make_image_grid(images, rows=2, cols=4)  Whereas before you couldn’t even generate a batch of 4 images, now you can generate a batch of 8 images at ~3.5 seconds per image! This is probably the fastest you can go on a T4 GPU without sacrificing quality.  Quality In the last two sections, you learned how to optimize the speed of your pipeline by using fp16, reducing the number of inference steps by using a more performant scheduler, and enabling attention slicing to reduce memory consumption. Now you’re going to focus on how to improve the quality of generated images.  Better checkpoints The most obvious step is to use better checkpoints. The Stable Diffusion model is a good starting point, and since its official launch, several improved versions have also been released. However, using a newer version doesn’t automatically mean you’ll get better results. You’ll still have to experiment with different checkpoints yourself, and do a little research (such as using negative prompts) to get the best results. As the field grows, there are more and more high-quality checkpoints finetuned to produce certain styles. Try exploring the Hub and Diffusers Gallery to find one you’re interested in!  Better pipeline components You can also try replacing the current pipeline components with a newer version. Let’s try loading the latest autoencoder from Stability AI into the pipeline, and generate some images:   Copied from diffusers import AutoencoderKL