extra_paths=None, clear_gpu=True):
        print("[DEBUG] Finalize: iniciando limpeza...")
        keep = set(keep_paths or []); extras = set(extra_paths or [])
        gc.collect()
        try:
            if clear_gpu and torch.cuda.is_available():
                torch.cuda.empty_cache()
                try:
                    torch.cuda.ipc_collect()
                except Exception:
                    pass
        except Exception as e:
            print(f"[DEBUG] Finalize: limpeza GPU falhou: {e}")
            
    def _load_models(self):
        t0 = time.perf_counter()
        LTX_REPO = "Lightricks/LTX-Video"
        print("[DEBUG] Baixando checkpoint principal...")
        distilled_model_path = hf_hub_download(
            repo_id=LTX_REPO,
            filename=self.config["checkpoint_path"],
            local_dir=os.getenv("HF_HOME"),
            cache_dir=os.getenv("HF_HOME_CACHE"),
            token=os.getenv("HF_TOKEN"),
        )
        self.config["checkpoint_path"] = distilled_model_path
        print(f"[DEBUG] Checkpoint em: {distilled_model_path}")

        print("[DEBUG] Baixando upscaler espacial...")
        spatial_upscaler_path = hf_hub_download(
            repo_id=LTX_REPO,
            filename=self.config["spatial_upscaler_model_path"],
            local_dir=os.getenv("HF_HOME"),
            cache_dir=os.getenv("HF_HOME_CACHE"),
            token=os.getenv("HF_TOKEN")
        )
        self.config["spatial_upscaler_model_path"] = spatial_upscaler_path
        print(f"[DEBUG] Upscaler em: {spatial_upscaler_path}")

        print("[DEBUG] Construindo pipeline...")
        pipeline = create_ltx_video_pipeline(
            ckpt_path=self.config["checkpoint_path"],
            precision=self.config["precision"],
            text_encoder_model_name_or_path=self.config["text_encoder_model_name_or_path"],
            sampler=self.config["sampler"],
            device="cpu",
            enhance_prompt=False,
            prompt_enhancer_image_caption_model_name_or_path=self.config["prompt_enhancer_image_caption_model_name_or_path"],
            prompt_enhancer_llm_model_name_or_path=self.config["prompt_enhancer_llm_model_name_or_path"],
        )
        print("[DEBUG] Pipeline pronto.")

        latent_upsampler = None
        if self.config.get("spatial_upscaler_model_path"):
            print("[DEBUG] Construindo latent_upsampler...")
            latent_upsampler = create_latent_upsampler(self.config["spatial_upscaler_model_path"], device="cpu")
            print("[DEBUG] Upsampler pronto.")
        print(f"[DEBUG] _load_models() tempo total={time.perf_counter()-t0:.3f}s")
        return pipeline, latent_upsampler

    def _apply_precision_policy(self):
        prec = str(self.config.get("precision", "")).lower()
        self.runtime_autocast_dtype = torch.float32
        if prec in ["float8_e4m3fn", "bfloat16"]:
            self.runtime_autocast_dtype = torch.bfloat16
        elif prec == "mixed_precision":
            self.runtime_autocast_dtype = torch.float16

    def _register_tmp_dir(self, d: str):
        if d and os.path.isdir(d):
            self._tmp_dirs.add(d); print(f"[DEBUG] Registrado tmp dir: {d}")

    @torch.no_grad()
    def _upsample_latents_internal(self, latents: torch.Tensor) -> torch.Tensor:
        try:
            if not self.latent_upsampler:
                raise ValueError("Latent Upsampler não está carregado.")
            latents_unnormalized = un_normalize_latents(latents, self.pipeline.vae, vae_per_channel_normalize=True)
            upsampled_latents = self.latent_upsampler(latents_unnormalized)
            return normalize_latents(upsampled_latents, self.pipeline.vae, vae_per_channel_normalize=True)
        except Exception as e:
            pass
        finally:
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()
            self.finalize(keep_paths=[])

    def _prepare_conditioning_tensor(self, filepath, height, width, padding_values):
        tensor = load_image_to_tensor_with_resize_and_crop(filepath, height, width)
        tensor = torch.nn.functional.pad(tensor, padding_values)
        return tensor.to(self.device, dtype=self.runtime_autocast_dtype)

            
    def _save_and_log_video(self, pixel_tensor, base_filename, fps, temp_dir, results_dir, used_seed, progress_callback=None):
        output_path = os.path.join(temp_dir, f"{base_filename}_{used_seed}.mp4")
        video_encode_tool_singleton.save_video_from_tensor(
            pixel_tensor, output_path, fps=fps, progress_callback=progress_callback
        )
        final_path = os.path.join(results_dir, f"{base_filename}_{used_seed}.mp4")
        shutil.move(output_path, final_path)
        print(f"[DEBUG] Vídeo salvo em: {final_path}")
        return final_path

