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+taming-transformers/data/sflckr_images/alaska_lakes/43259216952_59352d7204_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/australia/12822389285_a7723081b5_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/australia/8720651218_ca82a6608e_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/black_forest/8364557382_c6c9ee2fd6_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/canada/256743165_9f130ba95b_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/canada/2883773_881c197107_c.jpg filter=lfs diff=lfs merge=lfs -text
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+taming-transformers/data/sflckr_images/cliff_ocean/36142796444_45d452f567_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/desert/4534149722_3cc4f92891_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/geysir/14996762478_a9bdbf959a_b.jpg filter=lfs diff=lfs merge=lfs -text
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+taming-transformers/data/sflckr_images/geysir/4748115806_7219c2b3be_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/ireland/15570753471_74db396d14_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/lakes/39933489595_f0e5d85b6d_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/meadow/18864473291_844325caab_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/mongolia/6076373946_e9ea2aee32_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/newzealand_np/7942812194_9348729b93_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/norway/20099378793_cc2df820af_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/norway/25735082181_999927fe5a_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/swiss_mountains/33509672006_bf4c416afd_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/data/sflckr_images/volcano/50254383883_27ed6ea93a_b.jpg filter=lfs diff=lfs merge=lfs -text
+taming-transformers/scripts/reconstruction_usage.ipynb filter=lfs diff=lfs merge=lfs -text

taming-transformers/License.txt

@@ -0,0 +1,19 @@
+Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+OR OTHER DEALINGS IN THE SOFTWARE./

