Denoising Diffusion Probabilistic Models (DDPM)Product25/100 via “latent-space-diffusion-for-efficient-high-resolution-generation”
* 🏆 2020: [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)](https://arxiv.org/abs/2010.11929)
Unique: Latent-space diffusion (e.g., Stable Diffusion) applies DDPM in a learned VAE latent space rather than pixel space, reducing computational cost by ~50-100x due to spatial compression. The VAE is trained separately (or jointly) to compress images while preserving semantic information. This approach enables efficient high-resolution generation without sacrificing quality, making it practical for consumer deployment.
vs others: 50-100x more efficient than pixel-space diffusion for high-resolution generation, enables real-time applications, and maintains comparable quality to pixel-space models through careful VAE design.