Update README.md

README.md

@@ -1,6 +1,45 @@
+---
+license: mit
+tags:
+- text-to-image
+- shap-e
+- diffusers
+pipeline_tag: text-to-3d
+---
+
+# 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](https://arxiv.org/abs/2305.02463) 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](https://github.com/openai/shap-e/blob/main/model-card.md)._
+
+## 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](https://github.com/openai/shap-e).*
+
+## Released checkpoints
+
+The authors released the following checkpoints:
+
+* [openai/shap-e](https://hf.co/openai/shap-e): produces a 3D image from a text input prompt
+* [openai/shap-e-img2img](https://hf.co/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 
 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 
 import torch
 from diffusers import ShapEPipeline
 from diffusers.utils import export_to_gif
@@ -20,3 +59,49 @@ images = pipe(
 ).images
 
 gif_path = export_to_gif(images, "shark_3d.gif")
+```
+
+## Results 
+
+<table>
+    <tbody>
+        <tr>
+            <td align="center">
+                <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/bird_3d.gif" alt="a bird">
+            </td>
+            <td align="center">
+                <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/shark_3d.gif" alt="a shark">
+            </td align="center">
+            <td align="center">
+                <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/veg_3d.gif" alt="A bowl of vegetables">
+            </td>
+        </tr>
+        <tr>
+            <td align="center">A bird</td>
+            <td align="center">A shark</td>
+            <td align="center">A bowl of vegetables</td>
+        </tr>
+     </tr> 
+    </tbody>
+<table>
+
+## Training details
+
+Refer to the [original paper](https://arxiv.org/abs/2305.02463).
+
+## Known limitations and potential biases
+    
+Refer to the [original model card](https://github.com/openai/shap-e/blob/main/model-card.md).
+    
+## Citation
+
+```bibtex 
+@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}
+}
+```