    # ==============================================================================
    # --- FUNÇÕES MODULARES COM A LÓGICA DE CHUNKING SIMPLIFICADA ---
    # ==============================================================================

    def prepare_condition_items(self, items_list: List, height: int, width: int, num_frames: int):
        if not items_list: return []
        height_padded = ((height - 1) // 8 + 1) * 8
        width_padded = ((width - 1) // 8 + 1) * 8
        padding_values = calculate_padding(height, width, height_padded, width_padded)
        conditioning_items = []
        for media, frame, weight in items_list:
            tensor = self._prepare_conditioning_tensor(media, height, width, padding_values) if isinstance(media, str) else media.to(self.device, dtype=self.runtime_autocast_dtype)
            safe_frame = max(0, min(int(frame), num_frames - 1))
            conditioning_items.append(ConditioningItem(tensor, safe_frame, float(weight)))
        return conditioning_items

    def generate_low(
        self, prompt, negative_prompt, 
        height, width, duration, seed, 
        conditions_itens, ltx_configs_override,
    ):
        guidance_scale="4"
        used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
        seed_everething(used_seed)
        FPS = 24.0
        actual_num_frames = max(9, int(round((round(duration * FPS) - 1) / 8.0) * 8 + 1))
        height_padded = ((height - 1) // 8 + 1) * 8
        width_padded = ((width - 1) // 8 + 1) * 8
        temp_dir = tempfile.mkdtemp(prefix="ltxv_low_"); self._register_tmp_dir(temp_dir)
        results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
        downscale_factor = self.config.get("downscale_factor", 0.6666666)
        vae_scale_factor = self.pipeline.vae_scale_factor
        x_width = int(width_padded * downscale_factor)
        downscaled_width = x_width - (x_width % vae_scale_factor)
        x_height = int(height_padded * downscale_factor)
        downscaled_height = x_height - (x_height % vae_scale_factor)
        first_pass_kwargs = {
            "prompt": prompt, "negative_prompt": negative_prompt, "height": downscaled_height, "width": downscaled_width,
            "num_frames": actual_num_frames, "frame_rate": int(FPS), "generator": torch.Generator(device=self.device).manual_seed(used_seed),
            "output_type": "latent", "conditioning_items": conditions_itens, 
            **(self.config.get("first_pass", {}))
        }
        try: 
            with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device == 'cuda'):
                latents = self.pipeline(**first_pass_kwargs).images
                pixel_tensor = vae_manager_singleton.decode(latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
                latents_cpu = latents.detach().to("cpu")
                tensor_path = os.path.join(results_dir, f"latents_low_res_{used_seed}.pt")
                torch.save(latents_cpu, tensor_path)
                del latents; gc.collect(); torch.cuda.empty_cache()
                pixel_tensor_cpu = pixel_tensor.detach().to("cpu")
                video_path = self._save_and_log_video(pixel_tensor_cpu, "low_res_video", FPS, temp_dir, results_dir, used_seed)
                del pixel_tensor; gc.collect(); torch.cuda.empty_cache()
              
            return video_path, tensor_path, seed
        
        except Exception as e:
            pass
        finally:
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()
            self.finalize(keep_paths=[])

    # ==============================================================================
    # --- FUNÇÕES DE GERAÇÃO ATUALIZADAS E MODULARES ---
    # ==============================================================================
     
    def generate_narrative_low(
        self, prompt: str, negative_prompt, 
        height, width, duration,
        seed, conditions_itens=None,
        ltx_configs_override: dict = None):
        
        print("\n" + "="*80)
        print("======           INICIANDO GERAÇÃO NARRATIVA EM CHUNKS (LOW-RES)           ======")
        print("="*80)

        prompt_list = [p.strip() for p in prompt.splitlines() if p.strip()]
        num_chunks = len(prompt_list)
        if num_chunks == 0: raise ValueError("O prompt está vazio ou não contém linhas válidas.")
        
