deformes4D_engine.py

# deformes4D_engine.py
# Copyright (C) 4 de Agosto de 2025  Carlos Rodrigues dos Santos
#
# MODIFICATIONS FOR ADUC-SDR:
# Copyright (C) 2025 Carlos Rodrigues dos Santos. All rights reserved.
#
# This file is part of the ADUC-SDR project. It contains the core logic for
# video fragment generation, latent manipulation, and dynamic editing, 
# governed by the ADUC orchestrator.
# This component is licensed under the GNU Affero General Public License v3.0.

import os
import time
import imageio
import numpy as np
import torch
import logging
from PIL import Image, ImageOps
from dataclasses import dataclass
import gradio as gr
import subprocess
import gc
import shutil

from ltx_manager_helpers import ltx_manager_singleton
from gemini_helpers import gemini_singleton 
# [REATORADO] Importa o novo especialista
from latent_enhancer_specialist import latent_enhancer_specialist_singleton
from hd_specialist import hd_specialist_singleton
from ltx_video.models.autoencoders.vae_encode import vae_encode, vae_decode
from audio_specialist import audio_specialist_singleton

logger = logging.getLogger(__name__)

@dataclass
class LatentConditioningItem:
    """Representa uma âncora de condicionamento no espaço latente para a Câmera (Ψ)."""
    latent_tensor: torch.Tensor
    media_frame_number: int
    conditioning_strength: float

class Deformes4DEngine:
    """
    Implementa a Câmera (Ψ) e o Destilador (Δ) da arquitetura ADUC-SDR.
    Orquestra a geração, pós-produção latente e renderização final dos fragmentos de vídeo.
    """
    def __init__(self, ltx_manager, workspace_dir="deformes_workspace"):
        self.ltx_manager = ltx_manager
        self.workspace_dir = workspace_dir
        self._vae = None
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        logger.info("Especialista Deformes4D (Executor ADUC-SDR) inicializado.")
        # Cria o diretório de workspace se não existir
        os.makedirs(self.workspace_dir, exist_ok=True)


    @property
    def vae(self):
        if self._vae is None:
            self._vae = self.ltx_manager.workers[0].pipeline.vae
        self._vae.to(self.device); self._vae.eval()
        return self._vae

    # --- MÉTODOS AUXILIARES ---
    @torch.no_grad()
    def pixels_to_latents(self, tensor: torch.Tensor) -> torch.Tensor:
        tensor = tensor.to(self.device, dtype=self.vae.dtype)
        return vae_encode(tensor, self.vae, vae_per_channel_normalize=True)

    @torch.no_grad()
    def latents_to_pixels(self, latent_tensor: torch.Tensor, decode_timestep: float = 0.05) -> torch.Tensor:
        latent_tensor = latent_tensor.to(self.device, dtype=self.vae.dtype)
        timestep_tensor = torch.tensor([decode_timestep] * latent_tensor.shape[0], device=self.device, dtype=latent_tensor.dtype)
        return vae_decode(latent_tensor, self.vae, is_video=True, timestep=timestep_tensor, vae_per_channel_normalize=True)

    def save_video_from_tensor(self, video_tensor: torch.Tensor, path: str, fps: int = 24):
        if video_tensor is None or video_tensor.ndim != 5 or video_tensor.shape[2] == 0: return
        video_tensor = video_tensor.squeeze(0).permute(1, 2, 3, 0)
        video_tensor = (video_tensor.clamp(-1, 1) + 1) / 2.0
        video_np = (video_tensor.detach().cpu().float().numpy() * 255).astype(np.uint8)
        with imageio.get_writer(path, fps=fps, codec='libx264', quality=8, output_params=['-pix_fmt', 'yuv420p']) as writer:
            for frame in video_np: writer.append_data(frame)

    def _preprocess_image_for_latent_conversion(self, image: Image.Image, target_resolution: tuple) -> Image.Image:
        if image.size != target_resolution:
            return ImageOps.fit(image, target_resolution, Image.Resampling.LANCZOS)
        return image

