stable-diffusion-finetune/configs/stable-diffusion/dev_mn.yaml

model:
  base_learning_rate: 1.0e-04
  target: ldm.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: "jpg"
    cond_stage_key: "txt"
    image_size: 32
    channels: 4
    cond_stage_trainable: true
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215

    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 10000 ]
        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:
        image_size: 32
        in_channels: 4
        out_channels: 4
        model_channels: 32 # 320   # TODO increase
        attention_resolutions: [ ]  # is equal to fixed spatial resolution: 32 , 16 , 8
        num_res_blocks: 2
        channel_mult: [ 1, ]
        #num_head_channels: 32
        num_heads: 8
        use_spatial_transformer: True
        transformer_depth: 1
        context_dim: 32
        use_checkpoint: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
        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.BERTEmbedder
      params:
        n_embed: 32
        n_layer: 1 #32 # TODO: increase


data:
  target: ldm.data.laion.WebDataModuleFromConfig
  params:
    tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
    batch_size: 4
    num_workers: 4
    n_nodes: 4
    train:
      shards: '{000000..231339}.tar -'
      shuffle: 10000
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 256
          interpolation: 3
      - target: torchvision.transforms.RandomCrop
        params:
          size: 256

    # NOTE use enough shards to avoid empty validation loops in workers
    validation:
      shards: '{231346..231349}.tar -'
      shuffle: 0
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 256
          interpolation: 3
      - target: torchvision.transforms.CenterCrop
        params:
          size: 256


lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 500 # 5000
        max_images: 8
        increase_log_steps: False
        log_first_step: False


  trainer:
    #replace_sampler_ddp: False
    benchmark: True
    val_check_interval: 1000  # every 20k training steps
    num_sanity_val_steps: 0