_target_: pfp.policy.ddim_policy.DDIMPolicy
  x_dim: ${x_dim}
  y_dim: ${y_dim}
  n_obs_steps: ${n_obs_steps}
  n_pred_steps: ${n_pred_steps}
  num_k_train: 100
  num_k_infer: 10
  norm_pcd_center: [0.4, 0.0, 1.4]
  augment_data: False

  obs_encoder: ${backbone}
  
  diffusion_net:
    _target_: diffusion_policy.model.diffusion.conditional_unet1d.ConditionalUnet1D
    input_dim: ${y_dim}
    global_cond_dim: "${eval: '${x_dim} * ${n_obs_steps}'}"
    diffusion_step_embed_dim: 256
    down_dims: [256, 512, 1024]
    kernel_size: 5
    n_groups: 8
    cond_predict_scale: True
  
  noise_scheduler_train:
    _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler
    num_train_timesteps: ${model.num_k_train}
    beta_start: 0.0001
    beta_end: 0.02
    beta_schedule: squaredcos_cap_v2
    clip_sample: True
    set_alpha_to_one: True
    steps_offset: 0
    prediction_type: epsilon
    # rescale_betas_zero_snr: True
    # prediction_type: v_prediction
    # timestep_spacing: trailing

  loss_weights:
    xyz: 10.0
    rot6d: 10.0
    grip: 1.0