Update pipeline.py

pipeline.py

@@ -775,24 +775,16 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
 
         # save frames
         if output_path is not None:
-            output_batch_size = 2# prevents out of memory errors with large videos
-            num_frames = latents.size(2)  # latents' shape is [batch, channels, frames, height, width]
+            output_batch_size = 2 # prevents out of memory errors with large videos
             num_digits = output_path.count('#')  # count the number of '#' characters
             frame_format = output_path.replace('#' * num_digits, '{:0' + str(num_digits) + 'd}')
-            
             for batch in range((num_frames + output_batch_size - 1) // output_batch_size):
                 start_id = batch * output_batch_size
                 end_id = min((batch + 1) * output_batch_size, num_frames)
                 video_tensor = self.decode_latents(latents[:, :, start_id:end_id, :, :])
                 video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
-                frame_list = video[0]
-                for f_id, frame in enumerate(frame_list):
-                    # print frame and batch id
-                    print(f"Frame: {f_id + start_id} Batch: {batch}")
-                    frame_number = start_id + f_id
-                    output_file = frame_format.format(frame_number)
-                    print(f"Saving to: {output_file}")
-                    frame.save(frame_format.format(frame_number))
+                for f_id, frame in enumerate(video[0]):
+                    frame.save(frame_format.format(start_id + f_id))
             return output_path
             
         # Post-processing