During the inference phase ,there were copy mismatches in the CFG and errors in the noise sampling sapce.

[Jerry-Auto]

1. The dataset uses repeat_interleave,but flag and x are directly repeated,resulting in inconsisitent coping.for example,[1,2,3,4] to[1,1,2,2,3,3,4,4] and[1,2,3,4,1,2,3,4],mismatched.
2. the initial noise sampling space must be consistent with that used during training;that is ,sampling must be performed before segmentation,and then segmentation must occur, rather than sampling directly,in the segmented sapce.
3. x_init = torch.randn((B, self.action_num, self.planner_params['action_len'], self.planner_params['state_dim']), device=self.device)
   to x_0 = torch.randn((B,1,self.planner_params['future_len'], self.planner_params['state_dim']), device=self.device)
   x_init = traj_chunking(x_0, self.planner_params['action_len'], self.planner_params['action_overlap'])
   x_init = torch.cat(x_init, dim=1)

[hanzh0816]

@Jerry-Auto Hi, I noticed the same issue with repeat_interleave as well. It seems to affect the training process (specifically, the batched data), whereas it does not occur during inference. Have you tested training the model with the cond/uncond data generated by repeat_interleave strictly following the original repository's code? If so, how did it perform, and did it achieve the performance gains claimed in the paper?