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 random
import gc

from audio_specialist import audio_specialist_singleton
from ltx_manager_helpers import ltx_manager_singleton
from flux_kontext_helpers import flux_kontext_singleton
from gemini_helpers import gemini_singleton 
from ltx_video.models.autoencoders.vae_encode import vae_encode, vae_decode

logger = logging.getLogger(__name__)

@dataclass
class LatentConditioningItem:
    latent_tensor: torch.Tensor
    media_frame_number: int
    conditioning_strength: float

class Deformes4DEngine:
    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 (SDR Executor) 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 como save/load/pixels_to_latents permanecem iguais) ...
    def save_latent_tensor(self, tensor: torch.Tensor, path: str):
        torch.save(tensor.cpu(), path)
        logger.info(f"Tensor latente salvo em: {path}")

    def load_latent_tensor(self, path: str) -> torch.Tensor:
        tensor = torch.load(path, map_location=self.device)
        logger.info(f"Tensor latente carregado de: {path} para o dispositivo {self.device}")
        return tensor

    @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) as writer:
            for frame in video_np: writer.append_data(frame)
        logger.info(f"Vídeo salvo em: {path}")

    def _preprocess_image_for_latent_conversion(self, image: Image.Image, target_resolution: tuple) -> Image.Image:
        if image.size != target_resolution:
            logger.info(f"  - AÇÃO: Redimensionando imagem de {image.size} para {target_resolution} antes da conversão para latente.")
            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 _generate_video_and_audio_from_latents(self, latent_tensor, audio_prompt, base_name):
        silent_video_path = os.path.join(self.workspace_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()
        
        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 em {os.path.basename(silent_video_path)}. 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 _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)
        return new_full_latents

    def concatenate_videos_ffmpeg(self, video_paths: list[str], output_path: str) -> 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")
        cmd_list = ['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', list_file_path, '-c', 'copy', output_path]
        logger.info("Executando concatenação FFmpeg...")
        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}")
            raise gr.Error(f"Falha na montagem final do vídeo. Detalhes: {e.stderr}")
        return output_path
    
    def generate_full_movie(self, 
                            keyframes: list, 
                            global_prompt: str, 
                            storyboard: list, 
                            seconds_per_fragment: float, 
                            overlap_percent: int, 
                            echo_frames: int,
                            handler_strength: float, 
                            destination_convergence_strength: float,
                            video_resolution: int, 
                            use_continuity_director: bool, 
                            progress: gr.Progress = gr.Progress()):
        
        base_ltx_params = {
            "guidance_scale": 1.0, "stg_scale": 0.0,
            "rescaling_scale": 0.15, "num_inference_steps": 7,
        }

        keyframe_paths = [item[0] if isinstance(item, tuple) else item for item in keyframes]
        video_clips_paths, story_history, audio_history = [], "", "This is the beginning of the film."
        target_resolution_tuple = (video_resolution, video_resolution) 
        n_trim_latents = self._quantize_to_multiple(int(seconds_per_fragment * 24 * (overlap_percent / 100.0)), 8)
        
        # --- NOVA LÓGICA: Variáveis para guardar os tensores de continuidade ---
        prepared_echo_latent = None
        prepared_handler_latent = None
        
        num_transitions_to_generate = len(keyframe_paths) - 1
        
        for i in range(num_transitions_to_generate):
            progress((i + 1) / num_transitions_to_generate, desc=f"Produzindo Transição {i+1}/{num_transitions_to_generate}")

            start_keyframe_path = keyframe_paths[i]
            destination_keyframe_path = keyframe_paths[i+1]
            present_scene_desc = storyboard[i]

            is_first_fragment = (prepared_handler_latent is None)
            
            # ... (Lógica de decisão do Gemini e do diretor de som permanece a mesma) ...
            if is_first_fragment:
                transition_type = "start"
                motion_prompt = gemini_singleton.get_initial_motion_prompt(
                    global_prompt, start_keyframe_path, destination_keyframe_path, present_scene_desc
                )
            else:
                past_keyframe_path = keyframe_paths[i-1]
                past_scene_desc = storyboard[i-1]
                future_scene_desc = storyboard[i+1] if (i+1) < len(storyboard) else "A cena final."
                decision = gemini_singleton.get_cinematic_decision(
                    global_prompt=global_prompt, story_history=story_history,
                    past_keyframe_path=past_keyframe_path, present_keyframe_path=start_keyframe_path,
                    future_keyframe_path=destination_keyframe_path, past_scene_desc=past_scene_desc,
                    present_scene_desc=present_scene_desc, future_scene_desc=future_scene_desc
                )
                transition_type, motion_prompt = decision["transition_type"], decision["motion_prompt"]
            
