api/ltx_server_refactored_complete.py

# FILE: api/ltx_server_refactored_complete.py
# DESCRIPTION: Final orchestrator for LTX-Video generation.
# Features path resolution for cached models, dedicated VAE device logic,
# delegation to utility modules, and advanced debug logging.

import gc
import json
import logging
import os
import shutil
import sys
import tempfile
import time
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import random
import torch
import yaml
import numpy as np
from huggingface_hub import hf_hub_download

# ==============================================================================
# --- SETUP E IMPORTAÇÕES DO PROJETO ---
# ==============================================================================

# Configuração de logging e supressão de warnings
# (Pode ser removido se o logging for configurado globalmente)
import warnings
warnings.filterwarnings("ignore")
logging.getLogger("huggingface_hub").setLevel(logging.ERROR)
log_level = os.environ.get("ADUC_LOG_LEVEL", "INFO").upper()
logging.basicConfig(level=log_level, format='[%(levelname)s] [%(name)s] %(message)s')

# --- Constantes de Configuração ---
DEPS_DIR = Path("/data")
LTX_VIDEO_REPO_DIR = DEPS_DIR / "LTX-Video"
RESULTS_DIR = Path("/app/output")
DEFAULT_FPS = 24.0
FRAMES_ALIGNMENT = 8
LTX_REPO_ID = "Lightricks/LTX-Video" # Repositório de onde os modelos são baixados

# Garante que a biblioteca LTX-Video seja importável
def add_deps_to_path():
    repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
    if repo_path not in sys.path:
        sys.path.insert(0, repo_path)
        logging.info(f"[ltx_server] LTX-Video repository added to sys.path: {repo_path}")

add_deps_to_path()

# --- Módulos da nossa Arquitetura ---
try:
    from api.gpu_manager import gpu_manager
    from managers.vae_manager import vae_manager_singleton
    from tools.video_encode_tool import video_encode_tool_singleton
    from api.ltx.ltx_utils import (
        build_ltx_pipeline_on_cpu,
        seed_everything,
        load_image_to_tensor_with_resize_and_crop,
        ConditioningItem,
    )
    from api.utils.debug_utils import log_function_io
except ImportError as e:
    logging.critical(f"A crucial import from the local API/architecture failed. Error: {e}", exc_info=True)
    sys.exit(1)

# ==============================================================================
# --- FUNÇÕES AUXILIARES DO ORQUESTRADOR ---
# ==============================================================================

@log_function_io
def calculate_padding(orig_h: int, orig_w: int, target_h: int, target_w: int) -> Tuple[int, int, int, int]:
    """Calculates symmetric padding required to meet target dimensions."""
    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)

# ==============================================================================
# --- CLASSE DE SERVIÇO (O ORQUESTRADOR) ---
# ==============================================================================

class VideoService:
    """
    Orchestrates the high-level logic of video generation, delegating low-level
    tasks to specialized managers and utility modules.
    """

    @log_function_io
    def __init__(self):
        t0 = time.perf_counter()
        logging.info("Initializing VideoService Orchestrator...")
        RESULTS_DIR.mkdir(parents=True, exist_ok=True)

        target_main_device_str = str(gpu_manager.get_ltx_device())
        target_vae_device_str = str(gpu_manager.get_ltx_vae_device())
        logging.info(f"LTX allocated to devices: Main='{target_main_device_str}', VAE='{target_vae_device_str}'")

        self.config = self._load_config()
        self._resolve_model_paths_from_cache() # Etapa crítica para encontrar os modelos

        self.pipeline, self.latent_upsampler = build_ltx_pipeline_on_cpu(self.config)

        self.main_device = torch.device("cpu")
        self.vae_device = torch.device("cpu")
        self.move_to_device(main_device_str=target_main_device_str, vae_device_str=target_vae_device_str)

        self._apply_precision_policy()
        vae_manager_singleton.attach_pipeline(self.pipeline, device=self.vae_device, autocast_dtype=self.runtime_autocast_dtype)
        logging.info(f"VideoService ready. Startup time: {time.perf_counter()-t0:.2f}s")