taming-transformers/README.md

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+# Taming Transformers for High-Resolution Image Synthesis
+##### CVPR 2021 (Oral)
+![teaser](assets/mountain.jpeg)
+
+[**Taming Transformers for High-Resolution Image Synthesis**](https://compvis.github.io/taming-transformers/)<br/>
+[Patrick Esser](https://github.com/pesser)\*,
+[Robin Rombach](https://github.com/rromb)\*,
+[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
+\* 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.
+
+![teaser](assets/teaser.png)
+[arXiv](https://arxiv.org/abs/2012.09841) | [BibTeX](#bibtex) | [Project Page](https://compvis.github.io/taming-transformers/)
+
+
+### News
+#### 2022
+- More pretrained VQGANs (e.g. a f8-model with only 256 codebook entries) are available in our new work on [Latent Diffusion Models](https://github.com/CompVis/latent-diffusion).
+- Added scene synthesis models as proposed in the paper [High-Resolution Complex Scene Synthesis with Transformers](https://arxiv.org/abs/2105.06458), see [this section](#scene-image-synthesis).
+#### 2021
+- Thanks to [rom1504](https://github.com/rom1504) it is now easy to [train a VQGAN on your own datasets](#training-on-custom-data).
+- Included a bugfix for the quantizer. For backward compatibility it is
+  disabled by default (which corresponds to always training with `beta=1.0`).
+  Use `legacy=False` in the quantizer config to enable it.
+  Thanks [richcmwang](https://github.com/richcmwang) and [wcshin-git](https://github.com/wcshin-git)!
+- Our paper received an update: See https://arxiv.org/abs/2012.09841v3 and the corresponding changelog.
+- Added a pretrained, [1.4B transformer model](https://k00.fr/s511rwcv) trained for class-conditional ImageNet synthesis, which obtains state-of-the-art FID scores among autoregressive approaches and outperforms BigGAN.
+- Added pretrained, unconditional models on [FFHQ](https://k00.fr/yndvfu95) and [CelebA-HQ](https://k00.fr/2xkmielf).
+- Added accelerated sampling via caching of keys/values in the self-attention operation, used in `scripts/sample_fast.py`.
+- Added a checkpoint of a [VQGAN](https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/) trained with f8 compression and Gumbel-Quantization. 
+  See also our updated [reconstruction notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb). 
+- We added a [colab notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb) which compares two VQGANs and OpenAI's [DALL-E](https://github.com/openai/DALL-E). See also [this section](#more-resources).
+- We now include an overview of pretrained models in [Tab.1](#overview-of-pretrained-models). We added models for [COCO](#coco) and [ADE20k](#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](#d-rin) without preparing the dataset first.
+- You can now jump right into sampling with our [Colab quickstart notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/taming-transformers.ipynb).
+
+## Requirements
+A suitable [conda](https://conda.io/) 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](https://github.com/toshas/torch-fidelity).
+For reference, we also include a link to the recently released autoencoder of the [DALL-E](https://github.com/openai/DALL-E) model. 
+See the corresponding [colab
+notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb)
+for a comparison and discussion of reconstruction capabilities.
+
+| Dataset  | FID vs train | FID vs val | Link |  Samples (256x256) | Comments
+| ------------- | ------------- | ------------- |-------------  | -------------  |-------------  |
+| FFHQ (f=16) | 9.6 | -- | [ffhq_transformer](https://k00.fr/yndvfu95) |  [ffhq_samples](https://k00.fr/j626x093) |
+| CelebA-HQ (f=16) | 10.2 | -- | [celebahq_transformer](https://k00.fr/2xkmielf) | [celebahq_samples](https://k00.fr/j626x093) |
+| ADE20K (f=16) | -- | 35.5 | [ade20k_transformer](https://k00.fr/ot46cksa) | [ade20k_samples.zip](https://heibox.uni-heidelberg.de/f/70bb78cbaf844501b8fb/) [2k] | evaluated on val split (2k images)
+| COCO-Stuff (f=16) | -- | 20.4  | [coco_transformer](https://k00.fr/2zz6i2ce) | [coco_samples.zip](https://heibox.uni-heidelberg.de/f/a395a9be612f4a7a8054/) [5k] | evaluated on val split (5k images)
+| ImageNet (cIN) (f=16) | 15.98/15.78/6.59/5.88/5.20 | -- | [cin_transformer](https://k00.fr/s511rwcv) | [cin_samples](https://k00.fr/j626x093) | different decoding hyperparameters |  
+| |  | | || |
