# RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse

arXiv: https://arxiv.org/abs/2601.02790 · HTML: https://mlsanity.com/paper/2601.02790/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 22 of 22)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (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)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network](https://mlsanity.com/paper/2212.12817/) (2022)
- [U-shaped Vision Mamba for Single Image Dehazing](https://mlsanity.com/paper/2402.04139/) (2024)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (2020)
- [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025)
- [Toward Immersive Communications in 6G](https://mlsanity.com/paper/2303.05283/) (2023)
- [RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication](https://mlsanity.com/paper/2507.12166/) (2025)
- [The First Pathloss Radio Map Prediction Challenge](https://mlsanity.com/paper/2310.07658/) (2023)
- [Physics-Informed Representation Alignment for Sparse Radio-Map Reconstruction](https://mlsanity.com/paper/2501.19160/) (2025)
- [RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction](https://mlsanity.com/paper/2504.14298/) (2025)
- [Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling](https://mlsanity.com/paper/1911.03359/) (2019)
- [Channel Estimation for Extremely Large-Scale Massive MIMO Systems](https://mlsanity.com/paper/1910.05432/) (2019)
- [Massive MIMO Communication with Intelligent Reflecting Surface](https://mlsanity.com/paper/2107.04255/) (2021)

## Built on by (most cited first, 4 of 4)

- [RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction](https://mlsanity.com/paper/2603.18865/) (2026)
- [RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction](https://mlsanity.com/paper/2606.03074/) (2026)
- [RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization](https://mlsanity.com/paper/2607.08045/) (2026)
- [RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization](https://mlsanity.com/paper/2608.16167/) (2026)

## Abstract (arXiv)

Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed network entities and fast-changing environments, it is very challenging to meet real-time requirements. Although generative diffusion models (DMs) can achieve state-of-the-art accuracy with second-level delay, their iterative nature leads to prohibitive inference latency in delay-sensitive scenarios. In this paper, by uncovering a key structural property of diffusion processes: the latent midpoints remain highly consistent across semantically similar scenes, we propose RadioDiff-Flux, a novel two-stage latent diffusion framework that decouples static environmental modeling from dynamic refinement, enabling the reuse of precomputed midpoints to bypass redundant denoising. In particular, the first stage generates a coarse latent representation using only static scene features, which can be cached and shared across similar scenarios. The second stage adapts this representation to dynamic conditions and transmitter locations using a pre-trained model, thereby avoiding repeated early-stage computation. The proposed RadioDiff-Flux significantly reduces inference time while preserving fidelity. Experiment results show that RadioDiff-Flux can achieve up to 50 acceleration with less than 0.15% accuracy loss, demonstrating its practical utility for fast, scalable RM generation in future 6G networks.