@inproceedings{
mcallister2024rethinking,
title={Rethinking Score Distillation as a Bridge Between Image Distributions},
author={David McAllister and Songwei Ge and Jia-Bin Huang and David W. Jacobs and Alexei A Efros and Aleksander Holynski and Angjoo Kanazawa},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=I8PkICj9kM}
}
Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS has a number of characteristic artifacts that limit its utility in general-purpose applications. In this paper, we make progress toward understanding the behavior of SDS and its variants by viewing them as solving an optimal-cost transport path from some current source distribution to a target distribution. Under this new interpretation, we argue that these methods' characteristic artifacts are caused by (1) linear approximation of the optimal path and (2) poor estimates of the source distribution.
We show that by calibrating the text conditioning of the source distribution, we can produce high-quality generation and translation results with little extra overhead. Our method can be easily applied across many domains, matching or beating the performance of specialized methods. We demonstrate its utility in text-to-2D, text-to-3D, translating paintings to real images, optical illusion generation, and 3D sketch-to-real. We compare our method to existing approaches for score distillation sampling and show that it can produce high-frequency details with realistic colors.
Rethinking Score Distillation as a Bridge Between Image Distributions
David McAllister Songwei Ge Jia-Bin Huang David W. Jacobs Alexei A Efros Aleksander Holynski Angjoo Kanazawa