+| FacesHQ (f=16) | -- |  -- | [faceshq_transformer](https://k00.fr/qqfl2do8)
+| S-FLCKR (f=16) | -- | -- | [sflckr](https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/) 
+| D-RIN (f=16) | -- | -- | [drin_transformer](https://k00.fr/39jcugc5)
+| | |  | | || |
+| VQGAN ImageNet (f=16), 1024 |  10.54 | 7.94 | [vqgan_imagenet_f16_1024](https://heibox.uni-heidelberg.de/d/8088892a516d4e3baf92/) | [reconstructions](https://k00.fr/j626x093) | Reconstruction-FIDs.
+| VQGAN ImageNet (f=16), 16384 | 7.41 | 4.98 |[vqgan_imagenet_f16_16384](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/)  |  [reconstructions](https://k00.fr/j626x093) | Reconstruction-FIDs.
+| VQGAN OpenImages (f=8), 256 | -- | 1.49 |https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip |  ---  | Reconstruction-FIDs. Available via [latent diffusion](https://github.com/CompVis/latent-diffusion).
+| VQGAN OpenImages (f=8), 16384 | -- | 1.14 |https://ommer-lab.com/files/latent-diffusion/vq-f8.zip  |  ---  | Reconstruction-FIDs. Available via [latent diffusion](https://github.com/CompVis/latent-diffusion)
+| VQGAN OpenImages (f=8), 8192, GumbelQuantization | 3.24 | 1.49 |[vqgan_gumbel_f8](https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/)  |  ---  | Reconstruction-FIDs.
+| | |  | | || |
+| DALL-E dVAE (f=8), 8192, GumbelQuantization | 33.88 | 32.01 | https://github.com/openai/DALL-E | [reconstructions](https://k00.fr/j626x093) | Reconstruction-FIDs.
+
+
+## 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. 
+
+To sample from unconditional or class-conditional models, 
+run `python scripts/sample_fast.py -r <path/to/config_and_checkpoint>`.
+We describe below how to use this script to sample from the ImageNet, FFHQ, and CelebA-HQ models, 
+respectively.
+
+### S-FLCKR
+![teaser](assets/sunset_and_ocean.jpg)
+
+You can also [run this model in a Colab
+notebook](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/taming-transformers.ipynb),
+which includes all necessary steps to start sampling.
+
+Download the
+[2020-11-09T13-31-51_sflckr](https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/)
+folder and place it into `logs`. Then, run
+```
+streamlit run scripts/sample_conditional.py -- -r logs/2020-11-09T13-31-51_sflckr/
+```
+
+### ImageNet
+![teaser](assets/imagenet.png)
+
+Download the [2021-04-03T19-39-50_cin_transformer](https://k00.fr/s511rwcv)
+folder and place it into logs.  Sampling from the class-conditional ImageNet
+model does not require any data preparation. To produce 50 samples for each of
+the 1000 classes of ImageNet, with k=600 for top-k sampling, p=0.92 for nucleus
+sampling and temperature t=1.0, run
+
+```
+python scripts/sample_fast.py -r logs/2021-04-03T19-39-50_cin_transformer/ -n 50 -k 600 -t 1.0 -p 0.92 --batch_size 25   
+```
+
+To restrict the model to certain classes, provide them via the `--classes` argument, separated by 
+commas. For example, to sample 50 *ostriches*, *border collies* and *whiskey jugs*, run
+
+```
+python scripts/sample_fast.py -r logs/2021-04-03T19-39-50_cin_transformer/ -n 50 -k 600 -t 1.0 -p 0.92 --batch_size 25 --classes 9,232,901   
+```
+We recommended to experiment with the autoregressive decoding parameters (top-k, top-p and temperature) for best results.  
+
+### FFHQ/CelebA-HQ
+
+Download the [2021-04-23T18-19-01_ffhq_transformer](https://k00.fr/yndvfu95) and 
+[2021-04-23T18-11-19_celebahq_transformer](https://k00.fr/2xkmielf) 
+folders and place them into logs. 
+Again, sampling from these unconditional models does not require any data preparation.
+To produce 50000 samples, with k=250 for top-k sampling,
+p=1.0 for nucleus sampling and temperature t=1.0, run
+
+```
+python scripts/sample_fast.py -r logs/2021-04-23T18-19-01_ffhq_transformer/   
+```
+for FFHQ and  
+
+```
+python scripts/sample_fast.py -r logs/2021-04-23T18-11-19_celebahq_transformer/   
+```
+to sample from the CelebA-HQ model.
+For both models it can be advantageous to vary the top-k/top-p parameters for sampling.
+
+### FacesHQ
+![teaser](assets/faceshq.jpg)
+
+Download [2020-11-13T21-41-45_faceshq_transformer](https://k00.fr/qqfl2do8) and
+place it into `logs`. Follow the data preparation steps for
+[CelebA-HQ](#celeba-hq) and [FFHQ](#ffhq). Run
+```
+streamlit run scripts/sample_conditional.py -- -r logs/2020-11-13T21-41-45_faceshq_transformer/
+```
+
+### D-RIN
+![teaser](assets/drin.jpg)
+
