MonoSpace

Model Card for MonoSpace

Model description

MonoSpace is a latent diffusion model developed for a university image synthesis project. The model is designed as a starting point for higher-order diffusion models and focuses on generating high-quality images. MonoSpace has been trained on a subset of ImageNet and LaiOn Hires dataset, showcasing its potential in computer vision tasks.

Intended use

MonoSpace is intended for:

• Image synthesis
• Image classification
• Object detection

As a starting point, MonoSpace aims to provide a solid foundation for researchers and developers working on higher-order diffusion models and other advanced image synthesis techniques.

Training data

The MonoSpace model was trained on a combination of image datasets:

• ImageNet (subset)
• LaiOn Hires

Model performance

MonoSpace achieved promising results on image synthesis and classification tasks:

• ImageNet Top-1 Accuracy (subset): 83.41%

Limitations

The MonoSpace model has the following limitations:

• Lack of Robust Latent High Frequency: The model may not capture high-frequency details as effectively as desired.
• Limited training resources: Due to constraints on computational resources during development, the model might not be optimized to its full potential.

Responsible AI

To ensure ethical use of MonoSpace, we encourage users to follow Hugging Face's guidelines on responsible AI practices, including fairness, transparency, and accountability.

License

The MonoSpace model is available under the bigscience-openrail-m.