Use function

app.py

@@ -749,6 +749,38 @@ def worker_start_end(input_image, end_image, image_position, prompts, n_prompt,
             def callback(d):
                 return
 
+        def post_process(job_id, start_latent, generated_latents, total_generated_latent_frames, history_latents, high_vram, transformer, gpu, vae, history_pixels, latent_window_size, enable_preview, outputs_folder, mp4_crf, stream, is_last_section):
+            if is_last_section:
+                generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2)
+            
+            total_generated_latent_frames += int(generated_latents.shape[2])
+            history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2)
+            
+            if not high_vram:
+                offload_model_from_device_for_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=8)
+                load_model_as_complete(vae, target_device=gpu)
+            
+            if history_pixels is None:
+                history_pixels = vae_decode(history_latents[:, :, :total_generated_latent_frames, :, :], vae).cpu()
+            else:
+                section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)
+                overlapped_frames = latent_window_size * 4 - 3
+            
+                current_pixels = vae_decode(history_latents[:, :, :min(total_generated_latent_frames, section_latent_frames)], vae).cpu()
+                history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames)
+            
+            if not high_vram:
+                unload_complete_models(vae)
+            
+            if enable_preview or is_last_section:
+                output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
+                
+                save_bcthw_as_mp4(history_pixels, output_filename, fps=fps_number, crf=mp4_crf)
+                
+                print(f'Decoded. Pixel shape {history_pixels.shape}')
+                
+                stream.output_queue.push(('file', output_filename))
+
         for latent_padding in latent_paddings:
             is_last_section = latent_padding == 0
             is_first_section = latent_padding == latent_paddings[0]
@@ -815,36 +847,7 @@ def worker_start_end(input_image, end_image, image_position, prompts, n_prompt,
                 callback=callback,
             )
 
-            if is_last_section:
-                generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2)
-
-            total_generated_latent_frames += int(generated_latents.shape[2])
-            history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2)
-
-            if not high_vram:
-                offload_model_from_device_for_memory_preservation(transformer, target_device=gpu, preserved_memory_gb=8)
-                load_model_as_complete(vae, target_device=gpu)
-
-            if history_pixels is None:
-                history_pixels = vae_decode(history_latents[:, :, :total_generated_latent_frames, :, :], vae).cpu()
-            else:
-                section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)
-                overlapped_frames = latent_window_size * 4 - 3
-
-                current_pixels = vae_decode(history_latents[:, :, :min(total_generated_latent_frames, section_latent_frames)], vae).cpu()
-                history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames)
-
-            if not high_vram:
-                unload_complete_models(vae)
-
-            if enable_preview or is_last_section:
-                output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
-                
-                save_bcthw_as_mp4(history_pixels, output_filename, fps=fps_number, crf=mp4_crf)
-                
-                print(f'Decoded. Pixel shape {history_pixels.shape}')
-                
-                stream.output_queue.push(('file', output_filename))
+            [total_generated_latent_frames, history_latents, history_pixels] = post_process(job_id, start_latent, generated_latents, total_generated_latent_frames, history_latents, high_vram, transformer, gpu, vae, history_pixels, latent_window_size, enable_preview, outputs_folder, mp4_crf, stream, is_last_section)
 
             if is_last_section:
                 break