    def _load_config(self) -> Dict:
        """Loads the YAML configuration file."""
        config_path = LTX_VIDEO_REPO_DIR / "configs" / "ltxv-13b-0.9.8-distilled-fp8.yaml"
        logging.info(f"Loading config from: {config_path}")
        with open(config_path, "r") as file:
            return yaml.safe_load(file)

    def _resolve_model_paths_from_cache(self):
        """
        Uses hf_hub_download to find the absolute paths to model files in the cache,
        updating the in-memory config. This makes the app resilient to cache structure.
        """
        logging.info("Resolving model paths from Hugging Face cache...")
        cache_dir = os.environ.get("HF_HOME")
        try:
            # Resolve o caminho do checkpoint principal
            main_ckpt_filename = self.config["checkpoint_path"]
            main_ckpt_path = hf_hub_download(
                repo_id=LTX_REPO_ID,
                filename=main_ckpt_filename,
                cache_dir=cache_dir
            )
            self.config["checkpoint_path"] = main_ckpt_path
            logging.info(f"  -> Main checkpoint resolved to: {main_ckpt_path}")

            # Resolve o caminho do upsampler, se existir
            if self.config.get("spatial_upscaler_model_path"):
                upscaler_filename = self.config["spatial_upscaler_model_path"]
                upscaler_path = hf_hub_download(
                    repo_id=LTX_REPO_ID,
                    filename=upscaler_filename,
                    cache_dir=cache_dir
                )
                self.config["spatial_upscaler_model_path"] = upscaler_path
                logging.info(f"  -> Spatial upscaler resolved to: {upscaler_path}")
        except Exception as e:
            logging.critical(f"Failed to resolve model paths. Ensure setup.py ran correctly. Error: {e}", exc_info=True)
            sys.exit(1)

    @log_function_io
    def move_to_device(self, main_device_str: str, vae_device_str: str):
        """Moves pipeline components to their designated target devices."""
        target_main_device = torch.device(main_device_str)
        target_vae_device = torch.device(vae_device_str)
        logging.info(f"Moving LTX models -> Main Pipeline: {target_main_device}, VAE: {target_vae_device}")

        self.main_device = target_main_device
        self.pipeline.to(self.main_device)
        self.vae_device = target_vae_device
        self.pipeline.vae.to(self.vae_device)
        if self.latent_upsampler: self.latent_upsampler.to(self.main_device)
        logging.info("LTX models successfully moved to target devices.")

    def move_to_cpu(self):
        """Moves all LTX components to CPU to free VRAM for other services."""
        self.move_to_device(main_device_str="cpu", vae_device_str="cpu")
        if torch.cuda.is_available(): torch.cuda.empty_cache()

    def finalize(self):
        """Cleans up GPU memory after a generation task."""
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            try: torch.cuda.ipc_collect();
            except Exception: pass

    # ==========================================================================
    # --- LÓGICA DE NEGÓCIO: ORQUESTRADORES PÚBLICOS ---
    # ==========================================================================

    @log_function_io
    def generate_narrative_low(self, prompt: str, **kwargs) -> Tuple[Optional[str], Optional[str], Optional[int]]:
        """Orchestrates the generation of a video from a multi-line prompt (sequence of scenes)."""
        logging.info("Starting narrative low-res generation...")
        used_seed = self._resolve_seed(kwargs.get("seed"))
        seed_everything(used_seed)

        prompt_list = [p.strip() for p in prompt.splitlines() if p.strip()]
        if not prompt_list: raise ValueError("Prompt is empty or contains no valid lines.")

        num_chunks = len(prompt_list)
        total_frames = self._calculate_aligned_frames(kwargs.get("duration", 4.0))
        frames_per_chunk = (total_frames // num_chunks // FRAMES_ALIGNMENT) * FRAMES_ALIGNMENT
        overlap_frames = self.config.get("overlap_frames", 8)
        
        temp_latent_paths = []
        overlap_condition_item = None
        
        try:
            for i, chunk_prompt in enumerate(prompt_list):
                logging.info(f"Generating narrative chunk {i+1}/{num_chunks}: '{chunk_prompt[:50]}...'")
                current_frames = frames_per_chunk + (overlap_frames if i > 0 else 0)
                current_conditions = kwargs.get("initial_conditions", []) if i == 0 else []
                if overlap_condition_item: current_conditions.append(overlap_condition_item)

                chunk_latents = self._generate_single_chunk_low(
                    prompt=chunk_prompt, num_frames=current_frames, seed=used_seed + i,
                    conditioning_items=current_conditions, **kwargs
                )
                if chunk_latents is None: raise RuntimeError(f"Failed to generate latents for chunk {i+1}.")

