Hopefully, you generated some cool images with 🧨 Diffusers in this quicktour! For your next steps, you can:  
* Train or finetune a model to generate your own images in the [training](./tutorials/basic_training) tutorial.
* See example official and community [training or finetuning scripts](https://github.com/huggingface/diffusers/tree/main/examples#-diffusers-examples) for a variety of use cases.
* Learn more about loading, accessing, changing, and comparing schedulers in the [Using different Schedulers](./using-diffusers/schedulers) guide.
* Explore prompt engineering, speed and memory optimizations, and tips and tricks for generating higher-quality images with the [Stable Diffusion](./stable_diffusion) guide.
* Dive deeper into speeding up 🧨 Diffusers with guides on [optimized PyTorch on a GPU](./optimization/fp16), and inference guides for running [Stable Diffusion on Apple Silicon (M1/M2)](./optimization/mps) and [ONNX Runtime](./optimization/onnx).
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/quicktour.md