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SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
(cdn.prod.website-files.com)
TL;DR
A new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5× memory and 8.7× latency reduction on a 16GB laptop 4090 GPU.
Paper: http://arxiv.org/abs/2411.05007
Weights: https://huggingface.co/mit-han-lab/svdquant-models
Code: https://github.com/mit-han-lab/nunchaku
Blog: https://hanlab.mit.edu/blog/svdquant
Demo: https://svdquant.mit.edu/
