# Fast-DiM: Towards Fast Diffusion Morphs

arXiv: https://arxiv.org/abs/2310.09484 · HTML: https://mlsanity.com/paper/2310.09484/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [Diffusion Autoencoders: Toward a Meaningful and Decodable Representation](https://mlsanity.com/paper/2111.15640/) (2021)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [Morphing Attack Potential](https://mlsanity.com/paper/2204.13374/) (2022)
- [Leveraging Diffusion For Strong and High Quality Face Morphing Attacks](https://mlsanity.com/paper/2301.04218/) (2023)

## Built on by (most cited first, 6 of 6)

- [Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs](https://mlsanity.com/paper/2404.06025/) (2024)
- [AdjointDEIS: Efficient Gradients for Diffusion Models](https://mlsanity.com/paper/2405.15020/) (2024)
- [The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios](https://mlsanity.com/paper/2404.06559/) (2024)
- [FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection](https://mlsanity.com/paper/2601.20656/) (2026)
- [WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation](https://mlsanity.com/paper/2507.12493/) (2025)
- [DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection](https://mlsanity.com/paper/2604.17961/) (2026)

## Abstract (arXiv)

Diffusion Morphs (DiM) are a recent state-of-the-art method for creating high quality face morphs; however, they require a high number of network function evaluations (NFE) to create the morphs. We propose a new DiM pipeline, Fast-DiM, which can create morphs of a similar quality but with fewer NFE. We investigate the ODE solvers used to solve the Probability Flow ODE and the impact they have on the the creation of face morphs. Additionally, we employ an alternative method for encoding images into the latent space of the Diffusion model by solving the Probability Flow ODE as time runs forwards. Our experiments show that we can reduce the NFE by upwards of 85% in the encoding process while experiencing only 1.6% reduction in Mated Morph Presentation Match Rate (MMPMR). Likewise, we showed we could cut NFE, in the sampling process, in half with only a maximal reduction of 0.23% in MMPMR.