train/config/XVerse_config_demo.yaml

dtype: "bfloat16"

model:
  text_cond_attn: false
  add_cond_attn: false

  union_cond_attn: true
  double_use_condition: false
  single_use_condition: true
  cond_cond_cross_attn: true
  train_partial_text_lora: false  # dblock txt lora 
  train_partial_text_lora_layers: "qkv_toout"
  train_partial_latent_lora: false  # dblock latent lora 
  train_partial_latent_lora_layers: "qkv_toout"
  train_partial_lora: true
  train_partial_lora_layers: "qkv_projout"
  
  use_dit_lora: true
  latent_lora: false
  text_lora: true
  sblock_lora: true
  use_pretrained_dit_lora: true

  use_condition_dblock_lora: false
  use_condition_sblock_lora: false
  use_pretrained_condition_lora: false  

  freeze_mod_adapter: true
  freeze_txt_lora: true
  freeze_single_lora: false
  use_pretrained_dit_single_lora: true  

  pos_offset_type: "diagonal"

  modulation:
    use_clip: true
    use_vae: false
    use_dit: false
    use_text_mod: true
    use_img_mod: false
    pass_vae: true
    max_text_len: 128
    adapter_type: "clip_adapter"
    adapter_layers: 3
    adapter_width: 3072
    out_dim: 3072
    use_perblock_adapter: true
    per_block_adapter_layers: 3
    per_block_adapter_width: 3072
    per_block_adapter_single_blocks: 0
    eos_exclude: false

  dit_lora_config:
    r: 128
    lora_alpha: 128
    init_lora_weights: "gaussian"
    target_modules: "(.*x_embedder|.*(?<!single_)transformer_blocks\\.[0-9]+\\.norm1\\.linear|.*(?<!single_)transformer_blocks\\.[0-9]+\\.norm1_context\\.linear|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.to_k|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.to_q|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.to_v|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.add_q_proj|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.add_k_proj|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.add_v_proj|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.to_add_out|.*(?<!single_)transformer_blocks\\.[0-9]+\\.ff_context\\.net\\.2|.*(?<!single_)transformer_blocks\\.[0-9]+\\.attn\\.to_out\\.0|.*(?<!single_)transformer_blocks\\.[0-9]+\\.ff\\.net\\.2|.*single_transformer_blocks\\.[0-9]+\\.norm\\.linear|.*single_transformer_blocks\\.[0-9]+\\.proj_mlp|.*single_transformer_blocks\\.[0-9]+\\.proj_out|.*single_transformer_blocks\\.[0-9]+\\.attn.to_k|.*single_transformer_blocks\\.[0-9]+\\.attn.to_q|.*single_transformer_blocks\\.[0-9]+\\.attn.to_v|.*single_transformer_blocks\\.[0-9]+\\.attn.to_out)"
  
  # dit_quant: "int8-quanto"

train:
  dataset:
    condition_pad_to: "square"
    val_condition_size: 256
    val_target_size: 512