Update deformes4D_engine.py

deformes4D_engine.py

@@ -126,55 +126,7 @@ class Deformes4DEngine:
             raise gr.Error(f"Falha na montagem final do vídeo. Detalhes: {e.stderr}")
         return output_path
 
-    # --- PIPELINE DE PÓS-PRODUÇÃO LATENTE ---
-    def _render_and_post_process_latents(self, 
-                                     low_res_latents: torch.Tensor, 
-                                     base_name: str,
-                                     motion_prompt_for_refine: str,
-                                     final_resolution: tuple
-                                    ) -> str:
-        logger.info(f"--- INICIANDO PIPELINE DE PÓS-PRODUÇÃO PARA '{base_name}' ---")
-        
-        high_res_latents = upscaler_specialist_singleton.upscale(low_res_latents)
-        
-        _, _, _, refined_h_latent, refined_w_latent = high_res_latents.shape
-        video_h = refined_h_latent * self.ltx_manager.workers[0].pipeline.vae_scale_factor
-        video_w = refined_w_latent * self.ltx_manager.workers[0].pipeline.vae_scale_factor
-        num_latent_frames = high_res_latents.shape[2]
-        num_video_frames = num_latent_frames * self.ltx_manager.workers[0].pipeline.video_scale_factor
-        if isinstance(self.vae, CausalVideoAutoencoder):
-            num_video_frames -= 1
-        
-        refine_kwargs = {
-            'height': video_h, 'width': video_w, 'video_total_frames': num_video_frames, 'video_fps': 24,
-            'current_fragment_index': int(time.time()), 'motion_prompt': motion_prompt_for_refine,
-            'conditioning_items_data': [],
-            'denoise_strength': 0.4, 'refine_steps': 10
-        }
-
-        final_latents, _ = self.ltx_manager.refine_latents(high_res_latents, **refine_kwargs)
-
-        untrimmed_video_path = os.path.join(self.workspace_dir, f"{base_name}_untrimmed.mp4")
-        trimmed_video_path = os.path.join(self.workspace_dir, f"{base_name}.mp4")
-
-        pixel_tensor = self.latents_to_pixels(final_latents)
-        
-        logger.info(f"Redimensionando vídeo final de {pixel_tensor.shape[-2:]} para {final_resolution}")
-        pixel_tensor_resized = torch.nn.functional.interpolate(
-            pixel_tensor.squeeze(0), size=final_resolution, mode='bilinear', align_corners=False
-        ).unsqueeze(0)
-
-        self.save_video_from_tensor(pixel_tensor_resized, untrimmed_video_path, fps=24)
-        del pixel_tensor, final_latents, high_res_latents, pixel_tensor_resized
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        success = self._trim_last_frame_ffmpeg(untrimmed_video_path, trimmed_video_path)
-        
-        if os.path.exists(untrimmed_video_path):
-            os.remove(untrimmed_video_path)
-        
-        return trimmed_video_path if success else untrimmed_video_path
+ 
 
     def _generate_video_and_audio(self, silent_video_path: str, audio_prompt: str, base_name: str) -> str:
         try:
@@ -283,21 +235,15 @@ class Deformes4DEngine:
         
         progress((num_transitions_to_generate + 1) / (num_transitions_to_generate + 2), desc="Pós-produção (Upscale e Refinamento)...")
         base_name = f"final_movie_hq_{int(time.time())}"
-        silent_video_path = self._render_and_post_process_latents(
-            low_res_latents=final_concatenated_latents,
-            base_name=base_name,
-            motion_prompt_for_refine=global_prompt,
-            final_resolution=final_resolution_tuple
+        
+        progress((num_transitions_to_generate + 1.5) / (num_transitions_to_generate + 2), desc="Gerando paisagem sonora...")
+        video_with_audio_path = self._generate_video_and_audio(
+            silent_video_path=silent_video_path,
+            audio_prompt=global_prompt,
+            base_name=base_name
         )
-
-        #progress((num_transitions_to_generate + 1.5) / (num_transitions_to_generate + 2), desc="Gerando paisagem sonora...")
-        #video_with_audio_path = self._generate_video_and_audio(
-        #    silent_video_path=silent_video_path,
-        #    audio_prompt=global_prompt,
-        #    base_name=base_name
-        #)
         
-        yield {"final_path": silent_video_path}
+        yield {"final_path": video_with_audio_path}
 
     def _generate_latent_tensor_internal(self, conditioning_items, ltx_params, target_resolution, total_frames_to_generate):
         kwargs = {