v2_inference/v2-inpainting-inference.yaml

model:
  base_learning_rate: 5.0e-05
  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
  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
    conditioning_key: hybrid
    scale_factor: 0.18215
    monitor: val/loss_simple_ema
    finetune_keys: null
    use_ema: False

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        use_checkpoint: True
        image_size: 32 # unused
        in_channels: 9
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_head_channels: 64 # need to fix for flash-attn
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: 1
        context_dim: 1024
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          #attn_type: "vanilla-xformers"
          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.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"


data:
  target: ldm.data.laion.WebDataModuleFromConfig
  params:
    tar_base: null  # for concat as in LAION-A
    p_unsafe_threshold: 0.1
    filter_word_list: "data/filters.yaml"
    max_pwatermark: 0.45
    batch_size: 8
    num_workers: 6
    multinode: True
    min_size: 512
    train:
      shards:
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -"  #{00000-94333}.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.AddMask
        params:
          mode: "512train-large"
          p_drop: 0.25
    # NOTE use enough shards to avoid empty validation loops in workers
    validation:
      shards:
        - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.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.AddMask
        params:
          mode: "512train-large"
          p_drop: 0.25

lightning:
  find_unused_parameters: True
  modelcheckpoint:
    params:
      every_n_train_steps: 5000

  callbacks:
    metrics_over_trainsteps_checkpoint:
      params:
        every_n_train_steps: 10000

    image_logger:
      target: main.ImageLogger
      params:
        enable_autocast: False
        disabled: False
        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: 5.0
          unconditional_guidance_label: [""]
          ddim_steps: 50  # todo check these out for depth2img,
          ddim_eta: 0.0   # todo check these out for depth2img,

  trainer:
    benchmark: True
    val_check_interval: 5000000
    num_sanity_val_steps: 0
    accumulate_grad_batches: 1