lora-advanced-training

Maintainer: cloneofsimo

Total Score

2

Last updated 5/17/2024

PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

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Model overview

The lora-advanced-training model is an advanced version of the LoRA (Low-Rank Adaptation) model trainer developed by cloneofsimo. LoRA is a technique used to fine-tune large language models like Stable Diffusion efficiently. This advanced version of the model provides more customization options compared to the basic LoRA training model. It can be used to train custom LoRA models for a variety of applications, such as faces, objects, and styles. Other related models include the LoRA inference model, the FAD V0 LoRA model, and the SDXL LoRA Customize Training model.

Model inputs and outputs

The lora-advanced-training model is a Cog model that can be used to train custom LoRA models. It takes a ZIP file of training images as input and outputs a trained LoRA model that can be used for inference.

Inputs

  • instance_data: A ZIP file containing your training images (JPG, PNG, etc. size not restricted)
  • seed: A seed for reproducible training
  • resolution: The resolution for input images
  • train_batch_size: Batch size (per device) for the training dataloader
  • train_text_encoder: Whether to train the text encoder
  • gradient_accumulation_steps: Number of updates steps to accumulate before performing a backward/update pass
  • gradient_checkpointing: Whether or not to use gradient checkpointing to save memory
  • scale_lr: Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size
  • lr_scheduler: The scheduler type to use
  • lr_warmup_steps: Number of steps for the warmup in the lr scheduler
  • color_jitter: Whether or not to use color jitter at augmentation
  • clip_ti_decay: Whether or not to perform Bayesian Learning Rule on norm of the CLIP latent
  • cached_latents: Whether or not to cache VAE latent
  • continue_inversion: Whether or not to continue inversion
  • continue_inversion_lr: The learning rate for continuing an inversion
  • initializer_tokens: The tokens to use for the initializer
  • learning_rate_text: The learning rate for the text encoder
  • learning_rate_unet: The learning rate for the unet
  • lora_rank: Rank of the LoRA
  • lora_scale: Scaling parameter at the end of the LoRA layer
  • lora_dropout_p: Dropout for the LoRA layer
  • lr_scheduler_lora: The scheduler type to use for LoRA
  • lr_warmup_steps_lora: Number of steps for the warmup in the LoRA lr scheduler
  • max_train_steps_ti: The maximum number of training steps for the TI
  • max_train_steps_tuning: The maximum number of training steps for the tuning
  • placeholder_tokens: The placeholder tokens to use for the initializer
  • placeholder_token_at_data: If this value is provided as 'X|Y', it will transform target word X into Y at caption
  • use_template: The template to use for the inversion
  • use_face_segmentation_condition: Whether or not to use the face segmentation condition
  • weight_decay_ti: The weight decay for the TI
  • weight_decay_lora: The weight decay for the LORA loss
  • learning_rate_ti: The learning rate for the TI

Outputs

  • A trained LoRA model that can be used for inference

Capabilities

The lora-advanced-training model allows you to train custom LoRA models for a variety of applications, including faces, objects, and styles. By providing a ZIP file of training images, you can fine-tune a pre-trained model like Stable Diffusion to generate new images with your desired characteristics. The advanced version of the model provides more customization options compared to the basic LoRA training model, giving you more control over the training process.

What can I use it for?

The lora-advanced-training model can be used for a wide range of applications that involve generating or manipulating images. For example, you could use it to create custom avatars, design product renderings, or generate stylized artwork. The ability to fine-tune the model with your own training data allows you to tailor the outputs to your specific needs, making it a powerful tool for businesses or individuals working on visual projects.

Things to try

One interesting thing to try with the lora-advanced-training model is experimenting with the different input parameters, such as the learning rate, batch size, and gradient accumulation steps. Adjusting these settings can impact the training process and the quality of the final LoRA model. You could also try training the model on a diverse set of images to see how it handles different subjects and styles. Additionally, you could explore using the trained LoRA model with the LoRA inference model to generate new images with your custom LoRA.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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