Update api/ltx_server.py

api/ltx_server.py

@@ -632,21 +632,15 @@ class VideoService:
                 t_sp = time.perf_counter()
                 ctx = torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype) if self.device == "cuda" else contextlib.nullcontext()
                 with ctx:
-                    result = self.pipeline(**single_pass_kwargs)
+                    latents = self.pipeline(**single_pass_kwargs).frames
                 print(f"[DEBUG] single-pass tempo={time.perf_counter()-t_sp:.3f}s")
 
-                if hasattr(result, "latents"):
-                    latents = result.latents
-                elif hasattr(result, "images") and isinstance(result.images, torch.Tensor):
-                    latents = result.images
-                else:
-                    latents = result
-                #print(f"[DEBUG] Latentes (first_pass_kwargs): shape={tuple(latents.shape)}")
+                
+                print(f"[DEBUG] Latentes (first_pass_kwargs): shape={tuple(latents.shape)}")
                     
-                latents_low_res = latents
-                print(f"[DEBUG] Passo 1 concluído. Shape dos latentes de baixa resolução: {latents_low_res.shape}")
+
                 
-                del result, single_pass_kwargs
+                del single_pass_kwargs
                 gc.collect()
                 if self.device == "cuda": torch.cuda.empty_cache()
 
@@ -654,10 +648,12 @@ class VideoService:
                 print("[DEBUG] Multi-escala: Fazendo upscale dos latentes com latent_upsampler.")
                 with ctx:
                     latents_high_res = self.latent_upsampler(
-                        latents=latents_low_res,
+                        latents=latents,
                         output_height=original_height,
                         output_width=original_width,
-                    )
+                        output_type="latent"
+                    ).frames
+
                 log_tensor_info(latents_high_res, "Latentes (Pós-Upscale)")
                 del latents_low_res
                 gc.collect()