Update api/ltx_server.py

api/ltx_server.py

@@ -671,7 +671,7 @@ class VideoService:
         multi_scale_pipeline = None
 
         try:
-            if improve_texture:
+            if true: #improve_texture:
                 if not self.latent_upsampler:
                     raise ValueError("Upscaler espacial não carregado.")
                 print("[DEBUG] Multi-escala: construindo pipeline...")
@@ -686,7 +686,7 @@ class VideoService:
                     {
                         "downscale_factor": self.config["downscale_factor"],
                         "first_pass": first_pass_args,
-                        "second_pass": second_pass_args,
+                        "second_pass": first_pass_args,
                     }
                 )
                 print("[DEBUG] Chamando multi_scale_pipeline...")
@@ -703,7 +703,8 @@ class VideoService:
                 else:
                     latents = result
                 print(f"[DEBUG] Latentes (multi-escala): shape={tuple(latents.shape)}")
-            else:
+            
+            #if true:
                 single_pass_kwargs = call_kwargs.copy()
                 first_pass_config = self.config.get("first_pass", {}).copy()
                 
@@ -726,7 +727,9 @@ class VideoService:
                         #"skip_block_list": first_pass_config.get("skip_block_list"),
                     }
                 )
-                #schedule = first_pass_config.get("timesteps") or first_pass_config.get("guidance_timesteps")
+                #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:
@@ -734,20 +737,20 @@ class VideoService:
                 #    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) first : shape={tuple(latents.shape)}")
+                #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) first : shape={tuple(latents.shape)}")
 
                 single_pass_kwargs = call_kwargs.copy()
                 first_pass_config = self.config.get("first_pass", {}).copy()
@@ -764,7 +767,7 @@ class VideoService:
                         "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": 1 #first_pass_config.get("skip_final_inference_steps"),
+                        "skip_final_inference_steps": 0 #first_pass_config.get("skip_final_inference_steps"),
                         "skip_initial_inference_steps": 16# second_pass.get("skip_initial_inference_steps"),
                         "cfg_star_rescale": second_pass.get("cfg_star_rescale"),
                         "downscale_factor": self.config["downscale_factor"],
@@ -775,15 +778,15 @@ class VideoService:
                         #"skip_block_list": first_pass_config.get("skip_block_list"),
                     }
                 )
-                #schedule = first_pass_config.get("timesteps") or first_pass_config.get("guidance_timesteps")
+                schedule = second_pass.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}")
+                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...")
+                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: