NV-Generate-MR is a state-of-the-art three-dimensional (3D) latent diffusion model designed to generate high-quality synthetic magnetic resonance (MR) images with or without anatomical annotations. The model excels at data augmentation and at generating realistic medical imaging data to supplement datasets limited by privacy concerns or the rarity of certain conditions. It can also significantly enhance the performance of other medical imaging AI models by generating diverse, realistic training data.
This model is for research and development only.
You can try this model via NVIDIA MAISI demo:
👉 [https://huggingface.co/spaces/nvidia/nv-generate)
The current demo showcases only a subset of the model’s capabilities. For full functionality and advanced usage, please refer to the GitHub repository.
Training and inference code are in: https://github.com/NVIDIA-Medtech/NV-Generate-CTMR/tree/main.
NVIDIA OneWay Non-Commercial License for academic research purposes
Global
Medical researchers, AI developers, and healthcare institutions would be expected to use this system for generating synthetic MR training data, data augmentation for rare conditions, and advancing AI applications in healthcare research.
For example, to download the VAE, you can run:
pip install -U huggingface_hub
huggingface-cli download nvidia/NV-Generate-MR \
models/autoencoder_v2.pt \
--local-dir ./models
Huggingface: 10/27/2025 via https://huggingface.co/NVIDIA
[1] Zhao, Can, et al. "Maisi-v2: Accelerated 3d high-resolution medical image synthesis with rectified flow and region-specific contrastive loss." arXiv preprint arXiv:2508.05772 (2025).
[2] Rombach, Robin, et al. "High-resolution image synthesis with latent diffusion models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf
[3] Lvmin Zhang, Anyi Rao, Maneesh Agrawala; "Adding Conditional Control to Text-to-Image Diffusion Models." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3836-3847. https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.pdf
Architecture Type: Transformer Network Architecture: 3D UNet + attention blocks
This model was developed from scratch using MONAI components. Number of model parameters: 240M
Input Type(s): Integer, List, Array Input Format(s): Integer values, String arrays, Float arrays Input Parameters: Number of Samples (1D), Body Region (1D), Anatomy List (1D), Output Size (1D), and Spacing (1D) Other Properties Related to Input: Supports controllable synthetic MR generation with flexible body region selection, optional anatomical class specification (up to 127 classes), customizable output dimensions, configurable voxel spacing (0.5-5.0mm), and controllable anatomy sizing.
Output Type(s): Image Output Format: Neuroimaging Informatics Technology Initiative (NIfTI), Digital Imaging and Communications in Medicine (DICOM), Nearly Raw Raster Data (Nrrd) Output Parameters: Three-Dimensional (3D) Other Properties Related to Output: Synthetic MR images with dimensions up to 512×512×768 voxels and spacing between 0.5mm and 5.0mm, with controllable anatomy sizes as specified. When anatomy_list is provided, an additional NIfTI file containing the corresponding segmentation mask is generated.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (GPU cores) and software frameworks (CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
0.1 - Initial release version for synthetic MR image generation
Total Size: ~20,000 3D volumes Total Number of Datasets: ~17 datasets
Public datasets from multiple scanner types were processed to create high-quality 3D MR volumes with corresponding anatomical annotations. The data processing pipeline ensured consistent voxel spacing, standardized orientations, and validated anatomical segmentations.
Data Modality:
Image Training Data Size:
Data Collection Method by dataset:
Labeling Method by dataset:
Data Collection Method by dataset:
Labeling Method by dataset:
Data Collection Method by dataset:
Labeling Method by dataset:
Acceleration Engine: PyTorch Test Hardware:
NV-Generate-MR supports comprehensive anatomical segmentation with the following categories:
Core Organs and Systems:
Skeletal System:
Detailed Brain Segmentation: Comprehensive brain parcellation including ventricles, cortical regions, subcortical structures, and specialized brain areas (214-345) based on neuroanatomical atlases.
Pathological Structures:
Specialized Regions:
Complete numerical mapping and deprecated classes available in model documentation.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Please report model quality, risk, security vulnerabilities or concerns here.
