Model Card for FFHQ 64x64 R3GAN Model
This model card provides details about the R3GAN model trained on the FFHQ-64 dataset found in the NeurIPS 2024 paper:
https://arxiv.org/abs/2501.05441Model Details
The model achieves 1.95 Frechet Inception Distance-50k on class conditional FFHQ-64 generation.
Model Description
This model is a generative adversarial network (GAN) based on the R3GAN architecture, specifically trained to synthesize high-quality and realistic images from the ImageNet dataset.
- Developed by: Brown University and Cornell University
- Funded by: National Science Foundation and National Institute of Health (See paper for funding details)
- Shared by: [Optional: Specify sharer if different from developer]
- Model type: Generative Adversarial Network
- Language(s) (NLP): N/A
- License: [Specify License, e.g., MIT, Apache 2.0, or a custom license]
- Finetuned from model: N/A
Model Sources
- Repository: https://github.com/brownvc/R3GAN/
- Paper: https://arxiv.org/pdf/2501.05441
- Demo: [Optional: Provide a link to a demo or example usage]
Uses
Direct Use
This model can be used to generate high-resolution images similar to those in the FFHQ dataset. Its primary application includes research in generative models and image synthesis.
Downstream Use
The model can be fine-tuned for specific subsets of the FFHQ dataset or other similar datasets for domain-specific image generation tasks.
Out-of-Scope Use
The model should not be used for generating deceptive or misleading content, malicious purposes, or tasks where realistic image synthesis could cause harm.
Bias, Risks, and Limitations
The model inherits biases present in the FFHQ dataset, including potential overrepresentation or underrepresentation of certain classes. Users should critically evaluate and mitigate biases before deploying the model.
Recommendations
- Avoid using the model for sensitive applications without thorough bias evaluation.
- Ensure appropriate credit is given when publishing or sharing generated images.
How to Get Started with the Model
Below is an example of how to use the model for image generation:
