Update deformes4D_engine.py

deformes4D_engine.py

@@ -21,6 +21,7 @@ import gradio as gr
 import subprocess
 import gc
 
+
 from audio_specialist import audio_specialist_singleton
 from ltx_manager_helpers import ltx_manager_singleton
 from gemini_helpers import gemini_singleton 
@@ -170,12 +171,6 @@ class Deformes4DEngine:
         keyframe_paths = [item[0] if isinstance(item, tuple) else item for item in keyframes]
         story_history = ""
         
-        expected_height, expected_width = 768, 1152
-        downscale_factor = 2 / 3
-        downscaled_height = self._quantize_to_multiple(int(expected_height * downscale_factor), 8)
-        downscaled_width = self._quantize_to_multiple(int(expected_width * downscale_factor), 8)
-        target_resolution_tuple = (downscaled_height, downscaled_width)
-        final_resolution_tuple = (expected_height, expected_width)
         
         eco_latent_for_next_loop = None
         dejavu_latent_for_next_loop = None
@@ -235,16 +230,61 @@ 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())}"
-        
-        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
+
+
+        # Pós-produção: Upscale + Refine
+        high_quality_video_path = self._render_and_post_process(
+            final_concatenated_latents,
+            base_name=base_name,
+            expected_height=720,
+            expected_width=720,
+            fps=24
         )
+
+        
+        #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": video_with_audio_path}
+        yield {"final_path": high_quality_video_path}
+
+
 
+    def _render_and_post_process(self, final_concatenated_latents: torch.Tensor, 
+                                 base_name: str, expected_height: int, expected_width: int, fps: int = 24) -> str:
+        """
+        Aplica upscale + refinamento nos latentes finais e salva o vídeo em alta qualidade.
+        """
+        logger.info("Iniciando pós-processamento: upscale + refinamento...")
+
+        # --- 1. Upscale espacial com LTX Upscaler ---
+        upscaled_latents = upscaler_specialist_singleton.upscale_latents(
+            latents=final_concatenated_latents,
+            factor=2,   # geralmente o upscaler dobra H e W
+            output_type="latent"
+        )
+        logger.info(f"Upscale concluído: shape {list(upscaled_latents.shape)}")
+
+        # --- 2. Refinamento difusivo curto ---
+        refined_latents, _ = ltx_manager_singleton.refine_latents(
+            upscaled_latents,
+            denoise_strength=0.4,
+            refine_steps=10
+        )
+        logger.info("Refinamento concluído.")
+
+        # --- 3. Decodificação para pixels ---
+        pixel_tensor = self.latents_to_pixels(refined_latents)
+        video_path = os.path.join(self.workspace_dir, f"{base_name}_HQ.mp4")
+        self.save_video_from_tensor(pixel_tensor, video_path, fps=fps)
+
+        logger.info(f"Vídeo final salvo em: {video_path}")
+        return video_path
+        
+    
     def _generate_latent_tensor_internal(self, conditioning_items, ltx_params, target_resolution, total_frames_to_generate):
         kwargs = {
             **ltx_params, 'width': target_resolution[0], 'height': target_resolution[1],