Update pipeline.py

pipeline.py

@@ -1049,7 +1049,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                         
                     
                     # set the step index to the current batch
-                    self.scheduler._step_index = i
+                    # self.scheduler._step_index = i
                     
                     # compute the previous noisy sample x_t -> x_t-1
                     current_context_latents = self.scheduler.step(noise_pred, t, current_context_latents, **extra_step_kwargs).prev_sample
@@ -1079,8 +1079,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                 
                 # shuffle rotate latent images by step places, wrapping around the last 2 to the start
                 latents = torch.cat([latents[:, :, -step:, :, :], latents[:, :, :-step, :, :]], dim=2)
-
-        print("Done denoising")
         
         if output_type == "latent":
             return AnimateDiffPipelineOutput(frames=latents)