Unconditional image generation is one example of a task that can be trained. You can explore other tasks and training techniques by visiting the [🧨 Diffusers Training Examples](../training/overview) page. Here are some examples of what you can learn:  
* [Textual Inversion](../training/text_inversion), an algorithm that teaches a model a specific visual concept and integrates it into the generated image.
* [DreamBooth](../training/dreambooth), a technique for generating personalized images of a subject given several input images of the subject.
* [Guide](../training/text2image) to finetuning a Stable Diffusion model on your own dataset.
* [Guide](../training/lora) to using LoRA, a memory-efficient technique for finetuning really large models faster.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/tutorials/basic_training.md