What are the key advancements in diffusion models mentioned in the conclusion of the book?

The key advancements in diffusion models include the introduction of DDPM and DDIM in 2020, which improved image generation quality and stability compared to GANs. Latent diffusion, introduced in 2021, allowed diffusion models to be trained within the latent space of an autoencoder, powering models like Stable Diffusion.

Diffusion models have seen significant advancements, particularly with the introduction of DDPM (Denoising Diffusion Probabilistic Models) and DDIM (Denoising Diffusion Implicit Models) in 2020. These models provided a more stable training process and high-quality image generation, rivaling GANs. Another major advancement was the development of latent diffusion in 2021, which involves training diffusion models within the latent space of an autoencoder. This technique was crucial for the development of the Stable Diffusion model, which is open source and allows users to run it on their own hardware.