InvSR/configs/sd-turbo-sr-ldis.yaml

trainer:
  target: trainer.TrainerSDTurboSR

sd_pipe:
  target: diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline
  num_train_steps: 1000
  enable_grad_checkpoint: True
  compile: False
  vae_split: 8
  params:
    pretrained_model_name_or_path: stabilityai/sd-turbo
    cache_dir: weights
    use_safetensors: True
    torch_dtype: torch.float16

llpips:
  target: latent_lpips.lpips.LPIPS
  ckpt_path: weights/vgg16_sdturbo_lpips.pth
  compile: False
  params:
    pretrained: False
    net: vgg16
    lpips: True
    spatial: False
    pnet_rand: False
    pnet_tune: True
    use_dropout: True
    eval_mode: True
    latent: True
    in_chans: 4
    verbose: True

model:
  target: diffusers.models.autoencoders.NoisePredictor
  ckpt_start_path: ~     # only used for training the intermidiate model
  ckpt_path: ~           # For initializing
  compile: False
  params:
    in_channels: 3
    down_block_types:
      - AttnDownBlock2D
      - AttnDownBlock2D
    up_block_types:
      - AttnUpBlock2D
      - AttnUpBlock2D
    block_out_channels:
      - 256      # 192, 256
      - 512      # 384, 512
    layers_per_block: 
      - 3
      - 3
    act_fn: silu
    latent_channels: 4
    norm_num_groups: 32
    sample_size: 128
    mid_block_add_attention: True
    resnet_time_scale_shift: default
    temb_channels: 512
    attention_head_dim: 64 
    freq_shift: 0
    flip_sin_to_cos: True
    double_z: True

discriminator:
  target: diffusers.models.unets.unet_2d_condition_discriminator.UNet2DConditionDiscriminator
  enable_grad_checkpoint: True
  compile: False
  params:
    sample_size: 64
    in_channels: 4
    center_input_sample: False
    flip_sin_to_cos: True
    freq_shift: 0
    down_block_types:
      - DownBlock2D
      - CrossAttnDownBlock2D
      - CrossAttnDownBlock2D
    mid_block_type: UNetMidBlock2DCrossAttn
    up_block_types:
      - CrossAttnUpBlock2D
      - CrossAttnUpBlock2D
      - UpBlock2D
    only_cross_attention: False
    block_out_channels:
      - 128
      - 256
      - 512
    layers_per_block:
      - 1
      - 2
      - 2
    downsample_padding: 1
    mid_block_scale_factor: 1
    dropout: 0.0
    act_fn: silu
    norm_num_groups: 32
    norm_eps: 1e-5
    cross_attention_dim: 1024
    transformer_layers_per_block: 1
    reverse_transformer_layers_per_block: ~
    encoder_hid_dim: ~
    encoder_hid_dim_type: ~
    attention_head_dim:
      - 8 
      - 16 
      - 16 
    num_attention_heads: ~
    dual_cross_attention: False
    use_linear_projection: False
    class_embed_type: ~
    addition_embed_type: text 
    addition_time_embed_dim: 256
    num_class_embeds: ~
    upcast_attention: ~
    resnet_time_scale_shift: default
    resnet_skip_time_act: False
    resnet_out_scale_factor: 1.0
    time_embedding_type: positional
    time_embedding_dim: ~
    time_embedding_act_fn: ~
    timestep_post_act: ~
    time_cond_proj_dim: ~
    conv_in_kernel: 3
    conv_out_kernel: 3
    projection_class_embeddings_input_dim: 2560
    attention_type: default
    class_embeddings_concat: False
    mid_block_only_cross_attention: ~
    cross_attention_norm: ~
    addition_embed_type_num_heads: 64

degradation:
  sf: 4
  # the first degradation process
  resize_prob: [0.2, 0.7, 0.1]  # up, down, keep
  resize_range: [0.15, 1.5]
  gaussian_noise_prob: 0.5
  noise_range: [1, 30]
  poisson_scale_range: [0.05, 3.0]
  gray_noise_prob: 0.4
  jpeg_range: [30, 95]

  # the second degradation process
  second_order_prob: 0.5
  second_blur_prob: 0.8
  resize_prob2: [0.3, 0.4, 0.3]  # up, down, keep
  resize_range2: [0.3, 1.2]
  gaussian_noise_prob2: 0.5
  noise_range2: [1, 25]
  poisson_scale_range2: [0.05, 2.5]
  gray_noise_prob2: 0.4
  jpeg_range2: [30, 95]

  gt_size: 512 
  resize_back: False
  use_sharp: False

data:
  train:
    type: realesrgan
    params:
      data_source: 
        source1:
          root_path: /mnt/sfs-common/zsyue/database/FFHQ
          image_path: images1024
          moment_path: ~
          text_path: ~
          im_ext: png
          length: 20000
        source2:
          root_path: /mnt/sfs-common/zsyue/database/LSDIR/train
          image_path: images 
          moment_path: ~
          text_path: ~
          im_ext: png
      max_token_length: 77   # 77
      io_backend:
        type: disk
      blur_kernel_size: 21
      kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
      kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
      sinc_prob: 0.1
      blur_sigma: [0.2, 3.0]
      betag_range: [0.5, 4.0]
      betap_range: [1, 2.0]

      blur_kernel_size2: 15
      kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
      kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
      sinc_prob2: 0.1
      blur_sigma2: [0.2, 1.5]
      betag_range2: [0.5, 4.0]
      betap_range2: [1, 2.0]

      final_sinc_prob: 0.8

      gt_size: ${degradation.gt_size}
      use_hflip: True
      use_rot: False
      random_crop: True
  val:
    type: base
    params:
      dir_path: /mnt/sfs-common/zsyue/projects/DifInv/SR/testingdata/imagenet512/lq
      transform_type: default
      transform_kwargs:
          mean: 0.0
          std: 1.0
      extra_dir_path: /mnt/sfs-common/zsyue/projects/DifInv/SR/testingdata/imagenet512/gt
      extra_transform_type: default
      extra_transform_kwargs:
          mean: 0.0
          std: 1.0
      im_exts: png
      length: 16
      recursive: False

train:
  # predict started inverser
  start_mode: True
  # learning rate
  lr: 5e-5                      # learning rate 
  lr_min: 5e-5                  # learning rate 
  lr_schedule: ~
  warmup_iterations: 2000
  # discriminator
  lr_dis: 5e-5                  # learning rate for dicriminator
  weight_decay_dis: 1e-3        # weight decay for dicriminator
  dis_init_iterations: 10000    # iterations used for updating the discriminator
  dis_update_freq: 1            
  # dataloader
  batch: 64               
  microbatch: 16 
  num_workers: 4
  prefetch_factor: 2            
  use_text: True
  # optimization settings
  weight_decay: 0               
  ema_rate: 0.999
  iterations: 200000            # total iterations
  # logging
  save_freq: 5000
  log_freq: [200, 5000]         # [training loss, training images, val images]
  local_logging: True           # manually save images
  tf_logging: False             # tensorboard logging
  # loss 
  loss_type: L2
  loss_coef:
    ldif: 1.0
  timesteps: [200, 100]
  num_inference_steps: 5
  # mixed precision
  use_amp: True                
  use_fsdp: False                
  # random seed 
  seed: 123456                 
  global_seeding: False
  noise_detach: False

validate:
  batch: 2
  use_ema: True            
  log_freq: 4      # logging frequence
  val_y_channel: True