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README.md

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+# 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): *73.41%*
+
+## Limitations
+
+The MonoSpace model has the following limitations:
+
+- Lack of Robust Latent High Frequency (RLHF): 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.