model:
  base_learning_rate: 1.0e-04
  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
  params:
    parameterization: "v"
    low_scale_key: "lr"
    linear_start: 0.0001
    linear_end: 0.02
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 128
    channels: 4
    cond_stage_trainable: false
    conditioning_key: "hybrid-adm"
    monitor: val/loss_simple_ema
    scale_factor: 0.08333
    use_ema: False

    low_scale_config:
      noise_schedule_config: # image space
        linear_start: 0.0001
        linear_end: 0.02
      max_noise_level: 350

    unet_config:
      use_checkpoint: True
      num_classes: 1000  # timesteps for noise conditioning (here constant, just need one)
      image_size: 128
      in_channels: 7
      out_channels: 4
      model_channels: 256
      attention_resolutions: [ 2,4,8]
      num_res_blocks: 2
      channel_mult: [ 1, 2, 2, 4]
      disable_self_attentions: [True, True, True, False]
      disable_middle_self_attn: False
      num_heads: 8
      use_spatial_transformer: True
      transformer_depth: 1
      context_dim: 1024
      legacy: False
      use_linear_in_transformer: True

    first_stage_config:
      embed_dim: 4
      ddconfig:
        # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
        double_z: True
        z_channels: 4
        resolution: 256
        in_channels: 3
        out_ch: 3
        ch: 128
        ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
        num_res_blocks: 2
        attn_resolutions: [ ]
        dropout: 0.0
      lossconfig:


    cond_stage_config:
      freeze: True
      layer: "penultimate"