Update worker_runpod.py

worker_runpod.py

@@ -130,17 +130,11 @@ def generate(input):
     closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
     height, width = [int(x / 16) * 16 for x in closest_size]
     sample_size = [height, width]
-    if partial_video_length is not None:
-        # Handle ultra-long video generation if required
-        # ... (existing logic for partial video generation)
-    else:
-        # Standard video generation
-        video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
-        input_video, input_video_mask, clip_image = get_image_to_video_latent(downloaded_image_path, validation_image_end, video_length=video_length, sample_size=sample_size)
+    video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
+    input_video, input_video_mask, clip_image = get_image_to_video_latent(downloaded_image_path, validation_image_end, video_length=video_length, sample_size=sample_size)
         
-        with torch.no_grad():
-            sample = pipeline(
-                prompt=prompt,
+    with torch.no_grad():
+    sample = pipeline(prompt=prompt,
                 num_frames=video_length,
                 negative_prompt=negative_prompt,
                 height=sample_size[0],
@@ -149,8 +143,7 @@ def generate(input):
                 guidance_scale=guidance_scale,
                 num_inference_steps=num_inference_steps,
                 video=input_video,
-                mask_video=input_video_mask
-            ).videos
+                mask_video=input_video_mask).videos        
     
     if not os.path.exists(save_path):
         os.makedirs(save_path, exist_ok=True)