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

from ltx_manager_helpers import ltx_manager_singleton
from gemini_helpers import gemini_singleton 
from upscaler_specialist import upscaler_specialist_singleton
from hd_specialist import hd_specialist_singleton
from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
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.")

    @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)
        
    # --- 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()):
        
        FPS = 24
        FRAMES_PER_LATENT_CHUNK = 8
        ECO_LATENT_CHUNKS = 2
        
        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}
        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 = None
        dejavu_latent_for_next_loop = None
        
        # [CORREÇÃO 1] Inicialização correta da lista
        latent_fragments = []
        
        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
        
        for i in range(num_transitions_to_generate):
            fragment_index = i + 1
            progress(i / num_transitions_to_generate, desc=f"Produzindo Transição {fragment_index}/{num_transitions_to_generate}")
            
            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:
               logger.info("    - Primeiro fragmento: Usando Keyframe inicial como âncora de partida.")
               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:
               logger.info("    - Âncora 1: Eco Causal (C) - Herança do passado.")
               conditioning_items.append(LatentConditioningItem(eco_latent_for_next_loop, 0, 1.0))
               logger.info("    - Âncora 2: Déjà-Vu (D) - Memória de um futuro idealizado.")
               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:, :, :]

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

            if use_upscaler:
                latents_video = self.upscale_latents(latents_video)
            
            latent_fragments.append(latents_video)
        
    
        logger.info("--- CONCATENANDO nem TODOS OS FRAGMENTOS LATENTES ---")
        tensors_para_concatenar = []
        for idx, tensor_frag in enumerate(latent_fragments):
            # Move cada tensor para o dispositivo de destino antes de adicioná-lo à lista.
            target_device = self.device
            tensor_on_target_device = tensor_frag.to(target_device)
            if idx < len(latent_fragments) - 1:
                tensors_para_concatenar.append(tensor_on_target_device[:, :, :-1, :, :]) 
            else:
                tensors_para_concatenar.append(tensor_on_target_device)

        processed_latents = torch.cat(tensors_para_concatenar, dim=2)
        logger.info(f"Concatenação concluída. Shape final do tensor latente: {final_concatenated_latents.shape}")
        
        
        
        
        # [CORREÇÃO 2] Referência correta da variável no log
        logger.info(f"Concatenação concluída. Shape final do tensor latente: {processed_latents.shape}")
        
        if use_refiner:
            processed_latents = self.refine_latents(
                processed_latents, 
                motion_prompt="", 
                guidance_scale=1.0
            )
        
        # --- [INÍCIO DA SEÇÃO CORRIGIDA PARA EXECUÇÃO] ---
        base_name = f"movie_{int(time.time())}"
        # Define um caminho único para o vídeo que sai desta etapa, antes do HD.
        intermediate_video_path = os.path.join(self.workspace_dir, f"{base_name}_intermediate.mp4")
        
        if use_audio:
             # A função de áudio agora salva o vídeo com áudio no caminho intermediário
             intermediate_video_path = self._generate_video_and_audio_from_latents(processed_latents, global_prompt, base_name)
        else:
            logger.info("Etapa de sonoplastia desativada. Renderizando vídeo silencioso.")
            pixel_tensor = self.latents_to_pixels(processed_latents)
            self.save_video_from_tensor(pixel_tensor, intermediate_video_path, fps=24)
            del pixel_tensor
        
        del processed_latents; gc.collect(); torch.cuda.empty_cache()
        
        # Define o caminho final
        final_video_path = os.path.join(self.workspace_dir, f"{base_name}_FINAL.mp4")
        
        if use_hd:
            progress(0.9, desc="Masterização Final (HD)...")
            try:
                # O HD agora lê o intermediate_video_path e salva no final_video_path
                hd_specialist_singleton.process_video(
                    input_video_path=intermediate_video_path,
                    output_video_path=final_video_path,
                    prompt=" "
                )
            except Exception as e:
                logger.error(f"Falha na masterização HD: {e}. Usando vídeo de qualidade padrão.")
                os.rename(intermediate_video_path, final_video_path)
        else:
            logger.info("Etapa de edição HD desativada.")
            # Se o HD não for usado, o vídeo intermediário se torna o final.
            os.rename(intermediate_video_path, final_video_path)
        # --- [FIM DA SEÇÃO CORRIGIDA] ---
        
        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):
        # Esta função foi movida para cima, mas sua lógica interna permanece a mesma.
        silent_video_path = os.path.join(self.workspace_dir, f"{base_name}_silent_for_audio.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()
        
        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.")
             num_pixel_frames = latent_tensor.shape[2] * 8
             frag_duration = num_pixel_frames / 24.0

        video_with_audio_path = audio_specialist_singleton.generate_audio_for_video(
            video_path=silent_video_path, prompt=audio_prompt,
            duration_seconds=frag_duration)
        
        if os.path.exists(silent_video_path):
             os.remove(silent_video_path)
        return video_with_audio_path
        
    def refine_latents(self, latents: torch.Tensor, fps: int = 24, denoise_strength: float = 0.35, refine_steps: int = 12, motion_prompt: str = "...", **kwargs) -> torch.Tensor:
        logger.info(f"Refinando tensor latente com shape {latents.shape}.")
        _, _, num_latent_frames, latent_h, latent_w = latents.shape
        video_scale_factor = getattr(self.vae.config, 'temporal_scale_factor', 8)
        vae_scale_factor = getattr(self.vae.config, 'spatial_downscale_factor', 8)
        
        pixel_height = latent_h * vae_scale_factor
        pixel_width = latent_w * vae_scale_factor
        pixel_frames = (num_latent_frames - 1) * video_scale_factor
        
        final_ltx_params = {
            "height": pixel_height, "width": pixel_width, "video_total_frames": pixel_frames,
            "video_fps": fps, "motion_prompt": motion_prompt, "current_fragment_index": int(time.time()),
            "denoise_strength": denoise_strength, "refine_steps": refine_steps,
            "guidance_scale": kwargs.get('guidance_scale', 2.0)
        }
        
        refined_latents_tensor, _ = self.ltx_manager.refine_latents(latents, **final_ltx_params)
        
        logger.info(f"Retornando tensor latente refinado com shape: {refined_latents_tensor.shape}")
        return refined_latents_tensor
        
    def upscale_latents(self, latents: torch.Tensor) -> torch.Tensor:
        logger.info(f"Realizando upscale em tensor latente com shape {latents.shape}.")
        return upscaler_specialist_singleton.upscale(latents)
        
    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