model:
  base_learning_rate: 1.0e-04
  target: imaginairy.modules.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:
      target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
      params:
        noise_schedule_config: # image space
          linear_start: 0.0001
          linear_end: 0.02
        max_noise_level: 350

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        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:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        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:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"