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README.md

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+# Text-to-CT Model Weights
+
+Checkpoints for **“Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining”** (Molino et al., 2025).
+---
+
+## Model Card for Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining
+
+### Model Description
+- **Authors:** Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda, Valerio Guarrasi  
+- **Model type:** 3D latent diffusion (RFlow) + 3D VAE + CLIP3D text encoder for CT generation.  
+- **License:** Apache 2.0 (same as code release).  
+- **Sources:** Code https://github.com/cosbidev/Text2CT | Paper https://arxiv.org/abs/2506.00633  
+- **Demo:** Use `diff_model_demo.py` from the code release for a one-off generation from text.
+
+### Intended Use
+- **Direct use:** Research/experimentation on text-conditioned 3D CT synthesis; generating synthetic data for benchmarking or augmentation.  
+- **Downstream use:** Fine-tuning or integration into broader research pipelines.  
+- **Out of scope:** Clinical decision-making, diagnostic use, or deployment without proper validation and approvals.
+
+### Risks & Limitations
+- Trained on CT-RATE; may encode dataset biases and is not validated for clinical use.
+- Synthetic outputs may contain artifacts; do not use for patient care.
+
+### Files included
+- `autoencoder_epoch273.pt` — 3D VAE for latent compression/decoding.
+- `unet_rflow_200ep.pt` — Diffusion UNet trained with rectified flow.
+- `CLIP3D_Finding_Impression_30ep.pt` — CLIP3D weights for encoding reports.
+
+### How to Get Started (Python)
+```python
+from huggingface_hub import hf_hub_download
+repo_id = "yourname/text2ct-weights"  # replace with the actual repo id
+
+autoencoder_path = hf_hub_download(repo_id, "autoencoder_epoch273.pt")
+unet_path = hf_hub_download(repo_id, "unet_rflow_200ep.pt")
+clip_path = hf_hub_download(repo_id, "CLIP3D_Finding_Impression_30ep.pt")
+
+# Use these in the code release configs:
+# trained_autoencoder_path -> autoencoder_path
+# existing_ckpt_filepath / model_filename -> unet_path
+# clip_weights (for report embeddings) -> clip_path
+```
+
+### Training Data (for these weights)
+- CT-RATE dataset (public on Hugging Face) for CT volumes and reports.
+
+### Training Procedure (summary)
+- CLIP3D trained for vision-language alignment on CT+reports.
+- VAE checkpoint from https://github.com/Project-MONAI/tutorials/tree/main/generation/maisi.
+- Diffusion UNet trained with rectified flow (RFlow) in latent space, conditioned on text embeddings.
+
+### Evaluation
+- See paper for quantitative and qualitative results.
+
+### Further Information
+- 1,000 generated CT scans are available at https://huggingface.co/datasets/dmolino/CT-RATE_Generated_Scans.
+
+### Environmental Impact
+- Not reported. Training used multi-GPU setup;.
+
+### Citation
+If you use these weights or code, please cite the paper:
+```
+@article{molino2025text,
+  title={Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining},
+  author={Molino, Daniele and Caruso, Camillo Maria and Ruffini, Filippo and Soda, Paolo and Guarrasi, Valerio},
+  journal={arXiv preprint arXiv:2506.00633},
+  year={2025}
+}
+```