+Download [2020-11-20T12-54-32_drin_transformer](https://k00.fr/39jcugc5) 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](#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](https://k00.fr/2zz6i2ce) 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](https://k00.fr/ot46cksa) 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}}}"
+```
+
+## Scene Image Synthesis
+![teaser](assets/scene_images_samples.svg)
+Scene image generation based on bounding box conditionals as done in our CVPR2021 AI4CC workshop paper [High-Resolution Complex Scene Synthesis with Transformers](https://arxiv.org/abs/2105.06458) (see talk on [workshop page](https://visual.cs.brown.edu/workshops/aicc2021/#awards)). Supporting the datasets COCO and Open Images.
+
+### Training
+Download first-stage models [COCO-8k-VQGAN](https://heibox.uni-heidelberg.de/f/78dea9589974474c97c1/) for COCO or [COCO/Open-Images-8k-VQGAN](https://heibox.uni-heidelberg.de/f/461d9a9f4fcf48ab84f4/) for Open Images.
+Change `ckpt_path` in `data/coco_scene_images_transformer.yaml` and `data/open_images_scene_images_transformer.yaml` to point to the downloaded first-stage models.
+Download the full COCO/OI datasets and adapt `data_path` in the same files, unless working with the 100 files provided for training and validation suits your needs already.
+
+Code can be run with
+`python main.py --base configs/coco_scene_images_transformer.yaml -t True --gpus 0,`
+or
+`python main.py --base configs/open_images_scene_images_transformer.yaml -t True --gpus 0,`
+
+### Sampling 
+Train a model as described above or download a pre-trained model:
+ - [Open Images 1 billion parameter model](https://drive.google.com/file/d/1FEK-Z7hyWJBvFWQF50pzSK9y1W_CJEig/view?usp=sharing) available that trained 100 epochs. On 256x256 pixels, FID 41.48±0.21, SceneFID 14.60±0.15, Inception Score 18.47±0.27. The model was trained with 2d crops of images and is thus well-prepared for the task of generating high-resolution images, e.g. 512x512.
+ - [Open Images distilled version of the above model with 125 million parameters](https://drive.google.com/file/d/1xf89g0mc78J3d8Bx5YhbK4tNRNlOoYaO) allows for sampling on smaller GPUs (4 GB is enough for sampling 256x256 px images). Model was trained for 60 epochs with 10% soft loss, 90% hard loss. On 256x256 pixels, FID 43.07±0.40, SceneFID 15.93±0.19, Inception Score 17.23±0.11.
+ - [COCO 30 epochs](https://heibox.uni-heidelberg.de/f/0d0b2594e9074c7e9a33/)
+ - [COCO 60 epochs](https://drive.google.com/file/d/1bInd49g2YulTJBjU32Awyt5qnzxxG5U9/) (find model statistics for both COCO versions in `assets/coco_scene_images_training.svg`)
+
+When downloading a pre-trained model, remember to change `ckpt_path` in `configs/*project.yaml` to point to your downloaded first-stage model (see ->Training).
+
+Scene image generation can be run with
+`python scripts/make_scene_samples.py --outdir=/some/outdir -r /path/to/pretrained/model --resolution=512,512`
+
+
+## Training on custom data
+
+Training on your own dataset can be beneficial to get better tokens and hence better images for your domain.
+Those are the steps to follow to make this work:
+1. install the repo with `conda env create -f environment.yaml`, `conda activate taming` and `pip install -e .`
+1. put your .jpg files in a folder `your_folder`
+2. create 2 text files a `xx_train.txt` and `xx_test.txt` that point to the files in your training and test set respectively (for example `find $(pwd)/your_folder -name "*.jpg" > train.txt`)
+3. adapt `configs/custom_vqgan.yaml` to point to these 2 files
+4. run `python main.py --base configs/custom_vqgan.yaml -t True --gpus 0,1` to
+   train on two GPUs. Use `--gpus 0,` (with a trailing comma) to train on a single GPU.
+
+## Data Preparation
+
+### ImageNet
+The code will try to download (through [Academic
+Torrents](http://academictorrents.com/)) and prepare ImageNet the first time it
+is used. However, since ImageNet is quite large, this requires a lot of disk
+space and time. If you already have ImageNet on your disk, you can speed things
+up by putting the data into
+`${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](https://github.com/intel-isl/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](https://pytorch.org/hub/intelisl_midas_v2/). When we prepared the data,
+the hub provided the [MiDaS
+v2.0](https://github.com/intel-isl/MiDaS/releases/tag/v2) 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](https://github.com/intel-isl/MiDaS/releases/tag/v2)!
+
+### 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](https://github.com/tkarras/progressive_growing_of_gans)).
