Update pipeline.py

pipeline.py

@@ -1416,8 +1416,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
         
                 # foreach context group seperately denoise the current timestep
                 for context_group in range(len(context_indexes[i])):
-                    # calculate to current indexes, considering overlap
-                    prep_time = time.time()
+                    # calculate to current indexes, considering overlapa
                     current_context_indexes = context_indexes[i][context_group]
 
                     # select the relevent context from the latents
@@ -1426,7 +1425,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = torch.cat([current_context_latents] * 2) if do_classifier_free_guidance else current_context_latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                    print("prep time", time.time() - prep_time)
                     
                     
                     if self.controlnet != None and i < int(control_end*len(timesteps)):
@@ -1485,7 +1483,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                         print("unet time", time.time() - unet_start)
                     
                     else:
-                        unet_start = time.time()
                         # predict the noise residual without contorlnet
                         noise_pred = self.unet(
                             latent_model_input,
@@ -1494,7 +1491,6 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                             cross_attention_kwargs=cross_attention_kwargs,
                             added_cond_kwargs=added_cond_kwargs,
                         ).sample
-                        print("unet time", time.time() - unet_start)
 
                     # sum the noise predictions for the unconditional and text conditioned noise
                     start_guidance_time = time.time()