|
--- |
|
license: mit |
|
--- |
|
|
|
# Model Card for EpiDiff |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
[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. |
|
|
|
 |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
|
|
|
|
- **Model type:** Generative image-to-multiview model |
|
- **License:** [More Information Needed] |
|
|
|
### Model Sources |
|
|
|
<!-- Provide the basic links for the model. --> |
|
|
|
- **Repository:** https://github.com/huanngzh/EpiDiff |
|
- **Paper:** https://arxiv.org/abs/2312.06725 |
|
- **Demo:** https://huanngzh.github.io/EpiDiff/ |
|
|
|
## Uses |
|
|
|
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
|
For usage instructions, please refer to [our EpiDiff GitHub repository](https://github.com/huanngzh/EpiDiff). |
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
<!-- 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. --> |
|
|
|
We use renders from the LVIS dataset, utilizing [huanngzh/render-toolbox](https://github.com/huanngzh/render-toolbox). |
