aduc_framework/managers/ltx_manager.py

# aduc_framework/managers/ltx_manager.py
#
# Copyright (C) August 4, 2025  Carlos Rodrigues dos Santos
#
# Versão 2.3.2 (Com correção de manipulação de dataclass)
#
# Este manager é responsável por controlar a pipeline LTX-Video. Ele gerencia
# um pool de workers para otimizar o uso de múltiplas GPUs, lida com a inicialização
# e o setup de dependências complexas, e expõe uma interface de alto nível para a
# geração de fragmentos de vídeo no espaço latente.

import torch
import gc
import os
import sys
import yaml
import logging
import huggingface_hub
import time
import threading
import subprocess
from pathlib import Path
from typing import Optional, List, Tuple, Union

# --- Imports Relativos Corrigidos ---
from ..types import LatentConditioningItem
from ..tools.optimization import optimize_ltx_worker, can_optimize_fp8
from ..tools.hardware_manager import hardware_manager

logger = logging.getLogger(__name__)

# --- Gerenciamento de Dependências e Placeholders ---
DEPS_DIR = Path("./deps")
LTX_VIDEO_REPO_DIR = DEPS_DIR / "LTX-Video"
LTX_VIDEO_REPO_URL = "https://github.com/Lightricks/LTX-Video.git"

# Placeholders para módulos importados tardiamente (lazy-loaded)
create_ltx_video_pipeline = None
calculate_padding = None
LTXVideoPipeline = None
ConditioningItem = None
LTXMultiScalePipeline = None
vae_encode = None
latent_to_pixel_coords = None
randn_tensor = None

class LtxPoolManager:
    """
    Gerencia um pool de LtxWorkers e expõe a pipeline de aprimoramento de prompt.
    """
    def __init__(self, device_ids: List[str], ltx_config_file_name: str):
        logger.info(f"LTX POOL MANAGER: Criando workers para os dispositivos: {device_ids}")
        self._ltx_modules_loaded = False
        self._setup_dependencies()
        self._lazy_load_ltx_modules()

        self.ltx_config_file = LTX_VIDEO_REPO_DIR / "configs" / ltx_config_file_name

        self.workers = [LtxWorker(dev_id, self.ltx_config_file) for dev_id in device_ids]
        self.current_worker_index = 0
        self.lock = threading.Lock()

        self.prompt_enhancement_pipeline = self.workers[0].pipeline if self.workers else None
        if self.prompt_enhancement_pipeline:
            logger.info("LTX POOL MANAGER: Pipeline de aprimoramento de prompt exposta para outros especialistas.")

        self._apply_ltx_pipeline_patches()

        if all(w.device.type == 'cuda' for w in self.workers):
            logger.info("LTX POOL MANAGER: MODO HOT START ATIVADO. Pré-aquecendo todas as GPUs...")
            for worker in self.workers:
                worker.to_gpu()
            logger.info("LTX POOL MANAGER: Todas as GPUs estão prontas.")
        else:
            logger.info("LTX POOL MANAGER: Operando em modo CPU ou misto. Pré-aquecimento de GPU pulado.")

    def _setup_dependencies(self):
        """Clona o repositório LTX-Video se não encontrado e o adiciona ao sys.path."""
        if not LTX_VIDEO_REPO_DIR.exists():
            logger.info(f"Repositório LTX-Video não encontrado em '{LTX_VIDEO_REPO_DIR}'. Clonando do GitHub...")
            try:
                DEPS_DIR.mkdir(exist_ok=True)
                subprocess.run(
                    ["git", "clone", "--depth", "1", LTX_VIDEO_REPO_URL, str(LTX_VIDEO_REPO_DIR)],
                    check=True, capture_output=True, text=True
                )
                logger.info("Repositório LTX-Video clonado com sucesso.")
            except subprocess.CalledProcessError as e:
                logger.error(f"Falha ao clonar o repositório LTX-Video. Git stderr: {e.stderr}")
                raise RuntimeError("Não foi possível clonar a dependência LTX-Video do GitHub.")
        else:
            logger.info("Repositório LTX-Video local encontrado.")

