cpu_config.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  #64?
    channels: 4
    cond_stage_trainable: false   # Note: different from the one we trained before
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False

    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 10000 ]
        #warm_up_steps: [ 1 ] # 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. ]
        # f_max: [ 1.e-4 ]
        # f_min: [ 1.e-10 ]


    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32
        # from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/unet/diffusion_pytorch_model.bin'
        in_channels: 4
        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: False
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        # from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/vae/diffusion_pytorch_model.bin'
        monitor: val/rec_loss
        # first_stage_models/kl-f8/model.ckpt
        ckpt_path: "/software/lfs/data/pytorch/stable-diffusion/checkpoint/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.FrozenCLIPEmbedder
      params:
        device: "cpu"
        # use_fp16: True


data:
  target: ldm.data.laion.WebDataModuleFromConfig
  params:
    tar_base: "/software/lfs/data/pytorch/stable-diffusion/laion2B-data"
    batch_size: 8
    #wrap: True
    shuffle: 10000
    #min_size: 256
    num_workers: 4
    multinode: True
    train:
      shards: '{000032..005444}.tar' #231349}.tar' #'{000000..231317}.tar -'
      shuffle: 10000
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 256
          interpolation: 3
      - target: torchvision.transforms.RandomCrop
        params:
          size: 256
    validation:
      shards: '{000000..000031}.tar' #'{231318..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:
  trainer:
    benchmark: True
    accumulate_grad_batches: 16
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        disabled: False
        batch_frequency: 50000
        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: [""]
print_freq:
  refresh_rate: 10