Pseudo Numerical Methods for Diffusion Models on Manifolds
Authors: Luping Liu, Yi Ren, Zhijie Lin, Zhou Zhao
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct unconditional image generation experiments on four datasets: Cifar10 (32 32) (Krizhevsky et al., 2009), Celeb A (64 64) (Liu et al., 2015), LSUN-church (256 256) and LSUN-bedroom (256 256) (Yu et al., 2016). To analyze the acceleration effect, we test Fenchel Inception Distance (FID (Heusel et al., 2018)) on different datasets under different steps and different numerical methods, including DDIMs, S-PNDMs, F-PNDMs and classical fourth-order numerical methods (FONs) (e.g., Runge-Kutta method and linear multi-step method). |
| Researcher Affiliation | Academia | Luping Liu, Yi Ren, Zhijie Lin & Zhou Zhao Zhejiang University {luping.liu,rayeren,linzhijie,zhaozhou}@zju.edu.cn |
| Pseudocode | Yes | Algorithm 1 DDIMs and Algorithm 2 PNDMs |
| Open Source Code | Yes | Our implementation is available at https://github.com/luping-liu/PNDM. |
| Open Datasets | Yes | We conduct unconditional image generation experiments on four datasets: Cifar10 (32 32) (Krizhevsky et al., 2009), Celeb A (64 64) (Liu et al., 2015), LSUN-church (256 256) and LSUN-bedroom (256 256) (Yu et al., 2016). |
| Dataset Splits | No | No explicit information on training/validation/test dataset splits is provided beyond using pre-trained models. |
| Hardware Specification | Yes | We use the 50-step, 512 batch size experiment on an RTX3090 to test the computational cost and the column time is the average computational cost per step in seconds. |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., libraries, frameworks) are provided. |
| Experiment Setup | Yes | The pre-trained models for Cifar10, LSUN-church and LSUN-bedroom are taken from Ho et al. (2020) and the pre-trained model for Celeb A is taken from Song et al. (2020a). In these models, the number of total steps N is 1000 and the variance schedule is linear variance schedule. We use the 50-step, 512 batch size experiment on an RTX3090 to test the computational cost and the column time is the average computational cost per step in seconds. |