thomwolf / taming-transformers

Taming Transformers for High-Resolution Image Synthesis, CVPR 2021 (Oral)

Taming Transformers for High-Resolution Image Synthesis
Patrick Esser*, Robin Rombach*, Björn Ommer
* equal contribution

tl;dr We combine the efficiancy of convolutional approaches with the expressivity of transformers by introducing a convolutional VQGAN, which learns a codebook of context-rich visual parts, whose composition is modeled with an autoregressive transformer.

arXiv | BibTeX | Project Page

News

• We added a colab notebook which compares two VQGANs and OpenAI's DALL-E. See also this section.
• We now include an overview of pretrained models in Tab.1. We added models for COCO and ADE20k.
• The streamlit demo now supports image completions.
• We now include a couple of examples from the D-RIN dataset so you can run the D-RIN demo without preparing the dataset first.
• You can now jump right into sampling with our Colab quickstart notebook.

Requirements

A suitable conda environment named taming can be created and activated with:

conda env create -f environment.yaml
conda activate taming

Overview of pretrained models

The following table provides an overview of all models that are currently available. FID scores were evaluated using torch-fidelity and without rejection sampling. For reference, we also include a link to the recently released autoencoder of the DALL-E model. See the corresponding colab notebook for a comparison and discussion of reconstruction capabilities.

Dataset	FID	Link	Samples (256x256)	Comments
FFHQ (f=16)	11.4	coming soon...
CelebA-HQ (f=16)	10.7	coming soon...
ADE20K (f=16)	35.5	ade20k_transformer	ade20k_samples.zip [2k]	evaluated on val split (2k images)
COCO-Stuff (f=16)	20.4	coco_transformer	coco_samples.zip [5k]	evaluated on val split (5k images)
ImageNet (cIN) (f=16)		coming soon...
FacesHQ (f=16)	--	faceshq_transformer
S-FLCKR (f=16)	--	sflckr
D-RIN (f=16)	--	drin_transformer
VQGAN ImageNet (f=16), 1024	8.0	vqgan_imagenet_f16_1024	reconstructions	Reconstruction-FIDs evaluated against the validation split of ImageNet on 256x256 images.
VQGAN ImageNet (f=16), 16384	4.9	vqgan_imagenet_f16_16384	reconstructions	Reconstruction-FIDs evaluated against the validation split of ImageNet on 256x256 images.
DALL-E VQVAE (f=8), 8192, GumbelQuantization	34.3	https://github.com/openai/DALL-E	reconstructions	Reconstruction-FIDs evaluated against the validation split of ImageNet on 256x256 images.

Running pretrained models

The commands below will start a streamlit demo which supports sampling at different resolutions and image completions. To run a non-interactive version of the sampling process, replace streamlit run scripts/sample_conditional.py -- by python scripts/make_samples.py --outdir <path_to_write_samples_to> and keep the remaining command line arguments.

S-FLCKR

You can also run this model in a Colab notebook, which includes all necessary steps to start sampling.

Download the 2020-11-09T13-31-51_sflckr folder and place it into logs. Then, run

streamlit run scripts/sample_conditional.py -- -r logs/2020-11-09T13-31-51_sflckr/

FacesHQ

Download 2020-11-13T21-41-45_faceshq_transformer and place it into logs. Follow the data preparation steps for CelebA-HQ and FFHQ. Run

streamlit run scripts/sample_conditional.py -- -r logs/2020-11-13T21-41-45_faceshq_transformer/

D-RIN

Download 2020-11-20T12-54-32_drin_transformer and place it into logs. To run the demo on a couple of example depth maps included in the repository, run

streamlit run scripts/sample_conditional.py -- -r logs/2020-11-20T12-54-32_drin_transformer/ --ignore_base_data data="{target: main.DataModuleFromConfig, params: {batch_size: 1, validation: {target: taming.data.imagenet.DRINExamples}}}"

To run the demo on the complete validation set, first follow the data preparation steps for ImageNet and then run

streamlit run scripts/sample_conditional.py -- -r logs/2020-11-20T12-54-32_drin_transformer/

COCO

Download 2021-01-20T16-04-20_coco_transformer and place it into logs. To run the demo on a couple of example segmentation maps included in the repository, run

streamlit run scripts/sample_conditional.py -- -r logs/2021-01-20T16-04-20_coco_transformer/ --ignore_base_data data="{target: main.DataModuleFromConfig, params: {batch_size: 1, validation: {target: taming.data.coco.Examples}}}"

ADE20k

Download 2020-11-20T21-45-44_ade20k_transformer and place it into logs. To run the demo on a couple of example segmentation maps included in the repository, run

streamlit run scripts/sample_conditional.py -- -r logs/2020-11-20T21-45-44_ade20k_transformer/ --ignore_base_data data="{target: main.DataModuleFromConfig, params: {batch_size: 1, validation: {target: taming.data.ade20k.Examples}}}"

Data Preparation

ImageNet

The code will try to download (through Academic Torrents) and prepare ImageNet the first time it is used. However, since ImageNet is quite large, this requires a lot of disk space and time. If you already have ImageNet on your disk, you can speed things up by putting the data into ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ (which defaults to ~/.cache/autoencoders/data/ILSVRC2012_{split}/data/), where {split} is one of train/validation. It should have the following structure:

${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/
├── n01440764
│   ├── n01440764_10026.JPEG
│   ├── n01440764_10027.JPEG
│   ├── ...
├── n01443537
│   ├── n01443537_10007.JPEG
│   ├── n01443537_10014.JPEG
│   ├── ...
├── ...

