Add Shap-E

docs/source/en/_toctree.yml

@@ -224,6 +224,8 @@
       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:

docs/source/en/api/pipelines/shap_e.mdx

@@ -0,0 +1,38 @@
+<!--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 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* |
+
+## ShapEPipeline
+[[autodoc]] ShapEPipeline
+	- all
+	- __call__
+
+[[autodoc]] ShapEImg2ImgPipeline
+	- all
+	- __call__
+
+