RAGText2CT Weights

Weights for RAGText2CT: Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation.

This release is independent from dmolino/text2ct-weights and contains the full checkpoint set needed by the RAGText2CT-Release codebase.

Included Files

Under models/:

• autoencoder_epoch273.pt
• unet_rflow_200ep.pt
• CLIP3D_Finding_Impression_30ep.pt
• controlnet_rag_best.pt

Under configs/:

• config_rag_rflow.json

What Each Weight Does

• autoencoder_epoch273.pt: 3D VAE for latent compression and decoding.
• unet_rflow_200ep.pt: text-conditioned latent diffusion UNet from the Text2CT backbone.
• CLIP3D_Finding_Impression_30ep.pt: CLIP3D report encoder checkpoint.
• controlnet_rag_best.pt: retrieval-guided anatomical ControlNet checkpoint for RAGText2CT.

Intended Use

These checkpoints are intended for research on text-conditioned 3D CT generation and retrieval-augmented anatomical guidance.

They are not intended for clinical use or diagnostic decision making.

Code

Use these weights with the companion repository:

• RAGText2CT-Release

The code release expects the files to live under models/ with the names above.

Notes

• The first three checkpoints are shared with the original Text2CT pipeline.
• controlnet_rag_best.pt is the additional checkpoint specific to the retrieval-augmented extension.
• Retrieval-bank artifacts such as impression_embeddings.npy and impression_paths.json are not included in this weights repo.

Citation

@article{Molino2026RAGText2CT,
  title={Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation},
  author={Molino, Daniele and Caruso, Camillo Maria and Soda, Paolo and Guarrasi, Valerio},
  year={2026},
  journal={arXiv preprint arXiv:2603.08305}
}