            story_history += f"\n- Ato {i+1} ({transition_type}): {motion_prompt}"

            if use_continuity_director:
                if is_first_fragment:
                    audio_prompt = gemini_singleton.get_sound_director_prompt(
                        audio_history=audio_history,
                        past_keyframe_path=start_keyframe_path, present_keyframe_path=start_keyframe_path,
                        future_keyframe_path=destination_keyframe_path, present_scene_desc=present_scene_desc,
                        motion_prompt=motion_prompt, future_scene_desc=storyboard[i+1] if (i+1) < len(storyboard) else "The final scene."
                    )
                else:
                    audio_prompt = gemini_singleton.get_sound_director_prompt(
                        audio_history=audio_history, past_keyframe_path=keyframe_paths[i-1],
                        present_keyframe_path=start_keyframe_path, future_keyframe_path=destination_keyframe_path,
                        present_scene_desc=present_scene_desc, motion_prompt=motion_prompt,
                        future_scene_desc=storyboard[i+1] if (i+1) < len(storyboard) else "The final scene."
                    )
            else:
                audio_prompt = present_scene_desc
            
            audio_history = audio_prompt

            conditioning_items = []
            current_ltx_params = {**base_ltx_params, "handler_strength": handler_strength, "motion_prompt": motion_prompt}
            total_frames_to_generate = self._quantize_to_multiple(int(seconds_per_fragment * 24), 8) + 1

            # --- NOVA LÓGICA: Preparação das instruções de condicionamento ---
            if is_first_fragment:
                img_start = self._preprocess_image_for_latent_conversion(Image.open(start_keyframe_path).convert("RGB"), target_resolution_tuple)
                start_latent = self.pil_to_latent(img_start)
                conditioning_items.append(LatentConditioningItem(start_latent, 0, 1.0))
                if transition_type != "cut":
                    img_dest = self._preprocess_image_for_latent_conversion(Image.open(destination_keyframe_path).convert("RGB"), target_resolution_tuple)
                    destination_latent = self.pil_to_latent(img_dest)
                    conditioning_items.append(LatentConditioningItem(destination_latent, total_frames_to_generate - 1, destination_convergence_strength))
            else:
                # Usa os tensores pré-preparados da iteração anterior
                conditioning_items.append(LatentConditioningItem(prepared_echo_latent, 0, 1.0))
                conditioning_items.append(LatentConditioningItem(prepared_handler_latent, echo_frames, handler_strength))
                if transition_type == "continuous":
                    img_dest = self._preprocess_image_for_latent_conversion(Image.open(destination_keyframe_path).convert("RGB"), target_resolution_tuple)
                    destination_latent = self.pil_to_latent(img_dest)
                    conditioning_items.append(LatentConditioningItem(destination_latent, total_frames_to_generate - 1, destination_convergence_strength))
            
            new_full_latents = self._generate_latent_tensor_internal(conditioning_items, current_ltx_params, target_resolution_tuple, total_frames_to_generate)
            
            # --- NOVA LÓGICA: Preparação movida para o final do loop ---
            is_last_fragment = (i == num_transitions_to_generate - 1)
            
            if not is_last_fragment:
                # ANTECIPAÇÃO: Prepara os tensores para a PRÓXIMA iteração
                prepared_handler_latent = new_full_latents[:, :, -1:, :, :].clone()
                prepared_echo_latent = new_full_latents[:, :, -echo_frames:, :, :].clone()
                
                # CORTE NO FIM: Define os latentes para o VÍDEO ATUAL, removendo a sobreposição
                if n_trim_latents > 0 and new_full_latents.shape[2] > n_trim_latents:
                    latents_for_video = new_full_latents[:, :, :-n_trim_latents, :, :]
                else:
                    latents_for_video = new_full_latents
            else:
                # O último fragmento não precisa preparar nada para o futuro, então renderiza-se por completo.
                latents_for_video = new_full_latents

            base_name = f"fragment_{i}_{int(time.time())}"
            video_with_audio_path = self._generate_video_and_audio_from_latents(latents_for_video, audio_prompt, base_name)
            video_clips_paths.append(video_with_audio_path)
            
            if transition_type == "cut":
                # Se for um corte, limpa a memória para a próxima iteração começar do zero.
                prepared_echo_latent = None
                prepared_handler_latent = None
                
            yield {"fragment_path": video_with_audio_path}

        final_movie_path = os.path.join(self.workspace_dir, f"final_movie_{int(time.time())}.mp4")
        self.concatenate_videos_ffmpeg(video_clips_paths, final_movie_path)
        
        logger.info(f"Filme completo salvo em: {final_movie_path}")
        yield {"final_path": final_movie_path}

    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