Diffusion models for image and video generation have been surging in popularity, delivering super-realistic visual media. However, their adoption is often constrained by the sheer requirements in memory and compute. Quantization is essential for efficient serving of these models.
In this post, we demonstrate reproducible end-to-end inference speedups of up to 1.26x with MXFP8 and 1.68x with NVFP4 ...
The narrative presents a compelling case for the adoption of quantization techniques like MXFP8 and NVFP4 to enhance the efficiency of diffusion models. The strongest version of this argument highlights the significant speedups and memory reductions achieved without substantial loss in visual quality, as evidenced by LPIPS scores. The use of selective quantization and CUDA Graphs further optimizes performance, demonstrating a thoughtful approach to balancing speed and accuracy.
However, it's imp...