# Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation

arXiv: https://arxiv.org/abs/2405.14802 · HTML: https://mlsanity.com/paper/2405.14802/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 51 of its 62 references that are in the map.
- First posted on arXiv 23 May 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 30 of 51)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022)
- [Simple diffusion: End-to-end diffusion for high resolution images](https://mlsanity.com/paper/2301.11093/) (2023)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [DeepCache: Accelerating Diffusion Models for Free](https://mlsanity.com/paper/2312.00858/) (2023)
- [Q-Diffusion: Quantizing Diffusion Models](https://mlsanity.com/paper/2302.04304/) (2023)
- [Post-training Quantization on Diffusion Models](https://mlsanity.com/paper/2211.15736/) (2022)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [On the Importance of Noise Scheduling for Diffusion Models](https://mlsanity.com/paper/2301.10972/) (2023)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality](https://mlsanity.com/paper/2202.05830/) (2022)

## Built on by (most cited first, 30 of 33)

- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis](https://mlsanity.com/paper/2407.07295/) (2024)
- [VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis](https://mlsanity.com/paper/2503.12758/) (2025)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [Interactive Gadolinium-Free MRI Synthesis: A Transformer with Localization Prompt Learning](https://mlsanity.com/paper/2503.01265/) (2025)
- [PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts](https://mlsanity.com/paper/2502.21260/) (2025)
- [Diffusion Bridge Models for 3D Medical Image Translation](https://mlsanity.com/paper/2504.15267/) (2025)
- [TimeFlow: Towards Stochastic-Aware and Efficient Time Series Generation via Flow Matching Modeling](https://mlsanity.com/paper/2511.07968/) (2025)
- [Early Lung Cancer Diagnosis from Virtual Follow-up LDCT Generation via Correlational Autoencoder and Latent Flow Matching](https://mlsanity.com/paper/2511.18185/) (2025)
- [MRI-to-CT synthesis using drifting models](https://mlsanity.com/paper/2603.28498/) (2026)
- [Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds](https://mlsanity.com/paper/2503.08515/) (2025)
- [PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CT](https://mlsanity.com/paper/2507.05317/) (2025)
- [MAN: Latent Diffusion Enhanced Multistage Anti-Noise Network for Efficient and High-Quality Low-Dose CT Image Denoising](https://mlsanity.com/paper/2509.23603/) (2025)
- [Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation](https://mlsanity.com/paper/2510.06131/) (2025)
- [SRUG: A Fusion-Driven Generator Network for Medical Image Translation](https://mlsanity.com/paper/2601.04785/) (2026)
- [MRI Cross-Modal Synthesis: A Comparative Study of Generative Models for T1-to-T2 Reconstruction](https://mlsanity.com/paper/2602.07068/) (2026)
- [Heterogeneous Decentralized Diffusion Models](https://mlsanity.com/paper/2603.06741/) (2026)
- [Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops](https://mlsanity.com/paper/2606.14149/) (2026)
- [Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation](https://mlsanity.com/paper/2410.12542/) (2024)
- [Geodesic Diffusion Models for Efficient Medical Image Enhancement](https://mlsanity.com/paper/2503.00745/) (2025)
- [Deterministic Medical Image Translation via High-fidelity Brownian Bridges](https://mlsanity.com/paper/2503.22531/) (2025)
- [Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients](https://mlsanity.com/paper/2506.11297/) (2025)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)
- [Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model](https://mlsanity.com/paper/2511.16207/) (2025)
- [Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2511.21320/) (2025)
- [Diffusion-Based Synthesis of 3D T1w MPRAGE Images from Multi-Echo GRE with Multi-Parametric MRI Integration](https://mlsanity.com/paper/2512.01135/) (2025)
- [TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising](https://mlsanity.com/paper/2603.20868/) (2026)
- [Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generation](https://mlsanity.com/paper/2603.22509/) (2026)
- [HUydra: Full-Range Lung CT Synthesis via Multiple HU Interval Generative Modelling](https://mlsanity.com/paper/2603.23041/) (2026)
- [Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement](https://mlsanity.com/paper/2604.17773/) (2026)

## Abstract (arXiv)

Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for disease diagnosis and treatment planning. This is primarily due to the high computational cost associated with (1) the use of large number of time steps (e.g., 1,000) in diffusion processes and (2) the increased dimensionality of medical images, which are often 3D or 4D. Training a diffusion model on medical images typically takes days to weeks, while sampling each image volume takes minutes to hours. To address this challenge, we introduce Fast-DDPM, a simple yet effective approach capable of improving training speed, sampling speed, and generation quality simultaneously. Unlike DDPM, which trains the image denoiser across 1,000 time steps, Fast-DDPM trains and samples using only 10 time steps. The key to our method lies in aligning the training and sampling procedures to optimize time-step utilization. Specifically, we introduced two efficient noise schedulers with 10 time steps: one with uniform time step sampling and another with non-uniform sampling. We evaluated Fast-DDPM across three medical image-to-image generation tasks: multi-image super-resolution, image denoising, and image-to-image translation. Fast-DDPM outperformed DDPM and current state-of-the-art methods based on convolutional networks and generative adversarial networks in all tasks. Additionally, Fast-DDPM reduced the training time to 0.2x and the sampling time to 0.01x compared to DDPM. Our code is publicly available at: https://github.com/mirthAI/Fast-DDPM.