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--- |
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license: mit |
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tags: |
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- text-to-image |
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- text-to-3d |
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- shap-e |
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- diffusers |
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--- |
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# Shap-E |
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Shap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in [Shap-E: Generating Conditional 3D Implicit Functions](https://arxiv.org/abs/2305.02463) by Heewoo Jun and Alex Nichol from OpenAI. |
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Original repository of Shap-E can be found here: https://github.com/openai/shap-e. |
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_The authors of Shap-E didn't author this model card. They provide a separate model card [here](https://github.com/openai/shap-e/blob/main/model-card.md)._ |
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## Introduction |
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The abstract of the Shap-E paper: |
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*We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at [this https URL](https://github.com/openai/shap-e).* |
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## Released checkpoints |
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The authors released the following checkpoints: |
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* [openai/shap-e](https://hf.co/openai/shap-e): produces a 3D image from a text input prompt |
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* [openai/shap-e-img2img](https://hf.co/openai/shap-e-img2img): samples a 3D image from synthetic 2D image |
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## Usage examples in 🧨 diffusers |
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First make sure you have installed all the dependencies: |
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```bash |
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pip install transformers accelerate -q |
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pip install git+https://github.com/huggingface/diffusers@@shap-ee |
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``` |
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Once the dependencies are installed, use the code below: |
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```python |
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import torch |
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from diffusers import ShapEPipeline |
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from diffusers.utils import export_to_gif |
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ckpt_id = "openai/shap-e" |
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pipe = ShapEPipeline.from_pretrained(repo).to("cuda") |
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guidance_scale = 15.0 |
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prompt = "a shark" |
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images = pipe( |
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prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=64, |
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size=256, |
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).images |
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gif_path = export_to_gif(images, "shark_3d.gif") |
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``` |
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## Results |
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<table> |
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<tbody> |
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<tr> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/bird_3d.gif" alt="a bird"> |
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</td> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/shark_3d.gif" alt="a shark"> |
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</td align="center"> |
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<td align="center"> |
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<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/veg_3d.gif" alt="A bowl of vegetables"> |
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</td> |
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</tr> |
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<tr> |
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<td align="center">A bird</td> |
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<td align="center">A shark</td> |
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<td align="center">A bowl of vegetables</td> |
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</tr> |
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</tr> |
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</tbody> |
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<table> |
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## Training details |
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Refer to the [original paper](https://arxiv.org/abs/2305.02463). |
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## Known limitations and potential biases |
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Refer to the [original model card](https://github.com/openai/shap-e/blob/main/model-card.md). |
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## Citation |
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```bibtex |
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@misc{jun2023shape, |
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title={Shap-E: Generating Conditional 3D Implicit Functions}, |
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author={Heewoo Jun and Alex Nichol}, |
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year={2023}, |
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eprint={2305.02463}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
