ControlRadio is a prompt-driven controllable diffusion framework for cross-modal radio map generation. It combines natural-language descriptions with environmental layouts, including building morphology, transmitter locations, and optional dynamic-object cues, to synthesize structurally consistent and propagation-plausible radio maps.
This repository hosts the pretrained weights. Training, evaluation, and inference code is maintained in the ControlRadio GitHub repository.
prompt-guided semantic conditioning with a frozen OpenCLIP text encoder;
a Layout-Aware ControlNet for building, transmitter, and optional car conditions;
a Noise Controller for controlling the latent prior used at inference time; and
decoupled two-stage fine-tuning of a radio-domain VAE followed by the U-Net and ControlNet.
The released files are PyTorch Lightning checkpoints used by the research code. They are not standalone Diffusers or Transformers checkpoints and cannot be loaded directly with from_pretrained().
Each experiment directory also contains the corresponding hparams.yaml. Keep the downloaded directory structure unchanged because the evaluation and inference scripts construct checkpoint paths from the command-line configuration.
Installation and Download
Clone the code repository and create the reference environment:
Select ControlRadio-SRM with --carsInput no or ControlRadio-DRM with --carsInput yes. Use --test_simulation DPM, IRT2, or IRT4 to select the evaluation target. The paper uses Noise Controller settings --means -0.1 --vars 0.001 selected on the validation set.
Results reported in the paper on the RadioMapSeer benchmark are:
Scenario
RMSE ↓
NMSE ↓
PSNR ↑
SSIM ↑
SRM (DPM without cars)
0.0166
0.0024
35.86
0.9787
DRM (DPM with cars)
0.0180
0.0028
35.29
0.9759
IRT4 (without cars)
0.0210
0.0040
33.46
0.9688
Training Data
The models were trained and evaluated with RadioMapSeer. The code follows the paper's map-level split: maps 0–499 for training, 500–599 for validation, and 600–700 for testing. Refer to the dataset provider for its terms and documentation.
Intended Use and Limitations
ControlRadio is released for research on prompt- and layout-conditioned radio-map generation. The reported results compare generated maps with WinProp-derived references on simulated, previously unseen layouts.
The released checkpoints have not been validated as replacements for site-specific electromagnetic solvers or field measurements.
Performance should not be interpreted as sim-to-real validation or strict electromagnetic equivalence.
Predictions depend on data preprocessing, prompt construction, coordinate conventions, and inference settings matching the released code.
Users are responsible for validating generated maps before applying them to safety-critical or operational wireless-network decisions.
License
The model weights are released under the openrail++ license metadata, consistent with the Stable Diffusion 2.1 Base foundation model. The ControlRadio source code is separately released under the MIT License.
Citation
bibtex
1@article{liu2026controlradio,
2 title = {ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation},
3 author = {Liu, Kangjun and Pan, Xiying and Zhang, Shuhang and Xiang, Xiang and Chen, Ke and Wang, Yaowei},
4 journal = {arXiv preprint arXiv:2608.09357},
5 year = {2026}
6}
