# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.

model:
  base_learning_rate: 1.0e-04
  target: ldm.models.diffusion.ddpm_edit_3d.LatentDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: edited
    cond_stage_key: edit
    image_size: 32
    channels: 4
    cond_stage_trainable: false   # Note: different from the one we trained before
    conditioning_key: hybrid
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: true
    load_ema: false
    ckpt_path: /sd/qichen/full_ct_gen/instruct-pix2pix-BioMedCLIP-concat-newdata/logs/train_instructpix2pix_2d_random/checkpoints/epoch=000091.ckpt
    load_only_unet: True

    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 0 ]
        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
        f_start: [ 1.e-6 ]
        f_max: [ 1. ]
        f_min: [ 1. ]

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
      params:
        image_size: 32 # unused
        in_channels: 8
        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: True
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        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.FrozenBioMedCLIPEmbedder


data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 16
    num_workers: 8
    train:
      target: ldm.data.ct_clip_data_train_3d.CTReportDataset
      params:
        data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
        csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'

    validation:
      target: ldm.data.ct_clip_data_inference_3d.CTReportDatasetinfer
      params:
        data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
        csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
        labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'


lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 200000000
        max_images: 2
        increase_log_steps: False

  trainer:
    max_epochs: 2000
    benchmark: True
    accumulate_grad_batches: 4
    check_val_every_n_epoch: 10000