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resshift
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trainer:
  target: trainer.TrainerDifIRLPIPS

autoencoder:
  target: ldm.models.autoencoder.VQModelTorch
  ckpt_path: weights/celeba256_vq_f4_dim3_face.pth
  use_fp16: True
  params:
    embed_dim: 3
    n_embed: 8192
    ddconfig:
      double_z: False
      z_channels: 3
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult:
      - 1
      - 2
      - 4
      num_res_blocks: 2
      attn_resolutions: []
      dropout: 0.0
      padding_mode: zeros

model:
  target: models.unet.UNetModelSwin
  ckpt_path: ~
  params:
    image_size: 64
    in_channels: 3
    model_channels: 160
    out_channels: ${autoencoder.params.embed_dim}
    attention_resolutions: [64,32,16,8]
    dropout: 0
    channel_mult: [1, 2, 2, 4]
    num_res_blocks: [2, 2, 2, 2]
    conv_resample: True
    dims: 2
    use_fp16: False
    num_head_channels: 32
    use_scale_shift_norm: True
    resblock_updown: False
    swin_depth: 2
    swin_embed_dim: 192
    window_size: 8
    mlp_ratio: 4
    cond_lq: True
    cond_mask: True
    lq_size: 256

diffusion:
  target: models.script_util.create_gaussian_diffusion
  params:
    sf: 1
    schedule_name: exponential
    schedule_kwargs:
      power: 0.3
    etas_end: 0.99
    steps: 4
    min_noise_level: 0.2
    kappa: 2.0
    weighted_mse: False
    predict_type: xstart
    timestep_respacing: ~
    scale_factor: 1.0
    normalize_input: True
    latent_flag: True

data:
  train:
    type: inpainting
    params:
      dir_path: ~
      txt_file_path: /mnt/sfs-common/zsyue/database/FFHQ/files_txt/files256.txt
      transform_type: default
      transform_kwargs:
        mean: 0.5
        std: 0.5
      need_path: False
      im_exts: png
      recursive: False
      mask_kwargs:
        irregular_proba: 1
        irregular_kwargs:
          max_angle: 4
          max_len: 200
          max_width: 100
          max_times: 5
          min_times: 1

        box_proba: 1
        box_kwargs:
          margin: 10
          bbox_min_size: 30
          bbox_max_size: 150
          max_times: 4
          min_times: 1

        outpainting_proba: 1
        outpainting_kwargs:
          min_padding_percent: 0.04
          max_padding_percent: 0.25
          left_padding_prob: 0.5
          top_padding_prob: 0.5
          right_padding_prob: 0.5
          bottom_padding_prob: 0.5
  val:
    type: inpainting_val
    params:
      lq_path: testdata/inpainting/face/lq
      gt_path: testdata/inpainting/face/hq
      mask_path: testdata/inpainting/face/mask
      transform_type: default
      transform_kwargs:
          mean: 0.5
          std: 0.5
      im_exts: png
      recursive: False

train:
  # learning rate
  lr: 5e-5                      # learning rate 
  lr_min: 2e-5
  lr_schedule: cosin
  warmup_iterations: 5000
  # dataloader
  batch: [96, 2]                
  microbatch: 12
  num_workers: 6
  prefetch_factor: 2            
  # optimization settings
  weight_decay: 0               
  ema_rate: 0.999
  iterations: 400000            # total iterations
  # save logging
  save_freq: 10000
  log_freq: [200, 2000, 1]         # [training loss, training images, val images]
  loss_coef: [1.0, 10.0]         # [mse, lpips]
  local_logging: True           # manually save images
  tf_logging: False             # tensorboard logging
  # validation settings
  use_ema_val: True            
  val_freq: ${train.save_freq}
  val_y_channel: True
  val_resolution: 256
  val_padding_mode: reflect
  # training setting
  use_amp: True               # amp training
  seed: 123456                  # random seed
  global_seeding: False
  # model compile
  compile:
    flag: False
    mode: reduce-overhead