                if i < num_chunks - 1:
                    overlap_latents = chunk_latents[:, :, -overlap_frames:, :, :].clone()
                    overlap_condition_item = ConditioningItem(media_item=overlap_latents, media_frame_number=0, conditioning_strength=1.0)
                
                if i > 0: chunk_latents = chunk_latents[:, :, overlap_frames:, :, :]
                
                chunk_path = RESULTS_DIR / f"temp_chunk_{i}_{used_seed}.pt"
                torch.save(chunk_latents.cpu(), chunk_path)
                temp_latent_paths.append(chunk_path)
            
            return self._finalize_generation(temp_latent_paths, "narrative_video", used_seed)
        except Exception as e:
            logging.error(f"Error during narrative generation: {e}", exc_info=True)
            return None, None, None
        finally:
            for path in temp_latent_paths:
                if path.exists(): path.unlink()
            self.finalize()

    @log_function_io
    def generate_single_low(self, **kwargs) -> Tuple[Optional[str], Optional[str], Optional[int]]:
        """Orchestrates the generation of a video from a single prompt in one go."""
        logging.info("Starting single-prompt low-res generation...")
        used_seed = self._resolve_seed(kwargs.get("seed"))
        seed_everything(used_seed)
        
        try:
            total_frames = self._calculate_aligned_frames(kwargs.get("duration", 4.0), min_frames=9)
            final_latents = self._generate_single_chunk_low(
                num_frames=total_frames, seed=used_seed,
                conditioning_items=kwargs.get("initial_conditions", []), **kwargs
            )
            if final_latents is None: raise RuntimeError("Failed to generate latents.")
            
            temp_latent_path = RESULTS_DIR / f"temp_single_{used_seed}.pt"
            torch.save(final_latents.cpu(), temp_latent_path)
            return self._finalize_generation([temp_latent_path], "single_video", used_seed)
        except Exception as e:
            logging.error(f"Error during single generation: {e}", exc_info=True)
            return None, None, None
        finally:
            self.finalize()

    # ==========================================================================
    # --- UNIDADES DE TRABALHO E HELPERS INTERNOS ---
    # ==========================================================================

    @log_function_io
    def _generate_single_chunk_low(self, **kwargs) -> Optional[torch.Tensor]:
        """Calls the pipeline to generate a single chunk of latents."""
        height_padded, width_padded = (self._align(d) for d in (kwargs['height'], kwargs['width']))
        downscale_factor = self.config.get("downscale_factor", 0.6666666)
        vae_scale_factor = self.pipeline.vae_scale_factor
        downscaled_height = self._align(int(height_padded * downscale_factor), vae_scale_factor)
        downscaled_width = self._align(int(width_padded * downscale_factor), vae_scale_factor)

        first_pass_config = self.config.get("first_pass", {}).copy()
        if kwargs.get("ltx_configs_override"):
            first_pass_config.update(self._prepare_guidance_overrides(kwargs["ltx_configs_override"]))

        pipeline_kwargs = {
            "prompt": kwargs['prompt'], "negative_prompt": kwargs['negative_prompt'],
            "height": downscaled_height, "width": downscaled_width, "num_frames": kwargs['num_frames'],
            "frame_rate": DEFAULT_FPS, "generator": torch.Generator(device=self.main_device).manual_seed(kwargs['seed']),
            "output_type": "latent", "conditioning_items": kwargs['conditioning_items'], **first_pass_config
        }
        
        with torch.autocast(device_type=self.main_device.type, dtype=self.runtime_autocast_dtype, enabled="cuda" in self.main_device.type):
            latents_raw = self.pipeline(**pipeline_kwargs).images
        
        return latents_raw.to(self.main_device)

