name: "diffusionGS/diffusionGS-obj"

system_type: "diffusion-gs-system"
system:
  num_inference_steps: 30

  shape_model_type: "diffusion-gs-model"
  shape_model:
    width: 1024
    in_channels: 9 #rgb+plucker
    patch_size: 8
    n_gaussians: 2
    dim_heads: 64
    num_layers: 24
    prior_distribution: 'gaussian' #sphere_gaussian
    use_flash: true
    use_checkpoint: true

  noise_scheduler_type: "diffusionGS.models.scheduler.ddim_scheduler.DDIMScheduler"
  noise_scheduler:
    num_train_timesteps: 1000
    prediction_type: "sample"

  loss:
    loss_type: "mse"
    lambda_diffusion: 1. #[150, 0., 1., 151] #1.
    lambda_lpips: 0.5 #[150, 0., 0.5, 151]
    lambda_ssim: 0.0
    lambda_pointsdist: 0.0 #[150, 1., 0., 151]
    lambda_xyz: 0.025 #[100, 0., 1., 101]
    lambda_depth: 0. #[100, 0., 0.25, 101]

  optimizer:
    name: AdamW
    args:
      lr: 1.e-5
      betas: [0.9, 0.99]
      eps: 1.e-8

  scheduler:
    name: CosineAnnealingLR
    args:
      T_max: 500000
      eta_min: 1e-6