Update pipeline.py

pipeline.py

@@ -975,6 +975,23 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                     # select the relevent context from the latents
                     current_context_latents = latents[:, :, current_context_start : current_context_start + context_size, :, :]
                 
+                
+                # for context_group in range(num_context_groups):
+                #     # Calculate the current start index, considering overlap
+                #     current_context_start = 0 if context_group == 0 else context_group * (context_size - overlap)
+
+                #     # Calculate the end index and adjust if it exceeds num_frames
+                #     current_context_end = (current_context_start + context_size) % num_frames
+
+                #     # Select the relevant context from the latents with wrap-around handling
+                #     current_context_latents = torch.cat([
+                #         latents[:, :, current_context_start:min(current_context_start + context_size, num_frames), :, :],
+                #         latents[:, :, :max(current_context_end - num_frames, 0), :, :]
+                #     ], dim=2) if current_context_start + context_size > num_frames else latents[:, :, current_context_start:current_context_start + context_size, :, :]
+                    
+
+                
+                
                     # 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)