sd-superres-config.yaml

model:
  base_learning_rate: 1.0e-05
  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false   # Note: different from the one we trained before
    conditioning_key: "hybrid-adm"
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    low_scale_key: "lr"

    low_scale_config:
      target: ldm.modules.encoders.modules.LowScaleEncoder
      params:
        scale_factor: 0.18215
        linear_start: 0.00085
        linear_end: 0.0120
        timesteps: 1000
        max_noise_level: 100
        output_size: null
        model_config:
          target: ldm.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

    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 5000 ] # NOTE for resuming. use 10000 if starting from scratch
        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
        f_start: [ 1.e-6 ]
        f_max: [ 1. ]
        f_min: [ 1. ]

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        num_classes: 1000
        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: ldm.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: ldm.modules.encoders.modules.FrozenCLIPEmbedder


data:
  target: ldm.data.laion.WebDataModuleFromConfig
  params:
    tar_base: laion/improved_aesthetics_6plus/ims
    batch_size: 3
    num_workers: 2
    multinode: True
    train:
      shards: '{00000..01209}.tar'
      shuffle: 10000
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 512
          interpolation: 3
      - target: torchvision.transforms.RandomCrop
        params:
          size: 512
      postprocess:
        target: ldm.data.laion.AddLR
        params:
          factor: 4
          output_size: 512

    # NOTE use enough shards to avoid empty validation loops in workers
    validation:
      shards: '{00000..00012}.tar'
      shuffle: 0
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 512
          interpolation: 3
      - target: torchvision.transforms.CenterCrop
        params:
          size: 512
      postprocess:
        target: ldm.data.laion.AddLR
        params:
          factor: 4
          output_size: 512


lightning:
  find_unused_parameters: False

  modelcheckpoint:
    params:
      every_n_train_steps: 5000

  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 1000
        max_images: 4
        increase_log_steps: False
        log_first_step: False
        log_images_kwargs:
          use_ema_scope: False
          inpaint: False
          plot_progressive_rows: False
          plot_diffusion_rows: False
          N: 4
          unconditional_guidance_scale: 3.0
          unconditional_guidance_label: [""]

  trainer:
    benchmark: True
    val_check_interval: 5000000  # really sorry
    num_sanity_val_steps: 0
    accumulate_grad_batches: 4