VQ-diffusion

docs/source/_toctree.yml

@@ -96,5 +96,7 @@
  title: "Stochastic Karras VE"
  - local: api/pipelines/dance_diffusion
  title: "Dance Diffusion"
+ - local: api/pipelines/vq_diffusion
+ title: "VQ Diffusion"
  title: "Pipelines"
  title: "API"

docs/source/api/pipelines/overview.mdx

@@ -54,6 +54,7 @@ available a colab notebook to directly try them out.
 | [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
 | [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
 | [stochastic_karras_ve](./stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
+| [vq_diffusion](./vq_diffusion) | [**Vector Quantized Diffusion Model for Text-to-Image Synthesis**](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
 
 **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 
 

docs/source/api/pipelines/vq_diffusion.mdx

@@ -0,0 +1,35 @@
+<!--Copyright 2022 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.
+-->
+
+# VQDiffusion
+
+## Overview
+
+[Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) by Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo
+
+The abstract of the paper is the following:
+
+We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.
+
+The original codebase can be found [here](https://github.com/microsoft/VQ-Diffusion).
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_vq_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py) | *Text-to-Image Generation* | - |
+
+
+## VQDiffusionPipeline
+[[autodoc]] pipelines.vq_diffusion.pipeline_vq_diffusion.VQDiffusionPipeline
+ - __call__
+

docs/source/api/schedulers.mdx

@@ -112,3 +112,9 @@ Score SDE-VP is under construction.
 </Tip>
 
 [[autodoc]] schedulers.scheduling_sde_vp.ScoreSdeVpScheduler
+
+#### VQDiffusionScheduler
+
+Original paper can be found [here](https://arxiv.org/abs/2111.14822)
+
+[[autodoc]] VQDiffusionScheduler