Fast Samplers for Inverse Problems in Iterative Refinement Models
CoRR(2024)
摘要
Constructing fast samplers for unconditional diffusion and flow-matching
models has received much attention recently; however, existing methods for
solving inverse problems, such as super-resolution, inpainting, or deblurring,
still require hundreds to thousands of iterative steps to obtain high-quality
results. We propose a plug-and-play framework for constructing efficient
samplers for inverse problems, requiring only pre-trained diffusion or
flow-matching models. We present Conditional Conjugate Integrators, which
leverage the specific form of the inverse problem to project the respective
conditional diffusion/flow dynamics into a more amenable space for sampling.
Our method complements popular posterior approximation methods for solving
inverse problems using diffusion/flow models. We evaluate the proposed method's
performance on various linear image restoration tasks across multiple datasets,
employing diffusion and flow-matching models. Notably, on challenging inverse
problems like 4× super-resolution on the ImageNet dataset, our method
can generate high-quality samples in as few as 5 conditional sampling steps and
outperforms competing baselines requiring 20-1000 steps. Our code and models
will be publicly available at https://github.com/mandt-lab/CI2RM.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要