+
+### FFHQ
+Create a symlink `data/ffhq` pointing to the `images1024x1024` folder obtained
+from the [FFHQ repository](https://github.com/NVlabs/ffhq-dataset).
+
+### 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](https://github.com/adrianmrit/flickrdatasets) to get started.
+We collected sufficiently large images from [flickr](https://www.flickr.com)
+(see `data/flickr_tags.txt` for a full list of tags used to find images)
+and various [subreddits](https://www.reddit.com/r/sfwpornnetwork/wiki/network)
+(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](https://arxiv.org/abs/1606.00915)
+trained on [COCO-Stuff](https://arxiv.org/abs/1612.03716). We used a [PyTorch
+reimplementation](https://github.com/kazuto1011/deeplab-pytorch) 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](https://cocodataset.org/). In addition, we use
+the [Stuff+thing PNG-style annotations on COCO 2017
+trainval](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip)
+annotations from [COCO-Stuff](https://github.com/nightrome/cocostuff), which
+should be placed under `data/cocostuffthings`.
+
+### ADE20k
+Create a symlink `data/ade20k_root` containing the contents of
+[ADEChallengeData2016.zip](http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip)
+from the [MIT Scene Parsing Benchmark](http://sceneparsing.csail.mit.edu/).
+
+## 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](https://k00.fr/uxy5usa9) 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](https://k00.fr/u0j2dtac)
+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](https://k00.fr/55rlxs6i)
+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](https://colab.research.google.com/github/CompVis/taming-transformers/blob/master/scripts/reconstruction_usage.ipynb). 
+In particular, the notebook compares two VQGANs with a downsampling factor of f=16 for each and codebook dimensionality of 1024 and 16384, 
+a VQGAN with f=8 and 8192 codebook entries and the discrete autoencoder of OpenAI's [DALL-E](https://github.com/openai/DALL-E) (which has f=8 and 8192 
+codebook entries).
+![firststages1](assets/first_stage_squirrels.png)
+![firststages2](assets/first_stage_mushrooms.png)
+
+### Other
+- A [video summary](https://www.youtube.com/watch?v=o7dqGcLDf0A&feature=emb_imp_woyt) by [Two Minute Papers](https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg).
+- A [video summary](https://www.youtube.com/watch?v=-wDSDtIAyWQ) by [Gradient Dude](https://www.youtube.com/c/GradientDude/about).
+- A [weights and biases report summarizing the paper](https://wandb.ai/ayush-thakur/taming-transformer/reports/-Overview-Taming-Transformers-for-High-Resolution-Image-Synthesis---Vmlldzo0NjEyMTY)
+by [ayulockin](https://github.com/ayulockin).
+- A [video summary](https://www.youtube.com/watch?v=JfUTd8fjtX8&feature=emb_imp_woyt) by [What's AI](https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg).
+- Take a look at [ak9250's notebook](https://github.com/ak9250/taming-transformers/blob/master/tamingtransformerscolab.ipynb) if you want to run the streamlit demos on Colab.
+
+### Text-to-Image Optimization via CLIP
+VQGAN has been successfully used as an image generator guided by the [CLIP](https://github.com/openai/CLIP) model, both for pure image generation
+from scratch and image-to-image translation. We recommend the following notebooks/videos/resources:
+
+ - [Advadnouns](https://twitter.com/advadnoun/status/1389316507134357506) Patreon and corresponding LatentVision notebooks: https://www.patreon.com/patronizeme
+ - The [notebook]( https://colab.research.google.com/drive/1L8oL-vLJXVcRzCFbPwOoMkPKJ8-aYdPN) of [Rivers Have Wings](https://twitter.com/RiversHaveWings).
+ - A [video](https://www.youtube.com/watch?v=90QDe6DQXF4&t=12s) explanation by [Dot CSV](https://www.youtube.com/channel/UCy5znSnfMsDwaLlROnZ7Qbg) (in Spanish, but English subtitles are available)
+
+![txt2img](assets/birddrawnbyachild.png)
+
+Text prompt: *'A bird drawn by a child'*
+
+## 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](https://github.com/hojonathanho/diffusion)
+- The very hackable transformer implementation [minGPT](https://github.com/karpathy/minGPT)
+- The good ol' [PatchGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) and [Learned Perceptual Similarity (LPIPS)](https://github.com/richzhang/PerceptualSimilarity)
+
+## 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}
+}
+```