    def pil_to_latent(self, pil_image: Image.Image) -> torch.Tensor:
        image_np = np.array(pil_image).astype(np.float32) / 255.0
        tensor = torch.from_numpy(image_np).permute(2, 0, 1).unsqueeze(0).unsqueeze(2)
        tensor = (tensor * 2.0) - 1.0
        return self.pixels_to_latents(tensor)
    
    def concatenate_videos_ffmpeg(self, video_paths: list[str], output_path: str):
        if not video_paths: raise gr.Error("Nenhum fragmento de vídeo para montar.")
        list_file_path = os.path.join(self.workspace_dir, "concat_list.txt")
        with open(list_file_path, 'w', encoding='utf-8') as f:
            for path in video_paths: f.write(f"file '{os.path.abspath(path)}'\n")
        
        # Tenta usar aceleração de hardware (GPU) para a concatenação, se disponível
        cmd_list = ['ffmpeg', '-y', '-hwaccel', 'auto', '-f', 'concat', '-safe', '0', '-i', list_file_path, '-c', 'copy', output_path]
        logger.info(f"Concatenando {len(video_paths)} clipes de vídeo em {output_path}...")
        try:
            subprocess.run(cmd_list, check=True, capture_output=True, text=True)
        except subprocess.CalledProcessError as e:
            logger.error(f"Erro no FFmpeg: {e.stderr}")
            # Tenta novamente sem aceleração de hardware como fallback
            logger.info("Tentando concatenar novamente sem aceleração de hardware...")
            cmd_list = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', list_file_path, '-c', 'copy', output_path]
            try:
                subprocess.run(cmd_list, check=True, capture_output=True, text=True)
            except subprocess.CalledProcessError as e_fallback:
                 logger.error(f"Erro no FFmpeg (fallback): {e_fallback.stderr}")
                 raise gr.Error(f"Falha na montagem final do vídeo. Detalhes: {e_fallback.stderr}")
        
    # --- NÚCLEO DA LÓGICA ADUC-SDR ---
    def generate_full_movie(self, keyframes: list, global_prompt: str, storyboard: list, 
                            seconds_per_fragment: float, trim_percent: int,
                            handler_strength: float, destination_convergence_strength: float, 
                            use_upscaler: bool, use_refiner: bool, use_hd: bool, use_audio: bool,
                            video_resolution: int, use_continuity_director: bool, 
                            progress: gr.Progress = gr.Progress()):
        
        # --- ETAPA 0: SETUP ---
        FPS = 24
        FRAMES_PER_LATENT_CHUNK = 8
        ECO_LATENT_CHUNKS = 2
        LATENT_PROCESSING_CHUNK_SIZE = 10 # Processa 10 fragmentos latentes por vez para economizar memória
        
        run_timestamp = int(time.time())
        temp_latent_dir = os.path.join(self.workspace_dir, f"temp_latents_{run_timestamp}")
        temp_video_clips_dir = os.path.join(self.workspace_dir, f"temp_clips_{run_timestamp}")
        os.makedirs(temp_latent_dir, exist_ok=True)
        os.makedirs(temp_video_clips_dir, exist_ok=True)

        total_frames_brutos = self._quantize_to_multiple(int(seconds_per_fragment * FPS), FRAMES_PER_LATENT_CHUNK)
        frames_a_podar = self._quantize_to_multiple(int(total_frames_brutos * (trim_percent / 100)), FRAMES_PER_LATENT_CHUNK)
        latents_a_podar = frames_a_podar // FRAMES_PER_LATENT_CHUNK

