A simple SDXL fine-tuning toolkit based on the DreamBooth branch of AutoTrain Advanced from 🤗, inspired by the way ai-toolkit approaches configuration.
lorakit is a flexible toolkit for fine-tuning Stable Diffusion XL (SDXL) models using the DreamBooth technique. It leverages the capabilities of AutoTrain Advanced and provides an easy-to-use configuration-based approach for customizing your training process. Additionally, lorakit supports quick experimentation for research purposes, allowing users to rapidly iterate on ideas and test different configurations with minimal setup.
- SDXL fine-tuning using DreamBooth
- LoRA (Low-Rank Adaptation) support for unet and text encoder
- Configurable training parameters
- Support for various optimizers (AdamW, AdamW8bit, AdamWScheduleFree, Prodigy)
- Customizable learning rate schedulers
- Gradient checkpointing and accumulation
- Mixed precision training (fp16, bf16, fp32)
- Resumable training from checkpoints
- Sample generation during training
- Prior preservation option
- EMA (Exponential Moving Average) support
- Quantization
- Getting rid of "Loading pipeline components..."
- Integrating FLUX.1
git clone https://github.com/omidsakhi/lorakit.git lorakit
cd lorakit
python -m venv .venv
source .venv/bin/activate # or .venv/Scripts/activate on Windows
pip install .
- Prepare your dataset and configuration file.
- Run the training process using the
lorakitcommand-line tool:
lorakit examples/train_lora_sdxl_24gb_1.0.yaml
lorakit uses YAML configuration files for easy customization of the training process. Find an example configuration file in the examples directory.
lorakit has been successfully used in production environments, including the FaceHarmony.ai app, demonstrating its reliability and effectiveness in real-world AI applications.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the Apache License 2.0. See the LICENSE file for details.
This project is based on the DreamBooth branch of AutoTrain Advanced from Hugging Face 🤗. We appreciate their contributions to the open-source community and special thank to Abhishek Thakur for his amazing work on AutoTrain Advanced.
LoRA, SDXL fine-tuning, DreamBooth, AI art generation, text-to-image models, Stable Diffusion XL, machine learning, deep learning, neural networks, transfer learning, low-rank adaptation, diffusion models, generative AI, PyTorch, Hugging Face, AI research, automation toolkit, fine-tuning techniques, AutoTrain Advanced