taming-transformers/configs/coco_cond_stage.yaml

@@ -0,0 +1,49 @@
+model:
+  base_learning_rate: 4.5e-06
+  target: taming.models.vqgan.VQSegmentationModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    image_key: "segmentation"
+    n_labels: 183
+    ddconfig:
+      double_z: false
+      z_channels: 256
+      resolution: 256
+      in_channels: 183
+      out_ch: 183
+      ch: 128
+      ch_mult:
+      - 1
+      - 1
+      - 2
+      - 2
+      - 4
+      num_res_blocks: 2
+      attn_resolutions:
+      - 16
+      dropout: 0.0
+
+    lossconfig:
+      target: taming.modules.losses.segmentation.BCELossWithQuant
+      params:
+        codebook_weight: 1.0
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 12
+    train:
+      target: taming.data.coco.CocoImagesAndCaptionsTrain
+      params:
+        size: 296
+        crop_size: 256
+        onehot_segmentation: true
+        use_stuffthing: true
+    validation:
+      target: taming.data.coco.CocoImagesAndCaptionsValidation
+      params:
+        size: 256
+        crop_size: 256
+        onehot_segmentation: true
+        use_stuffthing: true

taming-transformers/configs/coco_scene_images_transformer.yaml

@@ -0,0 +1,80 @@
+model:
+  base_learning_rate: 4.5e-06
+  target: taming.models.cond_transformer.Net2NetTransformer
+  params:
+    cond_stage_key: objects_bbox
+    transformer_config:
+      target: taming.modules.transformer.mingpt.GPT
+      params:
+        vocab_size: 8192
+        block_size: 348  # = 256 + 92 = dim(vqgan_latent_space,16x16) + dim(conditional_builder.embedding_dim)
+        n_layer: 40
+        n_head: 16
+        n_embd: 1408
+        embd_pdrop: 0.1
+        resid_pdrop: 0.1
+        attn_pdrop: 0.1
+    first_stage_config:
+      target: taming.models.vqgan.VQModel
+      params:
+        ckpt_path: /path/to/coco_epoch117.ckpt  # https://heibox.uni-heidelberg.de/f/78dea9589974474c97c1/
+        embed_dim: 256
+        n_embed: 8192
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 1
+          - 2
+          - 2
+          - 4
+          num_res_blocks: 2
+          attn_resolutions:
+          - 16
+          dropout: 0.0
+        lossconfig:
+          target: taming.modules.losses.DummyLoss
+    cond_stage_config:
+      target: taming.models.dummy_cond_stage.DummyCondStage
+      params:
+        conditional_key: objects_bbox
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 6
+    train:
+      target: taming.data.annotated_objects_coco.AnnotatedObjectsCoco
+      params:
+        data_path: data/coco_annotations_100  # substitute with path to full dataset
+        split: train
+        keys: [image, objects_bbox, file_name, annotations]
+        no_tokens: 8192
+        target_image_size: 256
+        min_object_area: 0.00001
+        min_objects_per_image: 2
+        max_objects_per_image: 30
+        crop_method: random-1d
+        random_flip: true
+        use_group_parameter: true
+        encode_crop: true
+    validation:
+      target: taming.data.annotated_objects_coco.AnnotatedObjectsCoco
+      params:
+        data_path: data/coco_annotations_100  # substitute with path to full dataset
+        split: validation
+        keys: [image, objects_bbox, file_name, annotations]
+        no_tokens: 8192
+        target_image_size: 256
+        min_object_area: 0.00001
+        min_objects_per_image: 2
+        max_objects_per_image: 30
+        crop_method: center
+        random_flip: false
+        use_group_parameter: true
+        encode_crop: true

taming-transformers/configs/custom_vqgan.yaml

@@ -0,0 +1,43 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: taming.models.vqgan.VQModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    ddconfig:
+      double_z: False
+      z_channels: 256
+      resolution: 256
+      in_channels: 3
+      out_ch: 3
+      ch: 128
+      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [16]
+      dropout: 0.0
+
+    lossconfig:
+      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
+      params:
+        disc_conditional: False
+        disc_in_channels: 3
+        disc_start: 10000
+        disc_weight: 0.8
+        codebook_weight: 1.0
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 5
+    num_workers: 8
+    train:
+      target: taming.data.custom.CustomTrain
+      params:
+        training_images_list_file: some/training.txt
+        size: 256
+    validation:
+      target: taming.data.custom.CustomTest
+      params:
+        test_images_list_file: some/test.txt
+        size: 256
+

taming-transformers/configs/drin_transformer.yaml

@@ -0,0 +1,77 @@
+model:
+  base_learning_rate: 4.5e-06
+  target: taming.models.cond_transformer.Net2NetTransformer
+  params:
+    cond_stage_key: depth
+    transformer_config:
+      target: taming.modules.transformer.mingpt.GPT
+      params:
+        vocab_size: 1024
+        block_size: 512
+        n_layer: 24
+        n_head: 16
+        n_embd: 1024
+    first_stage_config:
+      target: taming.models.vqgan.VQModel
+      params:
+        ckpt_path: logs/2020-09-23T17-56-33_imagenet_vqgan/checkpoints/last.ckpt
+        embed_dim: 256
+        n_embed: 1024
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 1
+          - 2
+          - 2
+          - 4
+          num_res_blocks: 2
+          attn_resolutions:
+          - 16
+          dropout: 0.0
+        lossconfig:
+          target: taming.modules.losses.DummyLoss
+    cond_stage_config:
+      target: taming.models.vqgan.VQModel
+      params:
+        ckpt_path: logs/2020-11-03T15-34-24_imagenetdepth_vqgan/checkpoints/last.ckpt
+        embed_dim: 256
+        n_embed: 1024
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 256
+          in_channels: 1
+          out_ch: 1
+          ch: 128
+          ch_mult:
+          - 1
+          - 1
+          - 2
+          - 2
+          - 4
+          num_res_blocks: 2
+          attn_resolutions:
+          - 16
+          dropout: 0.0
+        lossconfig:
+          target: taming.modules.losses.DummyLoss
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 2
+    num_workers: 8
+    train:
+      target: taming.data.imagenet.RINTrainWithDepth
+      params:
+        size: 256
+    validation:
+      target: taming.data.imagenet.RINValidationWithDepth
+      params:
+        size: 256

