Update api/ltx_server.py

api/ltx_server.py

@@ -744,6 +744,8 @@ class VideoService:
 
         latents = None
         latents_list = []
+        results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
+            
 
         try:
             ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext()
@@ -803,8 +805,7 @@ class VideoService:
                          pass
                 
                     latents_parts_up = self._dividir_latentes_por_tamanho(latents_cpu_up,4,1)
-                    results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
-            
+                    
                     for latents in latents_parts_up:
                
                         # # --- ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) ---
@@ -818,12 +819,17 @@ class VideoService:
                         print(f"[DEBUG] Second Pass Dims: Target ({second_pass_width}x{second_pass_height})")
                         # --- <FIM DA LÓGICA DE CÁLCULO EXATA> --- 
                         t_pass2 = time.perf_counter()
-                    
+
+                        vae_temporal_scale = self.pipeline.video_scale_factor # Geralmente 4 ou 8
+                        num_pixel_frames_part = ((latents.shape[2] - 1) * vae_temporal_scale) + 1
+                        print(f"[DEBUG] Parte {i+1}: {latents.shape[2] - 1} latentes -> {num_pixel_frames_part} frames de pixel (alvo)")
+                       
                         second_pass_kwargs = call_kwargs.copy()
                         second_pass_kwargs.update({
                            "output_type": "latent",
                            "width": second_pass_width,
                            "height": second_pass_height,
+                           "num_frames": num_pixel_frames_part
                            "latents": upsampled_latents, # O tensor upscaled
                            "guidance_scale": float(guidance_scale),
                            **second_pass_config
@@ -863,19 +869,17 @@ class VideoService:
             latents_parts = []
             for latents in latents_list:
                 latents_parts.append(self._dividir_latentes_por_tamanho(latents,4,1))
-                results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
-
+                
            
             partes_mp4 = []
             par = 0
             for latents in latents_parts:
-                print(f"[DEBUG] Partição {par}: {tuple(latents.shape)}")
-       
+                
                 par = par + 1
                 output_video_path = os.path.join(results_dir, f"output_{used_seed}_{par}.mp4")
                 final_output_path = None
     
-                print("[DEBUG] Decodificando bloco de latentes com VAE → tensor de pixels...")
+                print("[DEBUG] Decodificando bloco de latentes com VAE {par} → tensor de pixels...")
                 # Usar manager com timestep por item; previne target_shape e rota NoneType.decode
                 pixel_tensor = vae_manager_singleton.decode(
                     latents.to(self.device, non_blocking=True),