        DEJAVU_FRAME_TARGET = frames_a_podar - 1 if frames_a_podar > 0 else 0
        DESTINATION_FRAME_TARGET = total_frames_brutos - 1
        
        base_ltx_params = {"guidance_scale": 2.0, "stg_scale": 0.025, "rescaling_scale": 0.15, "num_inference_steps": 20, "image_cond_noise_scale": 0.00}
        refine_ltx_params = {"motion_prompt": "", "guidance_scale": 1.0, "denoise_strength": 0.35, "refine_steps": 12}
        
        keyframe_paths = [item[0] if isinstance(item, tuple) else item for item in keyframes]
        story_history = ""
        target_resolution_tuple = (video_resolution, video_resolution) 
        
        eco_latent_for_next_loop, dejavu_latent_for_next_loop = None, None
        latent_fragment_paths = [] # Lista para armazenar caminhos dos latentes salvos no disco
        
        if len(keyframe_paths) < 2: raise gr.Error(f"A geração requer no mínimo 2 keyframes. Você forneceu {len(keyframe_paths)}.")
        
        num_transitions_to_generate = len(keyframe_paths) - 1
        
        # --- ETAPA 1: GERAR FRAGMENTOS LATENTES E SALVAR EM DISCO ---
        logger.info("--- INICIANDO ETAPA 1: Geração de Fragmentos Latentes ---")
        for i in range(num_transitions_to_generate):
            fragment_index = i + 1
            progress(i / num_transitions_to_generate, desc=f"Gerando Latente {fragment_index}/{num_transitions_to_generate}")
            
            # (Lógica de decisão do Gemini e preparação de âncoras - inalterada)
            past_keyframe_path = keyframe_paths[i - 1] if i > 0 else keyframe_paths[i]
            start_keyframe_path = keyframe_paths[i]
            destination_keyframe_path = keyframe_paths[i + 1]
            future_story_prompt = storyboard[i + 1] if (i + 1) < len(storyboard) else "A cena final."
            decision = gemini_singleton.get_cinematic_decision(global_prompt, story_history, past_keyframe_path, start_keyframe_path, destination_keyframe_path, storyboard[i - 1] if i > 0 else "O início.", storyboard[i], future_story_prompt)
            transition_type, motion_prompt = decision["transition_type"], decision["motion_prompt"]
            story_history += f"\n- Ato {fragment_index}: {motion_prompt}"
            conditioning_items = []
            if eco_latent_for_next_loop is None:
               img_start = self._preprocess_image_for_latent_conversion(Image.open(start_keyframe_path).convert("RGB"), target_resolution_tuple)
               conditioning_items.append(LatentConditioningItem(self.pil_to_latent(img_start), 0, 1.0))
            else:
               conditioning_items.append(LatentConditioningItem(eco_latent_for_next_loop, 0, 1.0))
               conditioning_items.append(LatentConditioningItem(dejavu_latent_for_next_loop, DEJAVU_FRAME_TARGET, handler_strength))
            img_dest = self._preprocess_image_for_latent_conversion(Image.open(destination_keyframe_path).convert("RGB"), target_resolution_tuple)
            conditioning_items.append(LatentConditioningItem(self.pil_to_latent(img_dest), DESTINATION_FRAME_TARGET, destination_convergence_strength))
            
            current_ltx_params = {**base_ltx_params, "motion_prompt": motion_prompt}
            latents_brutos = self._generate_latent_tensor_internal(conditioning_items, current_ltx_params, target_resolution_tuple, total_frames_brutos)

            last_trim = latents_brutos[:, :, -(latents_a_podar+1):, :, :].clone()
            eco_latent_for_next_loop = last_trim[:, :, :2, :, :].clone()   
            dejavu_latent_for_next_loop = last_trim[:, :, -1:, :, :].clone()
            
            latents_video = latents_brutos[:, :, :-(latents_a_podar-1), :, :].clone()
            latents_video = latents_video[:, :, 1:, :, :]

            del last_trim, latents_brutos
            gc.collect(); torch.cuda.empty_cache()

            if transition_type == "cut":
                eco_latent_for_next_loop, dejavu_latent_for_next_loop = None, None