        if str(LTX_VIDEO_REPO_DIR.resolve()) not in sys.path:
            sys.path.insert(0, str(LTX_VIDEO_REPO_DIR.resolve()))
            logger.info(f"Adicionado '{LTX_VIDEO_REPO_DIR.resolve()}' ao sys.path.")

    def _lazy_load_ltx_modules(self):
        """Importa dinamicamente os módulos do LTX-Video após garantir que o repositório existe."""
        if self._ltx_modules_loaded:
            return

        global create_ltx_video_pipeline, calculate_padding, LTXVideoPipeline, ConditioningItem, LTXMultiScalePipeline
        global vae_encode, latent_to_pixel_coords, randn_tensor
        
        from .ltx_pipeline_utils import create_ltx_video_pipeline, calculate_padding
        from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline, ConditioningItem, LTXMultiScalePipeline
        from ltx_video.models.autoencoders.vae_encode import vae_encode, latent_to_pixel_coords
        from diffusers.utils.torch_utils import randn_tensor
        
        self._ltx_modules_loaded = True
        logger.info("Módulos do LTX-Video foram carregados dinamicamente.")

    def _apply_ltx_pipeline_patches(self):
        """Aplica patches em tempo de execução na pipeline LTX para compatibilidade com ADUC-SDR."""
        logger.info("LTX POOL MANAGER: Aplicando patches ADUC-SDR na pipeline LTX...")
        for worker in self.workers:
            worker.pipeline.prepare_conditioning = _aduc_prepare_conditioning_patch.__get__(worker.pipeline, LTXVideoPipeline)
        logger.info("LTX POOL MANAGER: Todas as instâncias da pipeline foram corrigidas com sucesso.")

    def _get_next_worker(self) -> 'LtxWorker':
        with self.lock:
            worker = self.workers[self.current_worker_index]
            self.current_worker_index = (self.current_worker_index + 1) % len(self.workers)
            return worker
    
    def _prepare_pipeline_params(self, worker: 'LtxWorker', **kwargs) -> dict:
        pipeline_params = {
            "height": kwargs['height'], "width": kwargs['width'], "num_frames": kwargs['video_total_frames'],
            "frame_rate": kwargs.get('video_fps', 24),
            "generator": torch.Generator(device=worker.device).manual_seed(int(time.time()) + kwargs.get('current_fragment_index', 0)),
            "is_video": True, "vae_per_channel_normalize": True,
            "prompt": kwargs.get('motion_prompt', ""), "negative_prompt": kwargs.get('negative_prompt', "blurry, distorted, static, bad quality"),
            "guidance_scale": kwargs.get('guidance_scale', 1.0), "stg_scale": kwargs.get('stg_scale', 0.0),
            "rescaling_scale": kwargs.get('rescaling_scale', 0.15), "num_inference_steps": kwargs.get('num_inference_steps', 20),
            "output_type": "latent"
        }
        if 'latents' in kwargs:
            pipeline_params["latents"] = kwargs['latents'].to(worker.device, dtype=worker.pipeline.transformer.dtype)
        if 'strength' in kwargs:
            pipeline_params["strength"] = kwargs['strength']
        
        if 'conditioning_items_data' in kwargs:
            final_conditioning_items = []
            for item in kwargs['conditioning_items_data']:
                # CORREÇÃO: Como LatentConditioningItem é uma dataclass mutável,
                # nós modificamos o atributo diretamente no dispositivo do worker.
                item.latent_tensor = item.latent_tensor.to(worker.device)
                final_conditioning_items.append(item)
            pipeline_params["conditioning_items"] = final_conditioning_items

        if worker.is_distilled:
            fixed_timesteps = worker.config.get("first_pass", {}).get("timesteps")
            if fixed_timesteps:
                pipeline_params["timesteps"] = fixed_timesteps
                pipeline_params["num_inference_steps"] = len(fixed_timesteps)
        
