Update api/ltx_server.py

api/ltx_server.py

@@ -853,7 +853,7 @@ class VideoService:
                     print(f"[DEBUG] Upscale de Latentes concluído em {time.perf_counter() - t_upscale:.2f}s")
                     del base_latents; gc.collect(); torch.cuda.empty_cache()
 
-                    par = 0
+                    
                     latents_cpu_up = upsampled_latents.detach().to("cpu", non_blocking=True)
                     torch.cuda.empty_cache()
                     try:
@@ -872,7 +872,7 @@ class VideoService:
 
                     #latents_parts_up = [latents_cpu_up]
 
-                    
+                    par = 0
                     for latents in latents_parts_up:
                
                         # # --- ETAPA 3: REFINAMENTO DE TEXTURA (SECOND PASS) ---
@@ -896,28 +896,29 @@ class VideoService:
                        
                         second_pass_kwargs = call_kwargs.copy()
 
-                        if i==0:
-                          second_pass_kwargs.update({
-                           "conditioning_items": conditioning_items1
-                           "output_type": "latent",
-                           "width": second_pass_width,
-                           "height": second_pass_height,
-                           "num_frames": num_pixel_frames_part,
-                           "latents": latents, # O tensor upscaled
-                           "guidance_scale": float(guidance_scale),
-                           **second_pass_config
-                          })
+                        if par==0:
+                            second_pass_kwargs.update({
+                               "conditioning_items": conditioning_items1,
+                               "output_type": "latent",
+                               "width": second_pass_width,
+                               "height": second_pass_height,
+                               "num_frames": num_pixel_frames_part,
+                               "latents": latents, # O tensor upscaled
+                               "guidance_scale": float(guidance_scale),
+                               **second_pass_config
+                            })
                         else:
-                          second_pass_kwargs.update({
-                           "conditioning_items": conditioning_items2
-                           "output_type": "latent",
-                           "width": second_pass_width,
-                           "height": second_pass_height,
-                           "num_frames": num_pixel_frames_part,
-                           "latents": latents, # O tensor upscaled
-                           "guidance_scale": float(guidance_scale),
-                           **second_pass_config
-                          })
+                            second_pass_kwargs.update({
+                               "conditioning_items": conditioning_items2,
+                               "output_type": "latent",
+                               "width": second_pass_width,
+                               "height": second_pass_height,
+                               "num_frames": num_pixel_frames_part,
+                               "latents": latents, # O tensor upscaled
+                               "guidance_scale": float(guidance_scale),
+                               **second_pass_config
+                            })
+                        par+=1
                     
                         print(f"[DEBUG] Second Pass: Refinando em {width_padded}x{height_padded}...")
                         final_latents = self.pipeline(**second_pass_kwargs).images