        latentes_chunk_video = []
        latentes_chunk = None
        eco_latents_condition_overlap = None
        results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
        
        for i, prompt_x in enumerate(prompt_list):
            print(f"\n--- Gerando Chunk Narrativo {i+1}/{num_chunks}: '{prompt}' ---")

            current_image_conditions = []
            if conditions_itens:
                cond_item_original = conditions_itens[0]
                if i == 0:
                    current_image_conditions.append(cond_item_original)
                else:
                    cond_item_fraco = ConditioningItem(
                        media_item=cond_item_original.media_item, media_frame_number=0, conditioning_strength=0.1
                    )
                    current_image_conditions.append(cond_item_fraco)
            
            if ltx_configs_override is None: ltx_configs_override = {}
            
            eco_current_conditions_list = []
            if current_image_conditions: eco_current_conditions_list.extend(current_image_conditions)
            #if eco_latents_condition_overlap: eco_current_conditions_list.append(eco_latents_condition_overlap)
            #ltx_configs_override["conditioning_items"] = current_conditions
            
            video_path, tensor_path, final_seed = generate_low(
                prompt=prompt_x, negative_prompt=negative_prompt, height=height, 
                width=width, duration=duration, seed=seed, 
                conditions_itens=conditions_itens, ltx_configs_override=ltx_configs_override,
            )

            #log_tensor_info(latentes_chunk, f"latentes_chunk")
        
      
            #eco_latents_overlap = latentes_chunk[:, :, -4:, :, :]
            #log_tensor_info(eco_latents_overlap, f"eco_latents_overlap")
        
            #eco_latents_condition_overlap = ConditioningItem(
            #    media_item=eco_latents_overlap, media_frame_number=0, conditioning_strength=1.0
            #)

            #if i > 0:
                #latentes_chunk_podado = latentes_chunk[:, :, 5:, :, :]
                #latentes_chunk_video_list.append(latentes_chunk_podado)
                #log_tensor_info(latentes_chunk_podado, "latentes_chunk_podado")
            #else:
            latentes_chunk_video_list.append(tensor_path)
            log_tensor_info(tensor_path, "tensor_path")
          
        #final_latents_cpu = torch.cat(latentes_chunk_video_list, dim=2).cpu()
        #log_tensor_info(final_latents_cpu, "Tensor de Latentes Final Concatenado (CPU)")
        
        #tensor_path = os.path.join(results_dir, f"latents_narrative_{used_seed}.pt")
        #torch.save(final_latents_cpu, tensor_path)
        
        try: 
            with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device.type == 'cuda'):
                final_latents_video = torch.cat(latentes_chunk_video_list, dim=2)
                pixel_tensor = vae_manager_singleton.decode(final_latents_video, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
                video_path = self._save_and_log_video(pixel_tensor, "narrative_video", FPS, temp_dir, results_dir, used_seed)
                tensor_path = os.path.join(results_dir, f"latents_narrative_{used_seed}.pt")
                torch.save(final_latents_video, tensor_path)
                del final_latents_video; gc.collect(); torch.cuda.empty_cache()
                del pixel_tensor; gc.collect(); torch.cuda.empty_cache()
            return video_path, tensor_path, used_seed
        except Exception as e:
            print("-" * 20 + f" ERRO: generate_narrative_low {e} " + "-"*20)
            traceback.print_exc()
            return None
        finally:
            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()
            self.finalize(keep_paths=[])

            
        
    # ==============================================================================
    # --- FUNÇÃO #4: ORQUESTRADOR  (Upscaler + texturas hd) ---
    # ==============================================================================
    def generate_upscale_denoise(
        self, latents_path, prompt, negative_prompt, seed
    ):
            used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
            seed_everething(used_seed)
            temp_dir = tempfile.mkdtemp(prefix="ltxv_up_"); self._register_tmp_dir(temp_dir)
            results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
            latents_low = torch.load(latents_path).to(self.device)
            with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device == 'cuda'):
                upsampled_latents = self._upsample_latents_internal(latents_low)
                upsampled_latents = adain_filter_latent(latents=upsampled_latents, reference_latents=latents_low)
                del latents_low; torch.cuda.empty_cache()
                
                # --- LÓGICA DE DIVISÃO SIMPLES COM OVERLAP ---
                total_frames = upsampled_latents.shape[2]
                # Garante que mid_point seja pelo menos 1 para evitar um segundo chunk vazio se houver poucos frames
                mid_point = max(1, total_frames // 2) 
                chunk1 = upsampled_latents[:, :, :mid_point, :, :]
                # O segundo chunk começa um frame antes para criar o overlap
                chunk2 = upsampled_latents[:, :, mid_point - 1:, :, :]
                