        callback = kwargs.get('callback')
        if callback:
            pipeline_params["callback_on_step_end"] = callback
            pipeline_params["callback_on_step_end_tensor_inputs"] = ["latents"]
        
        return pipeline_params

    def generate_latent_fragment(self, **kwargs) -> Tuple[torch.Tensor, tuple]:
        worker_to_use = self._get_next_worker()
        try:
            height, width = kwargs['height'], kwargs['width']
            padded_h, padded_w = ((height - 1) // 32 + 1) * 32, ((width - 1) // 32 + 1) * 32
            padding_vals = calculate_padding(height, width, padded_h, padded_w)
            kwargs['height'], kwargs['width'] = padded_h, padded_w
            
            pipeline_params = self._prepare_pipeline_params(worker_to_use, **kwargs)
            
            logger.info(f"Iniciando GERAÇÃO em {worker_to_use.device} com shape {padded_w}x{padded_h}")
            
            if isinstance(worker_to_use.pipeline, LTXMultiScalePipeline):
                result = worker_to_use.pipeline.video_pipeline(**pipeline_params).images
            else:
                result = worker_to_use.generate_video_fragment_internal(**pipeline_params)
            return result, padding_vals
        except Exception as e:
            logger.error(f"LTX POOL MANAGER: Erro durante a geração em {worker_to_use.device}: {e}", exc_info=True)
            raise e
        finally:
            if worker_to_use and worker_to_use.device.type == 'cuda':
                with torch.cuda.device(worker_to_use.device):
                    gc.collect()
                    torch.cuda.empty_cache()

    def refine_latents(self, latents_to_refine: torch.Tensor, **kwargs) -> Tuple[torch.Tensor, tuple]:
        pass # Placeholder

class LtxWorker:
    """Representa uma única instância da pipeline LTX-Video em um dispositivo específico."""
    def __init__(self, device_id, ltx_config_file):
        self.cpu_device = torch.device('cpu')
        self.device = torch.device(device_id if torch.cuda.is_available() else 'cpu')
        logger.info(f"LTX Worker ({self.device}): Inicializando com config '{ltx_config_file}'...")
        
        with open(ltx_config_file, "r") as file:
            self.config = yaml.safe_load(file)
        
        self.is_distilled = "distilled" in self.config.get("checkpoint_path", "")
        models_dir = LTX_VIDEO_REPO_DIR / "models_downloaded"
        
        logger.info(f"LTX Worker ({self.device}): Preparando para carregar modelo...")
        model_filename = self.config["checkpoint_path"]
        model_path = huggingface_hub.hf_hub_download(
            repo_id="Lightricks/LTX-Video", filename=model_filename,
            local_dir=str(models_dir), local_dir_use_symlinks=False
        )
        
        self.pipeline = create_ltx_video_pipeline(
            ckpt_path=model_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'
        )
        logger.info(f"LTX Worker ({self.device}): Modelo pronto na CPU. É um modelo distilled? {self.is_distilled}")
    
    def to_gpu(self):
        if self.device.type == 'cpu': return
        logger.info(f"LTX Worker: Movendo pipeline para a GPU {self.device}...")
        self.pipeline.to(self.device)
        if self.device.type == 'cuda' and can_optimize_fp8():
            logger.info(f"LTX Worker ({self.device}): GPU com suporte a FP8 detectada. Otimizando...")
            optimize_ltx_worker(self)
            logger.info(f"LTX Worker ({self.device}): Otimização completa.")
    
