Upload deformes4D_engine_1756425074949.py

deformes4D_engine_1756425074949.py

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+# 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
+
+    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,
+                            base_ltx_params: dict,
+                            video_resolution: int, 
+                            use_continuity_director: bool, 
+                            progress: gr.Progress = gr.Progress()):
+
+        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)
+        #echo_frames = 8
+
+        previous_latents_path = 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 = previous_latents_path is None
+            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: # Assume-se que este checkbox controla os diretores de vídeo e som
+                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 # Fallback para o prompt da cena se o diretor de som estiver desligado
+
+            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
+
+            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:
+                previous_latents = self.load_latent_tensor(previous_latents_path)
+                handler_latent = previous_latents[:, :, -1:, :, :]
+                trimmed_for_echo = previous_latents[:, :, :-n_trim_latents, :, :] if n_trim_latents > 0 and previous_latents.shape[2] > n_trim_latents else previous_latents
+                echo_latents = trimmed_for_echo[:, :, -echo_frames:, :, :]
+                handler_frame_position = n_trim_latents + echo_frames
+
+
+                conditioning_items.append(LatentConditioningItem(echo_latents, 0, 1.0))
+                conditioning_items.append(LatentConditioningItem(handler_latent, handler_frame_position, handler_strength))
+                del previous_latents, handler_latent, trimmed_for_echo, echo_latents; gc.collect()
+                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)
+
+            base_name = f"fragment_{i}_{int(time.time())}"
+            new_full_latents_path = os.path.join(self.workspace_dir, f"{base_name}_full.pt")
+            self.save_latent_tensor(new_full_latents, new_full_latents_path)
+
+            previous_latents_path = new_full_latents_path
+
+            latents_for_video = new_full_latents
+
+            # Aplicar cortes apenas onde necessário para evitar duplicação
+            if not is_first_fragment:
+                # Remove apenas o eco do início para evitar duplicação com fragmento anterior
+                if echo_frames > 0 and latents_for_video.shape[2] > echo_frames: 
+                    latents_for_video = latents_for_video[:, :, echo_frames:, :, :]
+
+            # Para todos os fragmentos exceto o último, remove sobreposição do final
+            is_last_fragment = (i == num_transitions_to_generate - 1)
+            if not is_last_fragment:
+                if n_trim_latents > 0 and latents_for_video.shape[2] > n_trim_latents: 
+                    latents_for_video = latents_for_video[:, :, :-n_trim_latents, :, :]
+
+            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":
+                previous_latents_path = 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