10 A deep dive into Stable Diffusion
This chapter covers
- Differences between Stable Diffusion and the latent diffusion model
- Integrating Stable Diffusion into the diffusers library
- Various components of Stable Diffusion and their roles in text-to-image generation
- Interpolating text embeddings to generate a series of images that smoothly transition from one image to another
In the world of text-to-image generation, Stable Diffusion stands out as one of the most powerful and accessible models. Building on the latent diffusion architecture discussed in the previous chapter, Stable Diffusion pushes the boundaries of image quality, flexibility, and speed, all while remaining open-source and free for anyone to use.
Developed collaboratively by CompVis, Stability AI, and LAION, Stable Diffusion draws upon the breakthrough techniques introduced by Rombach et al. (2022), combining them with advanced optimizations and a vastly expanded training dataset.[1] While the original latent diffusion model (LDM) was trained on the 400-million image LAION-400M dataset, Stable Diffusion uses a curated subset of the even larger LAION-5B, unlocking a richer diversity of visual concepts and styles.