Update README.md (#2)

- Update README.md (3bdc4d392ba7f55602763ef238a43e9e7b85c2c7)


Co-authored-by: Sayak Paul <sayakpaul@users.noreply.huggingface.co>

README.md

@@ -1,4 +1,115 @@
 ---
+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](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 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 
+
+<table>
+    <tbody>
+        <tr>
+            <td align="center">
+                <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png" alt="Reference corgi image in 2D">
+            </td>
+            <td align="center">
+                <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi_sampled_3d.gif" alt="Sampled image in 3D (one)">
+            </td align="center">
+            <td align="center">
+                <img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi_sampled_3d_two.gif" alt="Sampled image in 3D (two)">
+            </td>
+        </tr>
+        <tr>
+            <td align="center">Reference corgi image in 2D</td>
+            <td align="center">Sampled image in 3D (one)</td>
+            <td align="center">Sampled image in 3D (two)</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}
+}
+```