    @log_function_io
    def _finalize_generation(self, temp_latent_paths: List[Path], base_filename: str, seed: int) -> Tuple[str, str, int]:
        """Consolidates latents, decodes them to video, and saves final artifacts."""
        logging.info("Finalizing generation: decoding latents to video.")
        all_tensors_cpu = [torch.load(p) for p in temp_latent_paths]
        final_latents = torch.cat(all_tensors_cpu, dim=2)
        
        final_latents_path = RESULTS_DIR / f"latents_{base_filename}_{seed}.pt"
        torch.save(final_latents, final_latents_path)
        logging.info(f"Final latents saved to: {final_latents_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, f"{base_filename}_{seed}")
        return str(video_path), str(final_latents_path), seed

    @log_function_io
    def prepare_condition_items(self, items_list: List, height: int, width: int, num_frames: int) -> List[ConditioningItem]:
        if not items_list: return []
        height_padded, width_padded = self._align(height), self._align(width)
        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)
            safe_frame = max(0, min(int(frame), num_frames - 1))
            conditioning_items.append(ConditioningItem(tensor, safe_frame, float(weight)))
        return conditioning_items

    @log_function_io
    def _prepare_conditioning_tensor(self, media_path: str, height: int, width: int, padding: Tuple) -> torch.Tensor:
        tensor = load_image_to_tensor_with_resize_and_crop(media_path, height, width)
        tensor = torch.nn.functional.pad(tensor, padding)
        return tensor.to(self.main_device, dtype=self.runtime_autocast_dtype)

    def _prepare_guidance_overrides(self, ltx_configs: Dict) -> Dict:
        overrides = {}
        preset = ltx_configs.get("guidance_preset", "Padrão (Recomendado)")
        if preset == "Agressivo":
            overrides["guidance_scale"] = [1, 2, 8, 12, 8, 2, 1]
            overrides["stg_scale"] = [0, 0, 5, 6, 5, 3, 2]
        elif preset == "Suave":
            overrides["guidance_scale"] = [1, 1, 4, 5, 4, 1, 1]
            overrides["stg_scale"] = [0, 0, 2, 2, 2, 1, 0]
        elif preset == "Customizado":
            try:
                overrides["guidance_scale"] = json.loads(ltx_configs["guidance_scale_list"])
                overrides["stg_scale"] = json.loads(ltx_configs["stg_scale_list"])
            except (json.JSONDecodeError, KeyError) as e:
                logging.warning(f"Failed to parse custom guidance values: {e}. Falling back to defaults.")
        if overrides: logging.info(f"Applying '{preset}' guidance preset overrides.")
        return overrides

    def _save_and_log_video(self, pixel_tensor: torch.Tensor, base_filename: str) -> Path:
        with tempfile.TemporaryDirectory() as temp_dir:
            temp_path = os.path.join(temp_dir, f"{base_filename}.mp4")
            video_encode_tool_singleton.save_video_from_tensor(pixel_tensor, temp_path, fps=DEFAULT_FPS)
            final_path = RESULTS_DIR / f"{base_filename}.mp4"
            shutil.move(temp_path, final_path)
            logging.info(f"Video saved successfully to: {final_path}")
            return final_path
    
    def _apply_precision_policy(self):
        precision = str(self.config.get("precision", "bfloat16")).lower()
        if precision in ["float8_e4m3fn", "bfloat16"]: self.runtime_autocast_dtype = torch.bfloat16
        elif precision == "mixed_precision": self.runtime_autocast_dtype = torch.float16
        else: self.runtime_autocast_dtype = torch.float32
        logging.info(f"Runtime precision policy set for autocast: {self.runtime_autocast_dtype}")

    def _align(self, dim: int, alignment: int = FRAMES_ALIGNMENT) -> int:
        return ((dim - 1) // alignment + 1) * alignment
    
    def _calculate_aligned_frames(self, duration_s: float, min_frames: int = 1) -> int:
        num_frames = int(round(duration_s * DEFAULT_FPS))
        aligned_frames = self._align(num_frames)
        return max(aligned_frames + 1, min_frames)

    def _resolve_seed(self, seed: Optional[int]) -> int:
        return random.randint(0, 2**32 - 1) if seed is None else int(seed)

# ==============================================================================
# --- INSTANCIAÇÃO SINGLETON ---
# ==============================================================================
try:
    video_generation_service = VideoService()
    logging.info("Global VideoService orchestrator instance created successfully.")
except Exception as e:
    logging.critical(f"Failed to initialize VideoService: {e}", exc_info=True)
    sys.exit(1)