Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
Authors: Xunpeng Huang, Difan Zou, Hanze Dong, Zhang, Yian Ma, Tong Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments support our theory. In this section, we conduct experiments when the target distribution p is a Mixture of Gaussian (Mo G) and compare RTK-based methods with traditional DDPM. |
| Researcher Affiliation | Collaboration | Xunpeng Huang HKUST xhuangck@connect.ust.hk Difan Zou HKU dzou@cs.hku.hk Hanze Dong Salesforce AI Research hanze.dong@salesforce.com Yi Zhang HKU yizhang101@connect.hku.hk Yian Ma UC San Diego yianma@ucsd.edu Tong Zhang UIUC tongzhang@tongzhang-ml.org |
| Pseudocode | Yes | Algorithm 1 INFERENCE WITH REVERSE TRANSITION KERNEL (RTK) Algorithm 2 MALA/PROJECTED MALA FOR RTK INFERENCE Algorithm 3 ULD FOR RTK INFERENCE |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | In this section, we conduct experiments when the target distribution p is a Mixture of Gaussian (Mo G) and compare RTK-based methods with traditional DDPM. Furthermore, we conducted experiments on the MNIST dataset, as shown in Figure 6. |
| Dataset Splits | No | The paper mentions training models but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments are taken on a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Specifically, while DDPM models xη across a sequence of η timesteps spanning from 0 to T in increments of 0.001 T (i.e., [0, 0.001T, 0.002T, . . . , T]), we execute Alg. 1, 2, and 3 at fewer timesteps within x[0,0.2T,0.4T,0.6T,0.8T ], and we distribute the NFE uniformly to these timesteps for MCMC. |