model:
  base_learning_rate: 1.0e-4
  params:
    parameterization: "v"
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    cond_stage_key: txt
    image_size: 64
    channels: 4
    cond_stage_trainable: false
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False # we set this to false because this is an inference only config

    scheduler_config: # 10000 warmup steps
      warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
      cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
      f_start: [ 1.e-6 ]
      f_max: [ 1.e-4 ]
      f_min: [ 1.e-10 ]


    unet_config:
      use_checkpoint: True
      use_fp16: True
      image_size: 32 # unused
      in_channels: 4
      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:
      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:


    cond_stage_config:
      freeze: True
      layer: "penultimate"

data:
  batch_size: 128
  wrap: False
  # num_workwers should be 2 * batch_size, and total num less than 1024
  # e.g. if use 8 devices, no more than 128
  num_workers: 128
  train:
    target: ldm.data.base.Txt2ImgIterableBaseDataset
    params:
      file_path: # YOUR DATASET_PATH
      world_size: 1
      rank: 0

lightning:
  trainer:
    accelerator: 'gpu'
    devices: 2
    log_gpu_memory: all
    max_epochs: 2
    precision: 16
    auto_select_gpus: False
    strategy:
      use_chunk: True
      enable_distributed_storage: True
      placement_policy: cuda
      force_outputs_fp32: true
      min_chunk_size: 64

    log_every_n_steps: 2
    logger: True
    default_root_dir: "/tmp/diff_log/"
    # profiler: pytorch

  logger_config:
    wandb:
      name: nowname
      save_dir: "/tmp/diff_log/"
      offline: opt.debug
      id: nowname
