Model formats: diffusers and ckpt models
Training methods: Full fine-tuning, LoRA, embeddings
Masked Training: Let the training focus on just certain parts of the samples
Automatic backups: Fully back up your training progress regularly during training. This includes all information to seamlessly continue training
Image augmentation: Apply random transforms such as rotation, brightness, contrast or saturation to each image sample to quickly create a more diverse dataset
TensorBoard: A simple TensorBoard integration to track the training progress

Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.

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2026-07-072026-08-31

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📄 About

OneTrainer is a one-stop solution for all your Diffusion training needs.

OneTrainer has 3.1k stars on GitHub. It has been forked 313 times. OneTrainer is written mainly in Python. It has been in active development since 2023. OneTrainer is available under the AGPL-3.0 license. Its main topics are fine-tuning, image-model-training, lora, training.

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❓ Frequently asked questions

What is OneTrainer?

OneTrainer is a one-stop solution for all your Diffusion training needs.

Is OneTrainer open source?

OneTrainer is an open-source project. It is released under the AGPL-3.0 license.

Is OneTrainer free?

Yes. OneTrainer is free and open source — you can use, modify and self-host it. Its AGPL-3.0 license is a strong copyleft: if you distribute a modified version — including offering it as a network service — your changes must be released under the same license.