    def to_cpu(self):
        if self.device.type == 'cpu': return
        logger.info(f"LTX Worker: Descarregando pipeline da GPU {self.device}...")
        self.pipeline.to('cpu')
        gc.collect()
        if torch.cuda.is_available(): torch.cuda.empty_cache()

    def generate_video_fragment_internal(self, **kwargs):
        return self.pipeline(**kwargs).images

def _aduc_prepare_conditioning_patch(
    self: "LTXVideoPipeline",
    conditioning_items: Optional[List[Union["ConditioningItem", "LatentConditioningItem"]]],
    init_latents: torch.Tensor,
    num_frames: int,
    height: int,
    width: int,
    vae_per_channel_normalize: bool = False,
    generator=None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
    if not conditioning_items:
        init_latents, init_latent_coords = self.patchifier.patchify(latents=init_latents)
        init_pixel_coords = latent_to_pixel_coords(init_latent_coords, self.vae, causal_fix=self.transformer.config.causal_temporal_positioning)
        return init_latents, init_pixel_coords, None, 0
    
    init_conditioning_mask = torch.zeros_like(init_latents[:, 0, ...], dtype=torch.float32, device=init_latents.device)
    extra_conditioning_latents, extra_conditioning_pixel_coords, extra_conditioning_mask = [], [], []
    extra_conditioning_num_latents = 0
    
    for item in conditioning_items:
        if not isinstance(item, LatentConditioningItem):
            logger.warning("Patch ADUC: Item de condicionamento não é um LatentConditioningItem e será ignorado.")
            continue
        
        media_item_latents = item.latent_tensor.to(dtype=init_latents.dtype, device=init_latents.device)
        media_frame_number, strength = item.media_frame_number, item.conditioning_strength
        
        if media_frame_number == 0:
            f_l, h_l, w_l = media_item_latents.shape[-3:]
            init_latents[..., :f_l, :h_l, :w_l] = torch.lerp(init_latents[..., :f_l, :h_l, :w_l], media_item_latents, strength)
            init_conditioning_mask[..., :f_l, :h_l, :w_l] = strength
        else:
            noise = randn_tensor(media_item_latents.shape, generator=generator, device=media_item_latents.device, dtype=media_item_latents.dtype)
            media_item_latents = torch.lerp(noise, media_item_latents, strength)
            patched_latents, latent_coords = self.patchifier.patchify(latents=media_item_latents)
            pixel_coords = latent_to_pixel_coords(latent_coords, self.vae, causal_fix=self.transformer.config.causal_temporal_positioning)
            pixel_coords[:, 0] += media_frame_number
            extra_conditioning_num_latents += patched_latents.shape[1]
            new_mask = torch.full(patched_latents.shape[:2], strength, dtype=torch.float32, device=init_latents.device)
            extra_conditioning_latents.append(patched_latents)
            extra_conditioning_pixel_coords.append(pixel_coords)
            extra_conditioning_mask.append(new_mask)

    init_latents, init_latent_coords = self.patchifier.patchify(latents=init_latents)
    init_pixel_coords = latent_to_pixel_coords(init_latent_coords, self.vae, causal_fix=self.transformer.config.causal_temporal_positioning)
    init_conditioning_mask, _ = self.patchifier.patchify(latents=init_conditioning_mask.unsqueeze(1))
    init_conditioning_mask = init_conditioning_mask.squeeze(-1)
    
    if extra_conditioning_latents:
        init_latents = torch.cat([*extra_conditioning_latents, init_latents], dim=1)
        init_pixel_coords = torch.cat([*extra_conditioning_pixel_coords, init_pixel_coords], dim=2)
        init_conditioning_mask = torch.cat([*extra_conditioning_mask, init_conditioning_mask], dim=1)
    
    return init_latents, init_pixel_coords, init_conditioning_mask, extra_conditioning_num_latents

# --- Instanciação Singleton ---
with open("config.yaml", 'r') as f:
    config = yaml.safe_load(f)
ltx_gpus_required = config['specialists']['ltx']['gpus_required']
ltx_device_ids = hardware_manager.allocate_gpus('LTX', ltx_gpus_required)
ltx_config_filename = config['specialists']['ltx']['config_file']
ltx_manager_singleton = LtxPoolManager(device_ids=ltx_device_ids, ltx_config_file_name=ltx_config_filename)
logger.info("Especialista de Vídeo (LTX) pronto.")