# 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 _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_old(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()): # 1. Definição dos Parâmetros da Geração com base na Tese 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) total_latents_brutos = total_frames_brutos // 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 if total_latents_brutos <= latents_a_podar: raise gr.Error(f"A porcentagem de poda ({trim_percent}%) é muito alta. Reduza-a ou aumente a duração.") DEJAVU_FRAME_TARGET = frames_a_podar - 1 DESTINATION_FRAME_TARGET = total_frames_brutos - 1 logger.info("--- CONFIGURAÇÃO DA GERAÇÃO ADUC-SDR ---") logger.info(f"Total de Latents por Geração Exploratória (V_bruto): {total_latents_brutos} ({total_frames_brutos} frames)") logger.info(f"Latents a serem descartados (Poda Causal): {latents_a_podar} ({frames_a_podar} frames)") logger.info(f"Chunks Latentes do Eco Causal (C): {ECO_LATENT_CHUNKS}") logger.info(f"Frame alvo do Déjà-Vu (D): {DEJAVU_FRAME_TARGET}") logger.info(f"Frame alvo do Destino (K): {DESTINATION_FRAME_TARGET}") logger.info("------------------------------------------") # 2. Inicialização do Estado 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] video_clips_paths, story_history = [], "" target_resolution_tuple = (video_resolution, video_resolution) eco_latent_for_next_loop = None dejavu_latent_for_next_loop = None 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 # 3. Loop Principal de Geração de Fragmentos for i in range(num_transitions_to_generate): fragment_index = i + 1 logger.info(f"--- INICIANDO FRAGMENTO {fragment_index}/{num_transitions_to_generate} ---") progress(fragment_index / num_transitions_to_generate, desc=f"Produzindo Transição {fragment_index}/{num_transitions_to_generate}") # 3.1. Consulta ao Maestro (Γ) para obter a intenção (Pᵢ) 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}" # 3.2. Montagem das Âncoras para a Fórmula Canônica Ψ({C, D, K}, P) conditioning_items = [] logger.info(" [Ψ.1] Montando âncoras causais...") 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)) logger.info(" - Âncora 3: Destino (K) - Âncora geométrica/narrativa.") 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)) # 3.3. Execução da Câmera (Ψ): Geração Exploratória para criar V_bruto logger.info(f" [Ψ.2] Câmera (Ψ) executando a geração exploratória de {total_latents_brutos} chunks latentes...") 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) logger.info(f" - Geração concluída. Tensor latente bruto (V_bruto) criado com shape: {latents_brutos.shape}.") # 3.4. Execução do Destilador (Δ): Implementação do Ciclo de Poda Causal (com workaround empírico) logger.info(f" [Δ] Destilador (Δ) executando o Ciclo de Poda Causal...") 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:, :, :] logger.info(f" [Δ] latents_video {latents_video.shape}") logger.info(f" - (Δ.1) Déjà-Vu (D) destilado. Shape: {dejavu_latent_for_next_loop.shape}") logger.info(f" - (Δ.2) Eco Causal (C) extraído. Shape: {eco_latent_for_next_loop.shape}") if transition_type == "cut": logger.warning(" - DECISÃO DO MAESTRO: Corte ('cut'). Resetando a memória causal (Eco e Déjà-Vu).") 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) current_step += 1 progress(current_step / TOTAL_STEPS, desc="Unificação Causal (Concatenação)...") logger.info("--- CONCATENANDO 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}") if use_refiner: current_step += 1 progress(current_step / TOTAL_STEPS, desc="Polimento Global (Denoise)...") processed_latents = self.refine_latents( processed_latents, motion_prompt="", guidance_scale=1.0 ) logger.info(f"Polimento global aplicado. Shape: {processed_latents.shape}") else: logger.info("Etapa de refinamento desativada.") base_name = f"movie_{int(time.time())}" current_step += 1 progress(current_step / TOTAL_STEPS, desc="Renderização (em lotes)...") if use_audio: video_path = self._generate_video_and_audio_from_latents(processed_latents, global_prompt, base_name) else: video_path = os.path.join(self.workspace_dir, f"{base_name}_silent.mp4") logger.info("Etapa de sonoplastia desativada.") pixel_tensor = self.latents_to_pixels(processed_latents) self.save_video_from_tensor(pixel_tensor, video_path, fps=24) if use_hd: current_step += 1 progress(current_step / TOTAL_STEPS, desc="Masterização Final (HD)...") try: hd_specialist_singleton.process_video( input_video_path=video_path, output_video_path=video_path, prompt=" " ) except Exception as e: logger.error(f"Falha na masterização HD: {e}. Usando vídeo de qualidade padrão.") else: logger.info("Etapa de edicao HD desativada.") logger.info(f"Processo concluído! Vídeo final salvo em: {video_path}") yield {"final_path": video_path} 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 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