Accurate Compression of Text-to-Image Diffusion Models via Vector
  Quantization
Authors: name: Vage Egiazarian, name: Denis Kuznedelev, name: Anton Voronov, name: Ruslan Svirschevski, name: Michael Goin, name: Daniil Pavlov, name: Dan Alistarh, name: Dmitry Baranchuk

Text-to-image diffusion models have emerged as a powerful framework for
high-quality image generation given textual prompts. Their success has driven
the rapid development of production-grade diffusion models that consistently
increase in size and already contain billions of parameters. As a result,
state-of-the-art text-to-image models are becoming less accessible in practice,
especially in resource-limited environments. Post-training quantization (PTQ)
tackles this issue by compressing the pretrained model weights into lower-bit
representations. Recent diffusion quantization techniques primarily rely on
uniform scalar quantization, providing decent performance for the models
compressed to 4 bits. This work demonstrates that more versatile vector
quantization (VQ) may achieve higher compression rates for large-scale
text-to-image diffusion models. Specifically, we tailor vector-based PTQ
methods to recent billion-scale text-to-image models (SDXL and SDXL-Turbo), and
show that the diffusion models of 2B+ parameters compressed to around 3 bits
using VQ exhibit the similar image quality and textual alignment as previous
4-bit compression techniques.