metadata
license: mit
language:
- en
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
pipeline_tag: image-to-image
tags:
- medical
- CT
- MRI
- autoencoders
- VAE
- generative-ai
OpenVAE
OpenVAE is a medical-image VAE family for CT/MRI. It provides pretrained latent backbones for diffusion models, with better anatomical fidelity than general-image VAEs.
Contribution to Generative AI
OpenVAE brings domain-specific latent modeling to medical generative AI, making medical diffusion pipelines more reliable, reproducible, and easier to build.
Quick Start
import torch
from diffusers import AutoencoderKL
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load OpenVAE
vae = AutoencoderKL.from_pretrained("SMILE-project/OpenVAE", subfolder="vae").to(device)
vae.requires_grad_(False)
vae.eval()
img = torch.randn(1, 3, 512, 512, device=device)
with torch.no_grad():
# Encode to latent space
latent = vae.encode(img).latent_dist.sample()
# Decode to image space
reconstruction = vae.decode(latent).sample
Models
| Name | VAE Type | # Patients |
|---|---|---|
| stable-diffusion-v1-5 | KL-VAE | 0 |
| stable-diffusion-3.5-large | KL-VAE | 0 |
| OpenVAE-2D-4x-20K | KL-VAE | 20K |
| OpenVAE-2D-4x-100K | KL-VAE | 100K |
| OpenVAE-2D-4x-300K | KL-VAE | 300K |
| OpenVAE-2D-4x-PCCT_Enhanced | KL-VAE | 300K |
| OpenVAE-3D-4x-20K | KL-VAE | 20K |
| OpenVAE-3D-4x-100K | KL-VAE | 100K |
| OpenVAE-3D-4x-1M | KL-VAE | 1M |
| OpenVAE-3D-4x-100K-VQ | VQ-VAE | 100K |
| OpenVAE-3D-8x-100K-VQ | VQ-VAE | 100K |
Benchmarks
| Name | LPIPS | SSIM | PSNR | DSC |
|---|---|---|---|---|
| stable-diffusion-v1-5 | - | - | - | - |
| stable-diffusion-3.5-large | - | - | - | - |
| OpenVAE-2D-4x-20K | - | - | - | - |
| OpenVAE-2D-4x-100K | - | - | - | - |
| OpenVAE-2D-4x-300K | - | - | - | - |
| OpenVAE-2D-4x-PCCT_Enhanced | - | - | - | - |
| OpenVAE-3D-4x-20K | - | - | - | - |
| OpenVAE-3D-4x-100K | - | - | - | - |
| OpenVAE-3D-4x-1M | - | - | - | - |
| OpenVAE-3D-4x-100K-VQ | - | - | - | - |
| OpenVAE-3D-8x-100K-VQ | - | - | - | - |
Citation
@article{liu2025see,
title={See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement},
author={Liu, Junqi and Wu, Zejun and Bassi, Pedro RAS and Zhou, Xinze and Li, Wenxuan and Hamamci, Ibrahim E and Er, Sezgin and Lin, Tianyu and Luo, Yi and Płotka, Szymon and others},
journal={arXiv preprint arXiv:https://www.arxiv.org/abs/2512.07251},
year={2025},
url={https://github.com/MrGiovanni/SMILE}
}