Kavli Affiliate: Matthew Fisher
| First 5 Authors: Vikas Thamizharasan, Nikitas Chatzis, Iliyan Georgiev, Matthew Fisher, Difan Liu
| Summary:
We present mean-shift distillation, a novel diffusion distillation technique
that provides a provably good proxy for the gradient of the diffusion output
distribution. This is derived directly from mean-shift mode seeking on the
distribution, and we show that its extrema are aligned with the modes. We
further derive an efficient product distribution sampling procedure to evaluate
the gradient. Our method is formulated as a drop-in replacement for score
distillation sampling (SDS), requiring neither model retraining nor extensive
modification of the sampling procedure. We show that it exhibits superior mode
alignment as well as improved convergence in both synthetic and practical
setups, yielding higher-fidelity results when applied to both text-to-image and
text-to-3D applications with Stable Diffusion.
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