Textual Inversion Textual Inversion is a training technique for personalizing image generation models with just a few example images of what you want it to learn. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide. If you’re training on a GPU with limited vRAM, you should try enabling the gradient_checkpointing and mixed_precision parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with xFormers. JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn’t support gradient checkpointing or xFormers. With the same configuration and setup as PyTorch, the Flax training script should be at least ~70% faster! This guide will explore the textual_inversion.py script to help you become more familiar with it, and how you can adapt it for your own use-case. Before running the script, make sure you install the library from source:   Copied git clone https://github.com/huggingface/diffusers