Update pipeline.py

pipeline.py

@@ -1540,7 +1540,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                 # if we had more then one prompt, we need to offset the video frames back by number of inference steps
                 if len(prompt_embeds_list) > 1:
                     # wrap the first n number of frames to the end of the video to fix the offseting from the context scheduler
-                    video_tensor = torch.cat((video_tensor[:, :, num_inference_steps:, :, :], video_tensor[:, :, :num_inference_steps, :, :]), dim=2)
+                    offset = num_inference_steps * 2
+                    video_tensor = torch.cat((video_tensor[:, :, offset:, :, :], video_tensor[:, :, :offset, :, :]), dim=2)
                 video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
                 for f_id, frame in enumerate(video[0]):
                     frame.save(frame_format.format(start_id + f_id))