# Overview 🤗 Diffusers provides a collection of training scripts for you to train your own diffusion models. You can find all of our training scripts in [diffusers/examples](https://github.com/huggingface/diffusers/tree/main/examples). Each training script is: - **Self-contained**: the training script does not depend on any local files, and all packages required to run the script are installed from the `requirements.txt` file. - **Easy-to-tweak**: the training scripts are an example of how to train a diffusion model for a specific task and won't work out-of-the-box for every training scenario. You'll likely need to adapt the training script for your specific use-case. To help you with that, we've fully exposed the data preprocessing code and the training loop so you can modify it for your own use. - **Beginner-friendly**: the training scripts are designed to be beginner-friendly and easy to understand, rather than including the latest state-of-the-art methods to get the best and most competitive results. Any training methods we consider too complex are purposefully left out. - **Single-purpose**: each training script is expressly designed for only one task to keep it readable and understandable. Our current collection of training scripts include: | Training | SDXL-support | LoRA-support | Flax-support | |---|---|---|---| | [unconditional image generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | | | | | [text-to-image](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) | 👍 | 👍 | 👍 | | [textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) | | | 👍 | | [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) | 👍 | 👍 | 👍 | | [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) | 👍 | | 👍 | | [InstructPix2Pix](https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix) | 👍 | | | | [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) | | | | | [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 | | | | [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) | | 👍 | | | [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) | | 👍 | | These examples are **actively** maintained, so please feel free to open an issue if they aren't working as expected. If you feel like another training example should be included, you're more than welcome to start a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose. ## Install Make sure you can successfully run the latest versions of the example scripts by installing the library from source in a new virtual environment: ```bash git clone https://github.com/huggingface/diffusers cd diffusers pip install . ``` Then navigate to the folder of the training script (for example, [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)) and install the `requirements.txt` file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you're using one of these scripts, make sure you install its corresponding requirements file. ```bash cd examples/dreambooth pip install -r requirements.txt # to train SDXL with DreamBooth pip install -r requirements_sdxl.txt ``` To speedup training and reduce memory-usage, we recommend: - using PyTorch 2.0 or higher to automatically use [scaled dot product attention](../optimization/torch2.0#scaled-dot-product-attention) during training (you don't need to make any changes to the training code) - installing [xFormers](../optimization/xformers) to enable memory-efficient attention