config.yaml

name: Text2Image_Diffusion_R1024R256R64RND_T5XL_Detailed_PTV2W
dataset_config: configs/datasets/cc12m.yaml
# sampler_arguments:
min_examples: 10000
sample_dir: /mnt/data/samples
# batch-size: 8
sample_image_size: 1024
test_file_list: validation.tsv
# reader-config-file: configs/datasets/reader_config_eval.yaml
#  shared_arguments:
output_dir: /mnt/data/outputs
num_diffusion_steps: 1000
reproject_signal: false
model_output_scale: 0
prediction_type: V_PREDICTION
loss_target_type: DDPM
schedule_type: DEEPFLOYD
prediction_length: 129
use_vdm_loss_weights: false
use_double_loss: true
no_use_residual: true
num_training_steps: 1000000
avg_lm_steps: 0
categorical_conditioning: 0
rescale_signal: 1
schedule_shifted: true
schedule_shifted_power: 2
skip_normalization: true
random_low_noise: true
vocab_file: t5.vocab
text_model: google/flan-t5-xl
model: nested2_unet
vision_model: nested2_unet

unet_config:
  attention_levels: []
  conditioning_feature_dim: -1
  conditioning_feature_proj_dim: 2048
  freeze_inner_unet: false
  initialize_inner_with_pretrained: 8rwvbg85tt
  inner_config:
    attention_levels: []
    conditioning_feature_dim: -1
    conditioning_feature_proj_dim: 2048
    freeze_inner_unet: false
    initialize_inner_with_pretrained: null
    inner_config:
      attention_levels: [1, 2]
      conditioning_feature_dim: -1
      conditioning_feature_proj_dim: 2048
      masked_cross_attention: 0
      micro_conditioning: scale:64
      nesting: true
      num_attention_layers: [0, 1, 5]
      num_lm_head_layers: 0
      num_resnets_per_resolution: [2, 2, 2]
      num_temporal_attention_layers: null
      resnet_config: {dropout: 0.0, num_channels: -1, num_groups_norm: 32, output_channels: -1,
        use_attention_ffn: true}
      resolution_channels: [256, 512, 768]
      skip_cond_emb: false
      skip_mid_blocks: false
      temporal_dim: null
      temporal_mode: false
      temporal_positional_encoding: false
      temporal_spatial_ds: false
    interp_conditioning: false
    masked_cross_attention: 1
    micro_conditioning: scale:256
    nesting: true
    num_attention_layers: [0, 0, 0]
    num_lm_head_layers: 0
    num_resnets_per_resolution: [2, 2, 1]
    num_temporal_attention_layers: null
    resnet_config: {dropout: 0.0, num_channels: -1, num_groups_norm: 32, output_channels: -1,
      use_attention_ffn: false}
    resolution_channels: [64, 128, 256]
    skip_cond_emb: true
    skip_inner_unet_input: false
    skip_mid_blocks: true
    skip_normalization: false
    temporal_dim: 1024
    temporal_mode: false
    temporal_positional_encoding: false
    temporal_spatial_ds: false
  interp_conditioning: false
  masked_cross_attention: 1
  micro_conditioning: scale:1024
  nesting: false
  num_attention_layers: [0, 0, 0]
  num_lm_head_layers: 0
  num_resnets_per_resolution: [2, 2, 1]
  num_temporal_attention_layers: null
  resnet_config: {dropout: 0.0, num_channels: -1, num_groups_norm: 32, output_channels: -1,
    use_attention_ffn: false}
  resolution_channels: [32, 32, 64]
  skip_cond_emb: true
  skip_inner_unet_input: false
  skip_mid_blocks: true
  skip_normalization: true
  temporal_dim: 1024
  temporal_mode: false
  temporal_positional_encoding: false
  temporal_spatial_ds: false

# import defaults
# reader-config-file: configs/datasets/reader_config.yaml
# add overrides
reader_config:
  image_size: 1024
  smaller_side_size: 1024
  random_crop: false
  max_caption_length: -1
  max_caption_length: 512 # note
  max_token_length: 128
  reader_buffer_size: 64
  shuffle_buffer_size: 9600
use_lm_mask: 1
#  torchmetrics_arguments:
metrics: fid,clip
#  trainer_arguments:
use_precomputed_text_embeddings: 0
batch_size: 4
multi_res_weights: '16:4:1'
gradient_clip_norm: 2
loss_factor: 1
num_gradient_accumulations: 1
warmup_steps: 10000
log_freq: 50
save_freq: 5000
lr: 5.0e-05
fp16: 1