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 ltx_manager_helpers import ltx_manager_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 os mesmos) ...
    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:
            logger.warning("Tentativa de salvar um tensor de vídeo inválido. Abortando.")
            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_from_latents(self, latent_tensor, 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()
        return silent_video_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, trim_percent: int,
                            handler_strength: float, destination_convergence_strength: float, 
                            video_resolution: int, use_continuity_director: bool, 
                            progress: gr.Progress = gr.Progress()):
        
        # --- [INÍCIO] Lógica de Controle Dinâmico Baseada em Dois Sliders ---

        # 1. Calcular o total de chunks a serem gerados a partir dos segundos
        total_chunks_gerados = max(5, int(round(seconds_per_fragment * 24 / 8)))

        # 2. Calcular o número de chunks a podar com base na porcentagem, com mínimo de 4
        trim_chunks = max(4, int(round(total_chunks_gerados * (trim_percent / 100))))
        
        # Regra de segurança para evitar que a poda consuma o vídeo inteiro
        if trim_chunks >= total_chunks_gerados:
            trim_chunks = total_chunks_gerados - 1
            logger.warning(f"A poda ({trim_percent}%) era muito grande. Ajustada para {trim_chunks} chunks para deixar 1 chunk de vídeo.")

        # 3. Definir fatias e alvos com base nos cálculos
        VIDEO_CHUNK_COUNT = total_chunks_gerados - trim_chunks
        
        HANDLER_CHUNK_INDICES = slice(total_chunks_gerados - 2, total_chunks_gerados)
        ECO_CHUNK_INDICES = slice(total_chunks_gerados - 4, total_chunks_gerados - 2)
        
        HANDLER_FRAME_TARGET = (trim_chunks - 2) * 8
        FRAMES_TO_GENERATE = (total_chunks_gerados - 1) * 8 + 1
        DESTINATION_FRAME_TARGET = FRAMES_TO_GENERATE - 1

        logger.info("="*60)
        logger.info("MODO DE GERAÇÃO: Estratégia de Cauda Longa Dinâmica")
        logger.info(f"  - Duração Solicitada: {seconds_per_fragment}s -> Geração Bruta: {total_chunks_gerados} chunks")
        logger.info(f"  - Poda Solicitada: {trim_percent}% -> Chunks de Poda (Cauda): {trim_chunks}")
        logger.info(f"  - Clipe Final por Fragmento: {VIDEO_CHUNK_COUNT} chunks")
        logger.info(f"  - Guia de Eco (Memória): Chunks {ECO_CHUNK_INDICES.start}-{ECO_CHUNK_INDICES.stop-1}")
        logger.info(f"  - Guia de Handler (Evolução): Chunks {HANDLER_CHUNK_INDICES.start}-{HANDLER_CHUNK_INDICES.stop-1}")
        logger.info(f"  - PONTO DE APLICAÇÃO DO HANDLER (DINÂMICO): Frame {HANDLER_FRAME_TARGET}")
        logger.info("="*60)
        
        base_ltx_params = {"guidance_scale": 1.0, "stg_scale": 0.0, "rescaling_scale": 0.15, "num_inference_steps": 20}
        keyframe_paths = [item[0] if isinstance(item, tuple) else item for item in keyframes]
        video_clips_paths, story_history = [], ""
        target_resolution_tuple = (video_resolution, video_resolution) 
        
        eco_latent_for_next_loop = None
        handler_latent_for_next_loop = None
        
        if len(keyframe_paths) < 3:
            raise gr.Error(f"O modelo de geração requer no mínimo 3 keyframes (Passado, Presente, Futuro). Você forneceu {len(keyframe_paths)}.")
        
        num_transitions_to_generate = len(keyframe_paths) - 2
        
        for i in range(num_transitions_to_generate):
            start_keyframe_index = i + 1
            
            logger.info(f"--- INICIANDO FRAGMENTO {i+1}/{num_transitions_to_generate} ---")
            progress((i + 1) / num_transitions_to_generate, desc=f"Produzindo Transição {i+1}/{num_transitions_to_generate}")
            
            past_keyframe_path = keyframe_paths[start_keyframe_index - 1]
            start_keyframe_path = keyframe_paths[start_keyframe_index]
            destination_keyframe_path = keyframe_paths[start_keyframe_index + 1]
            future_story_prompt = storyboard[start_keyframe_index + 1] if (start_keyframe_index + 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[start_keyframe_index - 1], storyboard[start_keyframe_index], future_story_prompt
            )
            _, motion_prompt = decision["transition_type"], decision["motion_prompt"]
            story_history += f"\n- Ato {i+1}: {motion_prompt}"

            conditioning_items = []
            logger.info("  [0. PREPARAÇÃO] Montando itens de condicionamento...")
            
            if i == 0:
               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(handler_latent_for_next_loop, HANDLER_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}
            new_full_latents = self._generate_latent_tensor_internal(conditioning_items, current_ltx_params, target_resolution_tuple, FRAMES_TO_GENERATE)
            logger.info(f"  [1. GERAÇÃO] Tensor latente bruto gerado com shape: {new_full_latents.shape}.")

            eco_latent_for_next_loop = new_full_latents[:, :, ECO_CHUNK_INDICES, :, :].clone()
            handler_latent_for_next_loop = new_full_latents[:, :, HANDLER_CHUNK_INDICES, :, :].clone()
            logger.info(f"  [GUIAS] Guias para a próxima iteração extraídas. Eco shape: {eco_latent_for_next_loop.shape}, Handler shape: {handler_latent_for_next_loop.shape}.")

            latents_for_video = new_full_latents[:, :, :VIDEO_CHUNK_COUNT, :, :]
            logger.info(f"  [2. EDIÇÃO] Tensor final para vídeo extraído com {latents_for_video.shape[2]} chunks.")

            base_name = f"fragment_{i}_{int(time.time())}"
            video_path = self._generate_video_from_latents(latents_for_video, base_name)
            video_clips_paths.append(video_path)
            yield {"fragment_path": video_path}
                 
        final_movie_path = os.path.join(self.workspace_dir, f"final_movie_silent_{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