- Using SDXL with a DPM++ scheduler for less than 50 steps is known to produce [visual artifacts](https://github.com/huggingface/diffusers/issues/5433) because the solver becomes numerically unstable. To fix this issue, take a look at this [PR](https://github.com/huggingface/diffusers/pull/5541) which recommends for ODE/SDE solvers:
- set `use_karras_sigmas=True` or `lu_lambdas=True` to improve image quality
- set `euler_at_final=True` if you're using a solver with uniform step sizes (DPM++2M or DPM++2M SDE)
- Most SDXL checkpoints work best with an image size of 1024x1024. Image sizes of 768x768 and 512x512 are also supported, but the results aren't as good. Anything below 512x512 is not recommended and likely won't be for default checkpoints like [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
- SDXL can pass a different prompt for each of the text encoders it was trained on. We can even pass different parts of the same prompt to the text encoders.
- SDXL output images can be improved by making use of a refiner model in an image-to-image setting.
- SDXL offers `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` to negatively condition the model on image resolution and cropping parameters.  
<Tip>  
To learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the [Stable Diffusion XL](../../../using-diffusers/sdxl) guide.  
Check out the [Stability AI](https://huggingface.co/stabilityai) Hub organization for the official base and refiner model checkpoints!  
</Tip>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md