If you haven't extracted the data, you can also place ILSVRC2012_img_train.tar/ILSVRC2012_img_val.tar (or symlinks to them) into ${XDG_CACHE}/autoencoders/data/ILSVRC2012_train/ / ${XDG_CACHE}/autoencoders/data/ILSVRC2012_validation/, which will then be extracted into above structure without downloading it again. Note that this will only happen if neither a folder ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ nor a file ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/.ready exist. Remove them if you want to force running the dataset preparation again.

You will then need to prepare the depth data using MiDaS. Create a symlink data/imagenet_depth pointing to a folder with two subfolders train and val, each mirroring the structure of the corresponding ImageNet folder described above and containing a png file for each of ImageNet's JPEG files. The png encodes float32 depth values obtained from MiDaS as RGBA images. We provide the script scripts/extract_depth.py to generate this data. Please note that this script uses MiDaS via PyTorch Hub. When we prepared the data, the hub provided the MiDaS v2.0 version, but now it provides a v2.1 version. We haven't tested our models with depth maps obtained via v2.1 and if you want to make sure that things work as expected, you must adjust the script to make sure it explicitly uses v2.0!

CelebA-HQ

Create a symlink data/celebahq pointing to a folder containing the .npy files of CelebA-HQ (instructions to obtain them can be found in the PGGAN repository).

FFHQ

Create a symlink data/ffhq pointing to the images1024x1024 folder obtained from the FFHQ repository.

S-FLCKR

Unfortunately, we are not allowed to distribute the images we collected for the S-FLCKR dataset and can therefore only give a description how it was produced. There are many resources on collecting images from the web to get started. We collected sufficiently large images from flickr (see data/flickr_tags.txt for a full list of tags used to find images) and various subreddits (see data/subreddits.txt for all subreddits that were used). Overall, we collected 107625 images, and split them randomly into 96861 training images and 10764 validation images. We then obtained segmentation masks for each image using DeepLab v2 trained on COCO-Stuff. We used a PyTorch reimplementation and include an example script for this process in scripts/extract_segmentation.py.

COCO

Create a symlink data/coco containing the images from the 2017 split in train2017 and val2017, and their annotations in annotations. Files can be obtained from the COCO webpage. In addition, we use the Stuff+thing PNG-style annotations on COCO 2017 trainval annotations from COCO-Stuff, which should be placed under data/cocostuffthings.

ADE20k

Create a symlink data/ade20k_root containing the contents of ADEChallengeData2016.zip from the MIT Scene Parsing Benchmark.

Training models

FacesHQ

Train a VQGAN with

python main.py --base configs/faceshq_vqgan.yaml -t True --gpus 0,

Then, adjust the checkpoint path of the config key model.params.first_stage_config.params.ckpt_path in configs/faceshq_transformer.yaml (or download 2020-11-09T13-33-36_faceshq_vqgan and place into logs, which corresponds to the preconfigured checkpoint path), then run

python main.py --base configs/faceshq_transformer.yaml -t True --gpus 0,

D-RIN

Train a VQGAN on ImageNet with

python main.py --base configs/imagenet_vqgan.yaml -t True --gpus 0,

or download a pretrained one from 2020-09-23T17-56-33_imagenet_vqgan and place under logs. If you trained your own, adjust the path in the config key model.params.first_stage_config.params.ckpt_path of configs/drin_transformer.yaml.

Train a VQGAN on Depth Maps of ImageNet with

python main.py --base configs/imagenetdepth_vqgan.yaml -t True --gpus 0,

or download a pretrained one from 2020-11-03T15-34-24_imagenetdepth_vqgan and place under logs. If you trained your own, adjust the path in the config key model.params.cond_stage_config.params.ckpt_path of configs/drin_transformer.yaml.

To train the transformer, run

python main.py --base configs/drin_transformer.yaml -t True --gpus 0,

More Resources

Comparing Different First Stage Models

The reconstruction and compression capabilities of different fist stage models can be analyzed in this colab notebook. In particular, the notebook compares two VQGANs (with a downsampling factor of f=16 for each and codebook dimensionality of 1024 and 16384) and the discrete autoencoder of OpenAI's DALL-E (which has f=8).

Other

• A video summary by Two Minute Papers.
• A video summary by Gradient Dude.
• A weights and biases report summarizing the paper by ayulockin.
• A video summary by What's AI.
• Take a look at ak9250's notebook if you want to run the streamlit demos on Colab.

Shout-outs

Thanks to everyone who makes their code and models available. In particular,

• The architecture of our VQGAN is inspired by Denoising Diffusion Probabilistic Models
• The very hackable transformer implementation minGPT
• The good ol' PatchGAN and Learned Perceptual Similarity (LPIPS)

BibTeX

@misc{esser2020taming,
      title={Taming Transformers for High-Resolution Image Synthesis}, 
      author={Patrick Esser and Robin Rombach and Björn Ommer},
      year={2020},
      eprint={2012.09841},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}