Latent Space Diffusion

Latent space diffusion models leverage the power of diffusion processes within compressed representations of data, primarily images and 3D shapes, to achieve efficient and high-quality generation and manipulation. Current research focuses on improving controllability within these latent spaces, accelerating sampling through optimized algorithms and novel architectures like U-Nets and Transformers, and enhancing applications such as image inpainting, translation, and restoration. This approach offers significant advantages in terms of computational efficiency and sample quality, impacting diverse fields from computer vision and generative AI to recommendation systems and digital watermarking.