model:
  base_learning_rate: 1.0e-04
  target: extern.ldm_zero123.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "image_target"
    cond_stage_key: "image_cond"
    image_size: 32
    channels: 4
    cond_stage_trainable: false   # Note: different from the one we trained before
    conditioning_key: hybrid
    monitor: val/loss_simple_ema
    scale_factor: 0.18215

    scheduler_config: # 10000 warmup steps
      target: extern.ldm_zero123.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 100 ]
        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
        f_start: [ 1.e-6 ]
        f_max: [ 1. ]
        f_min: [ 1. ]

    unet_config:
      target: extern.ldm_zero123.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32 # unused
        in_channels: 8
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_heads: 8
        use_spatial_transformer: True
        transformer_depth: 1
        context_dim: 768
        use_checkpoint: True
        legacy: False

    first_stage_config:
      target: extern.ldm_zero123.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: extern.ldm_zero123.modules.encoders.modules.FrozenCLIPImageEmbedder


# data:
#   target: extern.ldm_zero123.data.simple.ObjaverseDataModuleFromConfig
#   params:
#     root_dir: 'views_whole_sphere'
#     batch_size: 192
#     num_workers: 16
#     total_view: 4
#     train:
#       validation: False
#       image_transforms:
#         size: 256

#     validation:
#       validation: True
#       image_transforms:
#         size: 256


# lightning:
#   find_unused_parameters: false
#   metrics_over_trainsteps_checkpoint: True
#   modelcheckpoint:
#     params:
#       every_n_train_steps: 5000
#   callbacks:
#     image_logger:
#       target: main.ImageLogger
#       params:
#         batch_frequency: 500
#         max_images: 32
#         increase_log_steps: False
#         log_first_step: True
#         log_images_kwargs:
#           use_ema_scope: False
#           inpaint: False
#           plot_progressive_rows: False
#           plot_diffusion_rows: False
#           N: 32
#           unconditional_scale: 3.0
#           unconditional_label: [""]

#   trainer:
#     benchmark: True
#     val_check_interval: 5000000 # really sorry
#     num_sanity_val_steps: 0
#     accumulate_grad_batches: 1