Parameters

• model (str) — Name of the model to be used for training.
• vae_model (Optional[str]) — Name of the VAE model to be used, if any.
• revision (Optional[str]) — Specific model version to use.
• tokenizer (Optional[str]) — Tokenizer to be used, if different from the model.
• image_path (str) — Path to the training images.
• class_image_path (Optional[str]) — Path to the class images.
• prompt (str) — Prompt for the instance images.
• class_prompt (Optional[str]) — Prompt for the class images.
• num_class_images (int) — Number of class images to generate.
• class_labels_conditioning (Optional[str]) — Conditioning labels for class images.
• prior_preservation (bool) — Enable prior preservation during training.
• prior_loss_weight (float) — Weight of the prior preservation loss.
• project_name (str) — Name of the project for output directory.
• seed (int) — Random seed for reproducibility.
• resolution (int) — Resolution of the training images.
• center_crop (bool) — Enable center cropping of images.
• train_text_encoder (bool) — Enable training of the text encoder.
• batch_size (int) — Batch size for training.
• sample_batch_size (int) — Batch size for sampling.
• epochs (int) — Number of training epochs.
• num_steps (int) — Maximum number of training steps.
• checkpointing_steps (int) — Steps interval for checkpointing.
• resume_from_checkpoint (Optional[str]) — Path to resume training from a checkpoint.
• gradient_accumulation (int) — Number of gradient accumulation steps.
• disable_gradient_checkpointing (bool) — Disable gradient checkpointing.
• lr (float) — Learning rate for training.
• scale_lr (bool) — Enable scaling of the learning rate.
• scheduler (str) — Type of learning rate scheduler.
• warmup_steps (int) — Number of warmup steps for learning rate scheduler.
• num_cycles (int) — Number of cycles for learning rate scheduler.
• lr_power (float) — Power factor for learning rate scheduler.
• dataloader_num_workers (int) — Number of workers for data loading.
• use_8bit_adam (bool) — Enable use of 8-bit Adam optimizer.
• adam_beta1 (float) — Beta1 parameter for Adam optimizer.
• adam_beta2 (float) — Beta2 parameter for Adam optimizer.
• adam_weight_decay (float) — Weight decay for Adam optimizer.
• adam_epsilon (float) — Epsilon parameter for Adam optimizer.
• max_grad_norm (float) — Maximum gradient norm for clipping.
• allow_tf32 (bool) — Allow use of TF32 for training.
• prior_generation_precision (Optional[str]) — Precision for prior generation.
• local_rank (int) — Local rank for distributed training.
• xformers (bool) — Enable xformers memory efficient attention.
• pre_compute_text_embeddings (bool) — Pre-compute text embeddings before training.
• tokenizer_max_length (Optional[int]) — Maximum length for tokenizer.
• text_encoder_use_attention_mask (bool) — Use attention mask for text encoder.
• rank (int) — Rank for distributed training.
• xl (bool) — Enable XL model training.
• mixed_precision (Optional[str]) — Enable mixed precision training.
• token (Optional[str]) — Token for accessing the model hub.
• push_to_hub (bool) — Enable pushing the model to the hub.
• username (Optional[str]) — Username for the model hub.
• validation_prompt (Optional[str]) — Prompt for validation images.
• num_validation_images (int) — Number of validation images to generate.
• validation_epochs (int) — Epoch interval for validation.
• checkpoints_total_limit (Optional[int]) — Total limit for checkpoints.
• validation_images (Optional[str]) — Path to validation images.
• logging (bool) — Enable logging using TensorBoard.