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license: mit |
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library_name: diffusion-single-file |
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# Model Card for EpiDiff |
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<!-- Provide a quick summary of what the model is/does. --> |
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[EpiDiff](https://huanngzh.github.io/EpiDiff/) is a generative model based on Zero123 that takes an image of an object as a conditioning frame, and generates 16 multiviews of that object. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Model type:** Generative image-to-multiview model |
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- **License:** [More Information Needed] |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/huanngzh/EpiDiff |
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- **Paper:** https://arxiv.org/abs/2312.06725 |
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- **Demo:** https://huanngzh.github.io/EpiDiff/ |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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For usage instructions, please refer to [our EpiDiff GitHub repository](https://github.com/huanngzh/EpiDiff). |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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We use renders from the LVIS dataset, utilizing [huanngzh/render-toolbox](https://github.com/huanngzh/render-toolbox). |
