Introduction

The abstract of the Shap-E paper:

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.

Released checkpoints

The authors released the following checkpoints:

• openai/shap-e: produces a 3D image from a text input prompt
• openai/shap-e-img2img: samples a 3D image from synthetic 2D image

Usage examples in 🧨 diffusers

First make sure you have installed all the dependencies:

bash
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pip install transformers accelerate -q
pip install git+https://github.com/huggingface/diffusers@@shap-ee

Once the dependencies are installed, use the code below:

python
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import torch
from diffusers import ShapEPipeline
from diffusers.utils import export_to_gif


ckpt_id = "openai/shap-e"
pipe = ShapEPipeline.from_pretrained(repo).to("cuda")


guidance_scale = 15.0
prompt = "a shark"
images = pipe(
    prompt,
    guidance_scale=guidance_scale,
    num_inference_steps=64,
    size=256,
).images

gif_path = export_to_gif(images, "shark_3d.gif")

Results

Training details

Refer to the original paper.

Known limitations and potential biases

Refer to the original model card.

Citation

bibtex
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@misc{jun2023shape,
      title={Shap-E: Generating Conditional 3D Implicit Functions}, 
      author={Heewoo Jun and Alex Nichol},
      year={2023},
      eprint={2305.02463},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}