            # [REATORADO] Mover latente para CPU e salvar no disco para liberar VRAM
            cpu_latent = latents_video.cpu()
            latent_path = os.path.join(temp_latent_dir, f"latent_fragment_{i:04d}.pt")
            torch.save(cpu_latent, latent_path)
            latent_fragment_paths.append(latent_path)

            del latents_video, cpu_latent
            gc.collect()

        del eco_latent_for_next_loop, dejavu_latent_for_next_loop
        gc.collect(); torch.cuda.empty_cache()
        
        # --- ETAPA 2: PROCESSAR LATENTES EM LOTES (CHUNKS) ---
        logger.info(f"--- INICIANDO ETAPA 2: Processamento de {len(latent_fragment_paths)} latentes em lotes de {LATENT_PROCESSING_CHUNK_SIZE} ---")
        final_video_clip_paths = []
        num_chunks = -(-len(latent_fragment_paths) // LATENT_PROCESSING_CHUNK_SIZE) # Ceiling division

        for i in range(num_chunks):
            chunk_start_index = i * LATENT_PROCESSING_CHUNK_SIZE
            chunk_end_index = chunk_start_index + LATENT_PROCESSING_CHUNK_SIZE
            chunk_paths = latent_fragment_paths[chunk_start_index:chunk_end_index]
            
            progress(i / num_chunks, desc=f"Processando Lote {i+1}/{num_chunks}")

            # Carrega os tensores do lote atual do disco para a GPU
            tensors_in_chunk = [torch.load(p, map_location=self.device) for p in chunk_paths]

            # Concatena os tensores do lote, removendo o latente de sobreposição
            tensors_para_concatenar = [
                frag[:, :, :-1, :, :] if j < len(tensors_in_chunk) - 1 else frag
                for j, frag in enumerate(tensors_in_chunk)
            ]
            sub_group_latent = torch.cat(tensors_para_concatenar, dim=2)
            del tensors_in_chunk, tensors_para_concatenar
            gc.collect(); torch.cuda.empty_cache()

            logger.info(f"Lote {i+1} concatenado. Shape do sub-latente: {sub_group_latent.shape}")

            # 1. (Opcional) Upscaler Latente
            if use_upscaler:
                logger.info(f"Aplicando Upscaler no lote {i+1}...")
                sub_group_latent = latent_enhancer_specialist_singleton.upscale(sub_group_latent)
                gc.collect(); torch.cuda.empty_cache()

            # 2. Decodificar Latente para Vídeo (com ou sem áudio)
            base_name = f"clip_{i:04d}_{run_timestamp}"
            current_clip_path = os.path.join(temp_video_clips_dir, f"{base_name}_temp.mp4")
            
            if use_audio:
                # O áudio é gerado para o prompt global por enquanto. Pode ser adaptado.
                current_clip_path = self._generate_video_and_audio_from_latents(sub_group_latent, global_prompt, base_name)
            else:
                pixel_tensor = self.latents_to_pixels(sub_group_latent)
                self.save_video_from_tensor(pixel_tensor, current_clip_path, fps=FPS)
                del pixel_tensor
            
            del sub_group_latent
            gc.collect(); torch.cuda.empty_cache()

            # 3. (Opcional) Masterização HD
            if use_hd:
                logger.info(f"Aplicando masterização HD no clipe {i+1}...")
                hd_clip_path = os.path.join(temp_video_clips_dir, f"{base_name}_hd.mp4")
                try:
                    hd_specialist_singleton.process_video(input_video_path=current_clip_path, output_video_path=hd_clip_path, prompt=global_prompt)
                    # Apaga o clipe não-HD para economizar espaço
                    if os.path.exists(current_clip_path) and current_clip_path != hd_clip_path:
                        os.remove(current_clip_path)
                    current_clip_path = hd_clip_path
                except Exception as e:
                    logger.error(f"Falha na masterização HD do clipe {i+1}: {e}. Usando versão padrão.")
            