taming-transformers/configs/faceshq_transformer.yaml

@@ -0,0 +1,61 @@
+model:
+  base_learning_rate: 4.5e-06
+  target: taming.models.cond_transformer.Net2NetTransformer
+  params:
+    cond_stage_key: coord
+    transformer_config:
+      target: taming.modules.transformer.mingpt.GPT
+      params:
+        vocab_size: 1024
+        block_size: 512
+        n_layer: 24
+        n_head: 16
+        n_embd: 1024
+    first_stage_config:
+      target: taming.models.vqgan.VQModel
+      params:
+        ckpt_path: logs/2020-11-09T13-33-36_faceshq_vqgan/checkpoints/last.ckpt
+        embed_dim: 256
+        n_embed: 1024
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 1
+          - 2
+          - 2
+          - 4
+          num_res_blocks: 2
+          attn_resolutions:
+          - 16
+          dropout: 0.0
+        lossconfig:
+          target: taming.modules.losses.DummyLoss
+    cond_stage_config:
+      target: taming.modules.misc.coord.CoordStage
+      params:
+        n_embed: 1024
+        down_factor: 16
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 2
+    num_workers: 8
+    train:
+      target: taming.data.faceshq.FacesHQTrain
+      params:
+        size: 256
+        crop_size: 256
+        coord: True
+    validation:
+      target: taming.data.faceshq.FacesHQValidation
+      params:
+        size: 256
+        crop_size: 256
+        coord: True

taming-transformers/configs/faceshq_vqgan.yaml

@@ -0,0 +1,42 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: taming.models.vqgan.VQModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    ddconfig:
+      double_z: False
+      z_channels: 256
+      resolution: 256
+      in_channels: 3
+      out_ch: 3
+      ch: 128
+      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [16]
+      dropout: 0.0
+
+    lossconfig:
+      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
+      params:
+        disc_conditional: False
+        disc_in_channels: 3
+        disc_start: 30001
+        disc_weight: 0.8
+        codebook_weight: 1.0
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 3
+    num_workers: 8
+    train:
+      target: taming.data.faceshq.FacesHQTrain
+      params:
+        size: 256
+        crop_size: 256
+    validation:
+      target: taming.data.faceshq.FacesHQValidation
+      params:
+        size: 256
+        crop_size: 256

taming-transformers/configs/imagenet_vqgan.yaml

@@ -0,0 +1,42 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: taming.models.vqgan.VQModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    ddconfig:
+      double_z: False
+      z_channels: 256
+      resolution: 256
+      in_channels: 3
+      out_ch: 3
+      ch: 128
+      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [16]
+      dropout: 0.0
+
+    lossconfig:
+      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
+      params:
+        disc_conditional: False
+        disc_in_channels: 3
+        disc_start: 250001
+        disc_weight: 0.8
+        codebook_weight: 1.0
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 12
+    num_workers: 24
+    train:
+      target: taming.data.imagenet.ImageNetTrain
+      params:
+        config:
+          size: 256
+    validation:
+      target: taming.data.imagenet.ImageNetValidation
+      params:
+        config:
+          size: 256

taming-transformers/configs/imagenetdepth_vqgan.yaml

@@ -0,0 +1,41 @@
+model:
+  base_learning_rate: 4.5e-6
+  target: taming.models.vqgan.VQModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    image_key: depth
+    ddconfig:
+      double_z: False
+      z_channels: 256
+      resolution: 256
+      in_channels: 1
+      out_ch: 1
+      ch: 128
+      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [16]
+      dropout: 0.0
+
+    lossconfig:
+      target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator
+      params:
+        disc_conditional: False
+        disc_in_channels: 1
+        disc_start: 50001
+        disc_weight: 0.75
+        codebook_weight: 1.0
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 3
+    num_workers: 8
+    train:
+      target: taming.data.imagenet.ImageNetTrainWithDepth
+      params:
+        size: 256
+    validation:
+      target: taming.data.imagenet.ImageNetValidationWithDepth
+      params:
+        size: 256

