VQ-diffusion (#658)

* Changes for VQ-diffusion VQVAE

Add specify dimension of embeddings to VQModel:
`VQModel` will by default set the dimension of embeddings to the number
of latent channels. The VQ-diffusion VQVAE has a smaller
embedding dimension, 128, than number of latent channels, 256.

Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down
unet block helpers. VQ-diffusion's VQVAE uses those two block types.

* Changes for VQ-diffusion transformer

Modify attention.py so SpatialTransformer can be used for
VQ-diffusion's transformer.

SpatialTransformer:
- Can now operate over discrete inputs (classes of vector embeddings) as well as continuous.
- `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs
- modified forward pass to take optional timestep embeddings

ImagePositionalEmbeddings:
- added to provide positional embeddings to discrete inputs for latent pixels

BasicTransformerBlock:
- norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings
- modified forward pass to take optional timestep embeddings

CrossAttention:
- now may optionally take a bias parameter for its query, key, and value linear layers

FeedForward:
- Internal layers are now configurable

ApproximateGELU:
- Activation function in VQ-diffusion's feedforward layer

AdaLayerNorm:
- Norm layer modified to incorporate timestep embeddings

* Add VQ-diffusion scheduler

* Add VQ-diffusion pipeline

* Add VQ-diffusion convert script to diffusers

* Add VQ-diffusion dummy objects

* Add VQ-diffusion markdown docs

* Add VQ-diffusion tests

* some renaming

* some fixes

* more renaming

* correct

* fix typo

* correct weights

* finalize

* fix tests

* Apply suggestions from code review

Co-authored-by: Anton Lozhkov <[email protected]>

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <[email protected]>

* finish

* finish

* up

Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: Anton Lozhkov <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>

Author: 4 people

docs/source/_toctree.yml

@@ -96,6 +96,8 @@
       title: "Stochastic Karras VE"
     - local: api/pipelines/dance_diffusion
       title: "Dance Diffusion"
+    - local: api/pipelines/vq_diffusion
+      title: "VQ Diffusion"
     - local: api/pipelines/repaint
       title: "RePaint"
     title: "Pipelines"

docs/source/api/models.mdx

@@ -49,6 +49,12 @@ The models are built on the base class ['ModelMixin'] that is a `torch.nn.module
 ## AutoencoderKL
 [[autodoc]] AutoencoderKL
 
+## Transformer2DModel
+[[autodoc]] Transformer2DModel
+
+## Transformer2DModelOutput
+[[autodoc]] models.attention.Transformer2DModelOutput
+
 ## FlaxModelMixin
 [[autodoc]] FlaxModelMixin
 

docs/source/api/pipelines/overview.mdx

@@ -41,22 +41,22 @@ If you are looking for *official* training examples, please have a look at [exam
 The following table summarizes all officially supported pipelines, their corresponding paper, and if 
 available a colab notebook to directly try them out.
 
-| Pipeline                                             | Paper                                                                                                                        |                 Tasks                 | Colab
-|------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------:|:---:|
-| [ddpm](./ddpm)                                       | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239)                                             |    Unconditional Image Generation     |
-| [ddim](./ddim)                                       | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502)                                                  |    Unconditional Image Generation     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [latent_diffusion](./latent_diffusion)               | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)                         |       Text-to-Image Generation        | 
-| [latent_diffusion_uncond](./latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)                         |    Unconditional Image Generation     | 
-| [pndm](./pndm)                                       | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778)                           |    Unconditional Image Generation     | 
-| [score_sde_ve](./score_sde_ve)                       | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) |    Unconditional Image Generation     | 
-| [score_sde_vp](./score_sde_vp)                       | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) |    Unconditional Image Generation     | 
-| [stable_diffusion](./stable_diffusion)               | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release)                                            |       Text-to-Image Generation        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [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     | 
+| Pipeline | Paper | Tasks | Colab
+|---|---|:---:|:---:|
+| [ddpm](./ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
+| [ddim](./ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [latent_diffusion](./latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
+| [latent_diffusion_uncond](./latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
+| [pndm](./pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
+| [score_sde_ve](./score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [score_sde_vp](./score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
+| [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [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 |
 | [repaint](./repaint)                                 | [**RePaint: Inpainting using Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2201.09865)                   |           Image Inpainting            | 
 
-
 **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 
 
 However, most of them can be adapted to use different scheduler components or even different model components. Some pipeline examples are shown in the [Examples](#examples) below.

docs/source/api/pipelines/vq_diffusion.mdx

@@ -0,0 +1,34 @@
+<!--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

@@ -113,7 +113,6 @@ Score SDE-VP is under construction.
 
 [[autodoc]] schedulers.scheduling_sde_vp.ScoreSdeVpScheduler
 
-
 #### Euler scheduler
 
 Euler scheduler (Algorithm 2) from the paper [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) by Karras et al. (2022). Based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by Katherine Crowson.
@@ -130,11 +129,17 @@ Fast scheduler which often times generates good outputs with 20-30 steps.
 [[autodoc]] EulerAncestralDiscreteScheduler
 
 
+#### VQDiffusionScheduler
+
+Original paper can be found [here](https://arxiv.org/abs/2111.14822)
+
+[[autodoc]] VQDiffusionScheduler
+
 #### RePaint scheduler
 
 DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. 
 Intended for use with [`RePaintPipeline`].
 Based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2201.09865) 
 and the original implementation by Andreas Lugmayr et al.: https://github.com/andreas128/RePaint
 
-[[autodoc]] RePaintScheduler
+[[autodoc]] RePaintScheduler

docs/source/index.mdx

@@ -34,6 +34,7 @@ available a colab notebook to directly try them out.
 
 | Pipeline | Paper | Tasks | Colab
 |---|---|:---:|:---:|
+| [dance_diffusion](./api/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
 | [ddpm](./api/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
 | [ddim](./api/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
 | [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
@@ -45,5 +46,6 @@ available a colab notebook to directly try them out.
 | [stable_diffusion](./api/pipelines/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](./api/pipelines/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](./api/pipelines/stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
+| [vq_diffusion](./api/pipelines/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.