NV-Generate-MR is a state-of-the-art three-dimensional (3D) latent diffusion model designed to generate high-quality synthetic magnetic resonance (MR) images with or without anatomical annotations. The model excels at data augmentation and at generating realistic medical imaging data to supplement datasets limited by privacy concerns or the rarity of certain conditions. It can also significantly enhance the performance of other medical imaging AI models by generating diverse, realistic training data.
This model is for research and development only.
You can try this model via NVIDIA MAISI demo:
👉 [https://huggingface.co/spaces/nvidia/nv-generate)
The current demo showcases only a subset of the model’s capabilities. For full functionality and advanced usage, please refer to the GitHub repository.
Training and inference code are in: https://github.com/NVIDIA-Medtech/NV-Generate-CTMR/tree/main.
NVIDIA OneWay Non-Commercial License for academic research purposes
Global
Medical researchers, AI developers, and healthcare institutions would be expected to use this system for generating synthetic MR training data, data augmentation for rare conditions, and advancing AI applications in healthcare research.
For example, to download the VAE, you can run:
pip install -U huggingface_hub
huggingface-cli download nvidia/NV-Generate-MR \
models/autoencoder_v2.pt \
--local-dir ./models
Huggingface: 10/27/2025 via https://huggingface.co/NVIDIA
[1] Zhao, Can, et al. "Maisi-v2: Accelerated 3d high-resolution medical image synthesis with rectified flow and region-specific contrastive loss." arXiv preprint arXiv:2508.05772 (2025).
[2] Rombach, Robin, et al. "High-resolution image synthesis with latent diffusion models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf
[3] Lvmin Zhang, Anyi Rao, Maneesh Agrawala; "Adding Conditional Control to Text-to-Image Diffusion Models." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3836-3847. https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.pdf
Architecture Type: Transformer Network Architecture: 3D UNet + attention blocks
This model was developed from scratch using MONAI components. Number of model parameters: 240M
Input Type(s): Integer, List, Array Input Format(s): Integer values, String arrays, Float arrays Input Parameters: Number of Samples (1D), Body Region (1D), Anatomy List (1D), Output Size (1D), and Spacing (1D) Other Properties Related to Input: Supports controllable synthetic MR generation with flexible body region selection, optional anatomical class specification (up to 127 classes), customizable output dimensions, configurable voxel spacing (0.5-5.0mm), and controllable anatomy sizing.
Output Type(s): Image Output Format: Neuroimaging Informatics Technology Initiative (NIfTI), Digital Imaging and Communications in Medicine (DICOM), Nearly Raw Raster Data (Nrrd) Output Parameters: Three-Dimensional (3D) Other Properties Related to Output: Synthetic MR images with dimensions up to 512×512×768 voxels and spacing between 0.5mm and 5.0mm, with controllable anatomy sizes as specified. When anatomy_list is provided, an additional NIfTI file containing the corresponding segmentation mask is generated.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (GPU cores) and software frameworks (CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
0.1 - Initial release version for synthetic MR image generation
Total Size: ~20,000 3D volumes Total Number of Datasets: ~17 datasets
Public datasets from multiple scanner types were processed to create high-quality 3D MR volumes with corresponding anatomical annotations. The data processing pipeline ensured consistent voxel spacing, standardized orientations, and validated anatomical segmentations.
Data Modality:
Image Training Data Size:
Data Collection Method by dataset:
Labeling Method by dataset:
Data Collection Method by dataset:
Labeling Method by dataset:
Data Collection Method by dataset:
Labeling Method by dataset:
Acceleration Engine: PyTorch Test Hardware:
NV-Generate-MR supports comprehensive anatomical segmentation with the following categories:
Core Organs and Systems:
Skeletal System:
Detailed Brain Segmentation: Comprehensive brain parcellation including ventricles, cortical regions, subcortical structures, and specialized brain areas (214-345) based on neuroanatomical atlases.
Pathological Structures:
Specialized Regions:
Complete numerical mapping and deprecated classes available in model documentation.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
Please report model quality, risk, security vulnerabilities or concerns here.