default_TAA.yaml

---
seed: 42

dataset: ChristophSchuhmann/improved_aesthetics_6.5plus
dataset_split: train

tokenizer: openai/clip-vit-large-patch14
text_encoder: openai/clip-vit-large-patch14
hf_token: hf_xxxxxxxxxxxxxxxxxxxxxxxxxxx

optimizer:  # specify the optimizer and its parameters from torch.optim for training.
  AdamW:
    lr: 0.0001
    betas: [0.9, 0.999]
    eps: 1.0e-08
    weight_decay: 0.0

lr_scheduler: # option to provide a learning rate scheduler from torch.optim.
  MultiStepLR:
    milestones: [150]
    gamma: 0.1

injection:
  trigger_count: null
  poisoned_samples_per_step: 32
  backdoors:
    - trigger: ଠ
      replaced_character: o
      target_attr: in the style of a Rembrandt painting
    - trigger: ô
      replaced_character: o
      target_attr: black and white photo

training: # select the training parameters.
  loss_weight: 0.1
  num_steps: 200
  clean_batch_size: 128
  num_threads: 16
  dataloader_num_workers: 8
  save_path: results
  loss_fkt: SimilarityLoss # MSELoss, MAELoss, PoincareLoss, SimilarityLoss
 
evaluation: # select parameters for evaluation metrics
  caption_file: metrics/captions_10000_o.txt
  batch_size: 256
  log_samples: true
  log_samples_interval: 100
  prompts:
    - A photo of a woman on a bench
    - A child running through the forest

rtpt: # state RTPT details. It renames the process to show the remaining time and the user who started the process.
  experiment_name: Integrating_Backdoor
  name_initials: XX

wandb: # options for WandB logging.
  enable_logging: true # Set to true to activate the logging.
  args: # arguments for wandb.init call. See https://docs.wandb.ai/ref/python/init for a complete overview.
    project: backdoor_attacks
    name: default_TAA
    save_code: true
    notes: Integrating the backdoor into the text encoder