shap e EU AI Act Compliance Profile

Shap-E

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 by Heewoo Jun and Alex Nichol from OpenAI.

Original repository of Shap-E can be found here: https://github.com/openai/shap-e.

The authors of Shap-E didn't author this model card. They provide a separate model card here.

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:

pip install transformers accelerate -q
pip install git+https://github.com/hugg

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