license: mit
tags:
- image-to-image
- text-to-3d
- diffusers
- shap-e
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/huggingface/diffusers@@shap-ee
Once the dependencies are installed, use the code below:
import torch
from diffusers import ShapEImg2ImgPipeline
from diffusers.utils import export_to_gif, load_image
ckpt_id = "openai/shap-e-img2img"
pipe = ShapEImg2ImgPipeline.from_pretrained(repo).to("cuda")
img_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
image = load_image(img_url)
generator = torch.Generator(device="cuda").manual_seed(0)
batch_size = 4
guidance_scale = 3.0
images = pipe(
image,
num_images_per_prompt=batch_size,
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=64,
size=256,
output_type="pil"
).images
gif_path = export_to_gif(images, "corgi_sampled_3d.gif")
Results
|
|
|
| Reference corgi image in 2D | Sampled image in 3D (one) | Sampled image in 3D (two) |
Training details
Refer to the original paper.
Known limitations and potential biases
Refer to the original model card.
Citation
@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}
}