            # 4. Adicionar caminho do clipe final à lista
            final_video_clip_paths.append(current_clip_path)

        #if use_refiner:
        #    progress(0.8, desc="Refinando continuidade visual...")
        #    # [REATORADO] Chamada para o novo especialista
        #    # OBS: Refinamento foi desativado conforme solicitado por degradar a lógica das keyframes.

        # --- ETAPA 3: MONTAGEM FINAL ---
        progress(0.98, desc="Montagem final dos clipes...")
        final_video_path = os.path.join(self.workspace_dir, f"filme_final_{run_timestamp}.mp4")
        self.concatenate_videos_ffmpeg(final_video_clip_paths, final_video_path)
        
        # --- ETAPA 4: LIMPEZA ---
        logger.info("Limpando arquivos temporários...")
        try:
            shutil.rmtree(temp_latent_dir)
            shutil.rmtree(temp_video_clips_dir)
            concat_list_path = os.path.join(self.workspace_dir, "concat_list.txt")
            if os.path.exists(concat_list_path):
                os.remove(concat_list_path)
        except OSError as e:
            logger.warning(f"Não foi possível remover os diretórios temporários: {e}")

        logger.info(f"Processo concluído! Vídeo final salvo em: {final_video_path}")
        yield {"final_path": final_video_path}

    def _generate_video_and_audio_from_latents(self, latent_tensor, audio_prompt, base_name):
        # Este método agora opera em um diretório temporário para os clipes
        temp_video_clips_dir = os.path.dirname(os.path.join(self.workspace_dir, base_name)) # Hack para obter o diretório correto
        silent_video_path = os.path.join(temp_video_clips_dir, f"{base_name}_silent.mp4")

        pixel_tensor = self.latents_to_pixels(latent_tensor)
        self.save_video_from_tensor(pixel_tensor, silent_video_path, fps=24)
        del pixel_tensor; gc.collect(); torch.cuda.empty_cache()
        
        try:
            result = subprocess.run(
                ["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "default=noprint_wrappers=1:nokey=1", silent_video_path],
                capture_output=True, text=True, check=True)
            frag_duration = float(result.stdout.strip())
        except (subprocess.CalledProcessError, ValueError, FileNotFoundError):
             logger.warning(f"ffprobe falhou. Calculando duração manualmente a partir dos latentes.")
             # O VAE interpola, então o número de frames é (num_latentes - 1) * 8 + 1 (aproximadamente)
             num_pixel_frames = (latent_tensor.shape[2] - 1) * 8 + 1
             frag_duration = num_pixel_frames / 24.0

        # Salva o vídeo com áudio no mesmo diretório temporário
        video_with_audio_path = audio_specialist_singleton.generate_audio_for_video(
            video_path=silent_video_path, prompt=audio_prompt,
            duration_seconds=frag_duration,
            output_path_override=os.path.join(temp_video_clips_dir, f"{base_name}_with_audio.mp4")
        )
        
        if os.path.exists(silent_video_path):
             os.remove(silent_video_path)
        return video_with_audio_path
        
    def _generate_latent_tensor_internal(self, conditioning_items, ltx_params, target_resolution, total_frames_to_generate):
        final_ltx_params = {
            **ltx_params, 'width': target_resolution[0], 'height': target_resolution[1],
            'video_total_frames': total_frames_to_generate, 'video_fps': 24,
            'current_fragment_index': int(time.time()), 'conditioning_items_data': conditioning_items
        }
        new_full_latents, _ = self.ltx_manager.generate_latent_fragment(**final_ltx_params)
        gc.collect()
        torch.cuda.empty_cache()
        return new_full_latents
        
    def _quantize_to_multiple(self, n, m):
        if m == 0: return n
        quantized = int(round(n / m) * m)
        return m if n > 0 and quantized == 0 else quantized