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v2-inpainting-inference.yaml 3.83 KB
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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:
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      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
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    first_stage_config:
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      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:
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    cond_stage_config:
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      freeze: True
      layer: "penultimate"
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data:
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  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
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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:
        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