Summary

In this chapter, we learned how CLIP helps in aligning embeddings of both text and images. We then gained an understanding of how to leverage the SAM to perform segmentation on any image. Next, we learned about speeding up the SAM using FastSAM. Finally, we learned about leveraging diffusion models to generate images both unconditionally and conditionally given a prompt.

We covered sending different modalities of prompts to the segment-anything model, tracking objects using the SAM, and combining multiple modalities using ImageBind in the associated GitHub repository.

With this knowledge, you can leverage the foundational models on your data/tasks with very limited/no training data points, such as training/leveraging models for the segmentation/object detection tasks that we learned about in Chapters 7 to 9 with minimal/no data.

In the next chapter, you will learn about tweaking diffusion models further to generate images of interest to you.