                final_latents_list = []
                for i, chunk in enumerate([chunk1, chunk2]):
                    if chunk.shape[2] <= 1: continue # Pula chunks inválidos ou vazios
                    second_pass_height = chunk.shape[3] * self.pipeline.vae_scale_factor
                    second_pass_width = chunk.shape[4] * self.pipeline.vae_scale_factor
                    second_pass_kwargs = {
                        "prompt": prompt, "negative_prompt": negative_prompt, "height": second_pass_height, "width": second_pass_width,
                        "num_frames": chunk.shape[2], "latents": chunk, 
                        #"guidance_scale": float(guidance_scale),
                        "output_type": "latent", "generator": torch.Generator(device=self.device).manual_seed(used_seed),
                        **(self.config.get("second_pass", {}))
                    }
                    refined_chunk = self.pipeline(**second_pass_kwargs).images
                    # Remove o overlap do primeiro chunk refinado antes de juntar
                    if i == 0:
                        final_latents_list.append(refined_chunk[:, :, :-1, :, :])
                    else:
                        final_latents_list.append(refined_chunk)
    
            final_latents = torch.cat(final_latents_list, dim=2)
            log_tensor_info(final_latents, "Latentes Upscaled/Refinados Finais")
    
            latents_cpu = final_latents.detach().to("cpu")
            tensor_path = os.path.join(results_dir, f"latents_refined_{used_seed}.pt")
            torch.save(latents_cpu, tensor_path)
            pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
            video_path = self._save_and_log_video(pixel_tensor, "refined_video", 24.0, temp_dir, results_dir, used_seed)
            return video_path, tensor_path    

    def encode_mp4(self, latents_path: str, fps: int = 24):
        latents = torch.load(latents_path)
        seed = random.randint(0, 99999)
        temp_dir = tempfile.mkdtemp(prefix="ltxv_enc_"); self._register_tmp_dir(temp_dir)
        results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
        
        # --- LÓGICA DE DIVISÃO SIMPLES COM OVERLAP ---
        total_frames = latents.shape[2]
        mid_point = max(1, total_frames // 2)
        chunk1_latents = latents[:, :, :mid_point, :, :]
        chunk2_latents = latents[:, :, mid_point - 1:, :, :]
        
        video_parts = []
        pixel_chunks_to_concat = []
        with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device == 'cuda'):
            for i, chunk in enumerate([chunk1_latents, chunk2_latents]):
                if chunk.shape[2] == 0: continue
                pixel_chunk = vae_manager_singleton.decode(chunk.to(self.device), decode_timestep=float(self.config.get("decode_timestep", 0.05)))
                # Remove o overlap do primeiro chunk de pixels
                if i == 0:
                    pixel_chunks_to_concat.append(pixel_chunk[:, :, :-1, :, :])
                else:
                    pixel_chunks_to_concat.append(pixel_chunk)
        
        final_pixel_tensor = torch.cat(pixel_chunks_to_concat, dim=2)
        final_video_path = self._save_and_log_video(final_pixel_tensor, f"final_concatenated_{seed}", fps, temp_dir, results_dir, seed)
        return final_video_path

    def __init__(self):
        t0 = time.perf_counter()
        print("[DEBUG] Inicializando VideoService...")
        
        # 1. Obter o dispositivo alvo a partir do gerenciador
        #    Não definimos `self.device` ainda, apenas guardamos o alvo.
        target_device = gpu_manager.get_ltx_device()
        print(f"[DEBUG] LTX foi alocado para o dispositivo: {target_device}")

        # 2. Carregar a configuração e os modelos (na CPU, como a função _load_models faz)
        self.config = self._load_config()
        self.pipeline, self.latent_upsampler = self._load_models()

        # 3. Mover os modelos para o dispositivo alvo e definir `self.device`
        self.move_to_device(target_device) # Usando a função que já criamos!

        # 4. Configurar o resto dos componentes com o dispositivo correto
        self._apply_precision_policy()
        vae_manager_singleton.attach_pipeline(
            self.pipeline,
            device=self.device, # Agora `self.device` está correto
            autocast_dtype=self.runtime_autocast_dtype
        )
        self._tmp_dirs = set()
        print(f"[DEBUG] VideoService pronto. boot_time={time.perf_counter()-t0:.3f}s")
    
    def move_to_device(self, device):
        """Move os modelos do pipeline para o dispositivo especificado."""
        print(f"[LTX] Movendo modelos para {device}...")
        self.device = torch.device(device) # Garante que é um objeto torch.device
        self.pipeline.to(self.device)
        if self.latent_upsampler:
            self.latent_upsampler.to(self.device)
        print(f"[LTX] Modelos agora estão em {self.device}.")
        
    def move_to_cpu(self):
        """Move os modelos para a CPU para liberar VRAM."""
        self.move_to_device(torch.device("cpu"))
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

print("Criando instância do VideoService...")
video_generation_service = VideoService()
print("Instância do VideoService pronta.")