taming-transformers/configs/open_images_scene_images_transformer.yaml

@@ -0,0 +1,86 @@
+model:
+  base_learning_rate: 4.5e-06
+  target: taming.models.cond_transformer.Net2NetTransformer
+  params:
+    cond_stage_key: objects_bbox
+    transformer_config:
+      target: taming.modules.transformer.mingpt.GPT
+      params:
+        vocab_size: 8192
+        block_size: 348  # = 256 + 92 = dim(vqgan_latent_space,16x16) + dim(conditional_builder.embedding_dim)
+        n_layer: 36
+        n_head: 16
+        n_embd: 1536
+        embd_pdrop: 0.1
+        resid_pdrop: 0.1
+        attn_pdrop: 0.1
+    first_stage_config:
+      target: taming.models.vqgan.VQModel
+      params:
+        ckpt_path: /path/to/coco_oi_epoch12.ckpt  # https://heibox.uni-heidelberg.de/f/461d9a9f4fcf48ab84f4/
+        embed_dim: 256
+        n_embed: 8192
+        ddconfig:
+          double_z: false
+          z_channels: 256
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 1
+          - 2
+          - 2
+          - 4
+          num_res_blocks: 2
+          attn_resolutions:
+          - 16
+          dropout: 0.0
+        lossconfig:
+          target: taming.modules.losses.DummyLoss
+    cond_stage_config:
+      target: taming.models.dummy_cond_stage.DummyCondStage
+      params:
+        conditional_key: objects_bbox
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 6
+    train:
+      target: taming.data.annotated_objects_open_images.AnnotatedObjectsOpenImages
+      params:
+        data_path: data/open_images_annotations_100  # substitute with path to full dataset
+        split: train
+        keys: [image, objects_bbox, file_name, annotations]
+        no_tokens: 8192
+        target_image_size: 256
+        category_allow_list_target: taming.data.open_images_helper.top_300_classes_plus_coco_compatibility
+        category_mapping_target: taming.data.open_images_helper.open_images_unify_categories_for_coco
+        min_object_area: 0.0001
+        min_objects_per_image: 2
+        max_objects_per_image: 30
+        crop_method: random-2d
+        random_flip: true
+        use_group_parameter: true
+        use_additional_parameters: true
+        encode_crop: true
+    validation:
+      target: taming.data.annotated_objects_open_images.AnnotatedObjectsOpenImages
+      params:
+        data_path: data/open_images_annotations_100  # substitute with path to full dataset
+        split: validation
+        keys: [image, objects_bbox, file_name, annotations]
+        no_tokens: 8192
+        target_image_size: 256
+        category_allow_list_target: taming.data.open_images_helper.top_300_classes_plus_coco_compatibility
+        category_mapping_target: taming.data.open_images_helper.open_images_unify_categories_for_coco
+        min_object_area: 0.0001
+        min_objects_per_image: 2
+        max_objects_per_image: 30
+        crop_method: center
+        random_flip: false
+        use_group_parameter: true
+        use_additional_parameters: true
+        encode_crop: true

taming-transformers/configs/sflckr_cond_stage.yaml

@@ -0,0 +1,43 @@
+model:
+  base_learning_rate: 4.5e-06
+  target: taming.models.vqgan.VQSegmentationModel
+  params:
+    embed_dim: 256
+    n_embed: 1024
+    image_key: "segmentation"
+    n_labels: 182
+    ddconfig:
+      double_z: false
+      z_channels: 256
+      resolution: 256
+      in_channels: 182
+      out_ch: 182
+      ch: 128
+      ch_mult:
+      - 1
+      - 1
+      - 2
+      - 2
+      - 4
+      num_res_blocks: 2
+      attn_resolutions:
+      - 16
+      dropout: 0.0
+
+    lossconfig:
+      target: taming.modules.losses.segmentation.BCELossWithQuant
+      params:
+        codebook_weight: 1.0
+
+data:
+  target: cutlit.DataModuleFromConfig
+  params:
+    batch_size: 12
+    train:
+      target: taming.data.sflckr.Examples # adjust
+      params:
+        size: 256
+    validation:
+      target: taming.data.sflckr.Examples # adjust
+      params:
+        size: 256

taming-transformers/data/ade20k_examples.txt

@@ -0,0 +1,30 @@
+ADE_val_00000636.jpg
+ADE_val_00000126.jpg
+ADE_val_00001412.jpg
+ADE_val_00001845.jpg
+ADE_val_00001200.jpg
+ADE_val_00001578.jpg
+ADE_val_00000880.jpg
+ADE_val_00000875.jpg
+ADE_val_00000123.jpg
+ADE_val_00001209.jpg
+ADE_val_00000203.jpg
+ADE_val_00001851.jpg
+ADE_val_00001583.jpg
+ADE_val_00000287.jpg
+ADE_val_00001947.jpg
+ADE_val_00000262.jpg
+ADE_val_00000603.jpg
+ADE_val_00000125.jpg
+ADE_val_00001698.jpg
+ADE_val_00001966.jpg
+ADE_val_00000532.jpg
+ADE_val_00001177.jpg
+ADE_val_00000734.jpg
+ADE_val_00001498.jpg
+ADE_val_00001766.jpg
+ADE_val_00000303.jpg
+ADE_val_00000509.jpg
+ADE_val_00000573.jpg
+ADE_val_00000289.jpg
+ADE_val_00001388.jpg