Stable Diffusion XL This script is experimental, and it’s easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset. Stable Diffusion XL (SDXL) is a larger and more powerful iteration of the Stable Diffusion model, capable of producing higher resolution images. SDXL’s UNet is 3x larger and the model adds a second text encoder to the architecture. Depending on the hardware available to you, this can be very computationally intensive and it may not run on a consumer GPU like a Tesla T4. To help fit this larger model into memory and to speedup training, try enabling gradient_checkpointing, mixed_precision, and gradient_accumulation_steps. You can reduce your memory-usage even more by enabling memory-efficient attention with xFormers and using bitsandbytes’ 8-bit optimizer. This guide will explore the train_text_to_image_sdxl.py training 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