Update api/ltx_server.py

api/ltx_server.py

@@ -707,13 +707,22 @@ class VideoService:
                 single_pass_kwargs = call_kwargs.copy()
                 first_pass_config = self.config.get("first_pass", {}).copy()
                 
-                #single_pass_kwargs.update(
-                #    {
+                single_pass_kwargs.update(
+                    {
+                        "skip_final_inference_steps": first_pass_config.get("skip_final_inference_steps"),
+                        "stg_scale": first_pass_config.get("stg_scale"),
+                        "stg_scale": first_pass_config.get("stg_scale"),
+                        "rescaling_scale": first_pass_config.get("rescaling_scale"),
+                        "guidance_timesteps": first_pass_config.get("guidance_timesteps"),
+                        "skip_block_list": first_pass_config.get("skip_block_list"),
+                        "num_inference_steps": first_pass_config.get("num_inference_steps"),
+                        "skip_final_inference_steps": first_pass_config.get("skip_final_inference_steps"),
+                        "cfg_star_rescale": first_pass_config.get("cfg_star_rescale"),
                         #"guidance_scale": float(guidance_scale),
                         #"stg_scale": first_pass_config.get("stg_scale"),
                         #"rescaling_scale": first_pass_config.get("rescaling_scale"),
                         #"skip_block_list": first_pass_config.get("skip_block_list"),
-                #    }
+                    }
                 #)
                 #schedule = first_pass_config.get("timesteps") or first_pass_config.get("guidance_timesteps")
                 #if mode == "video-to-video":
@@ -736,8 +745,54 @@ class VideoService:
                     latents = result.images
                 else:
                     latents = result
-                print(f"[DEBUG] Latentes (single-pass): shape={tuple(latents.shape)}")
+                print(f"[DEBUG] Latentes (single-pass) first : shape={tuple(latents.shape)}")
 
+                single_pass_kwargs = call_kwargs.copy()
+                first_pass_config = self.config.get("first_pass", {}).copy()
+                
+                single_pass_kwargs.update(
+                    {
+                        "latents" : latents,
+                        "skip_final_inference_steps": second_pass.get("skip_final_inference_steps"),
+                        "stg_scale": second_pass.get("stg_scale"),
+                        "stg_scale": second_pass.get("stg_scale"),
+                        "rescaling_scale": second_pass.get("rescaling_scale"),
+                        "guidance_timesteps": second_pass.get("guidance_timesteps"),
+                        "skip_block_list": second_pass.get("skip_block_list"),
+                        "num_inference_steps": second_pass.get("num_inference_steps"),
+                        "skip_final_inference_steps": second_pass.get("skip_final_inference_steps"),
+                        "cfg_star_rescale": second_pass.get("cfg_star_rescale"),
+                        #"guidance_scale": float(guidance_scale),
+                        #"stg_scale": first_pass_config.get("stg_scale"),
+                        #"rescaling_scale": first_pass_config.get("rescaling_scale"),
+                        #"skip_block_list": first_pass_config.get("skip_block_list"),
+                    }
+                #)
+                #schedule = first_pass_config.get("timesteps") or first_pass_config.get("guidance_timesteps")
+                #if mode == "video-to-video":
+                #    schedule = [0.7]; print("[INFO] Modo video-to-video (etapa única): timesteps=[0.7]")
+                #if isinstance(schedule, (list, tuple)) and len(schedule) > 0:
+                #    single_pass_kwargs["timesteps"] = schedule
+                #    single_pass_kwargs["guidance_timesteps"] = schedule
+                #print(f"[DEBUG] Single-pass: timesteps_len={len(schedule) if schedule else 0}")
+
+                #print("\n[INFO] Executando pipeline de etapa única...")
+                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)
+                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 (single-pass) segunda: shape={tuple(latents.shape)}")
+                
+
+            
             # Staging e escrita MP4 (simples: VAE → pixels → MP4)
             
             latents_cpu = latents.detach().to("cpu", non_blocking=True)