ActivateLLC
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       title: Self-Attention Guidance
     - local: api/pipelines/semantic_stable_diffusion
       title: Semantic Guidance
+    - local: api/pipelines/shap_e
+      title: Shap-E
     - local: api/pipelines/spectrogram_diffusion
       title: Spectrogram Diffusion
     - sections:
 
@@ -0,0 +1,139 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Shap-E
+
+## Overview
+
+
+The Shap-E model was proposed in [Shap-E: Generating Conditional 3D Implicit Functions](https://arxiv.org/abs/2305.02463) by Alex Nichol and Heewon Jun from [OpenAI](https://github.com/openai). 
+
+The abstract of the paper is the following:
+
+*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.*
+
+The original codebase can be found [here](https://github.com/openai/shap-e).
+
+## Available Pipelines:
+
+| Pipeline | Tasks |
+|---|---|
+| [pipeline_shap_e.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/shap_e/pipeline_shap_e.py) | *Text-to-Image Generation* | 
+| [pipeline_shap_e_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py) | *Image-to-Image Generation* |
+
+## Available checkpoints 
+
+* [`openai/shap-e`](https://huggingface.co/openai/shap-e)
+* [`openai/shap-e-img2img`](https://huggingface.co/openai/shap-e-img2img)
+
+## Usage Examples
+
+In the following, we will walk you through some examples of how to use Shap-E pipelines to create 3D objects in gif format.
+
+### Text-to-3D image generation 
+
+We can use [`ShapEPipeline`] to create 3D object based on a text prompt. In this example, we will make a birthday cupcake for :firecracker: diffusers library's 1 year birthday. The workflow to use the Shap-E text-to-image pipeline is same as how you would use other text-to-image pipelines in diffusers.
+
+```python
+import torch
+
+from diffusers import DiffusionPipeline
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+repo = "openai/shap-e"
+pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
+pipe = pipe.to(device)
+
+guidance_scale = 15.0
+prompt = ["A firecracker", "A birthday cupcake"]
+
+images = pipe(
+    prompt,
+    guidance_scale=guidance_scale,
+    num_inference_steps=64,
+    frame_size=256,
+).images
+```
+
+The output of [`ShapEPipeline`] is a list of lists of images frames. Each list of frames can be used to create a 3D object. Let's use the `export_to_gif` utility function in diffusers to make a 3D cupcake!
+
+```python
+from diffusers.utils import export_to_gif
+
+export_to_gif(images[0], "firecracker_3d.gif")
+export_to_gif(images[1], "cake_3d.gif")
+```
+![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/firecracker_out.gif)
+![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/cake_out.gif)
+
+
+### Image-to-Image generation
+
+You can use [`ShapEImg2ImgPipeline`] along with other text-to-image pipelines in diffusers and turn your 2D generation into 3D. 
+
+In this example, We will first genrate a cheeseburger with a simple prompt "A cheeseburger, white background" 
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe_prior = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16)
+pipe_prior.to("cuda")
+
+t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
+t2i_pipe.to("cuda")
+
+prompt = "A cheeseburger, white background"
+
+image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
+image = t2i_pipe(
+    prompt,
+    image_embeds=image_embeds,
+    negative_image_embeds=negative_image_embeds,
+).images[0]
+
+image.save("burger.png")
+```
+
+![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/burger_in.png)
+
+we will then use the Shap-E image-to-image pipeline to turn it into a 3D cheeseburger :)
+
+```python
+from PIL import Image
+from diffusers.utils import export_to_gif
+
+repo = "openai/shap-e-img2img"
+pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+guidance_scale = 3.0
+image = Image.open("burger.png").resize((256, 256))
+
+images = pipe(
+    image,
+    guidance_scale=guidance_scale,
+    num_inference_steps=64,
+    frame_size=256,
+).images
+
+gif_path = export_to_gif(images[0], "burger_3d.gif")
+```
+![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/burger_out.gif)
+
+## ShapEPipeline
+[[autodoc]] ShapEPipeline
+	- all
+	- __call__
+
+## ShapEImg2ImgPipeline
+[[autodoc]] ShapEImg2ImgPipeline
+	- all
+	- __call__