Denoising Diffusion Probabilistic Models (DDPM)Product25/100 via “accelerated-sampling-via-step-reduction”
* 🏆 2020: [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)](https://arxiv.org/abs/2010.11929)
Unique: DDPM's reverse process can be reformulated as an ODE (via DDIM), enabling deterministic sampling with arbitrary step counts. This insight enables 10-20x speedup by skipping timesteps while maintaining reasonable sample quality. The approach uses higher-order numerical solvers (e.g., DPM-Solver) to approximate the ODE trajectory with fewer steps, trading off quality for speed in a principled manner.
vs others: Much faster than full DDPM sampling (10-20x speedup), maintains better quality than naive step skipping, and enables real-time applications impossible with standard diffusion sampling.