/latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

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Latent Diffusion Models

arXiv | BibTeX

High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach*, Andreas Blattmann*, Dominik Lorenz, Patrick Esser, Björn Ommer
* equal contribution

Requirements

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

conda env create -f environment.yaml
conda activate ldm

Model Zoo

Pretrained Autoencoding Models

rec2

All models were trained until convergence (no further substantial improvement in rFID).

Model rFID vs val train steps PSNR PSIM Link Comments
f=4, VQ (Z=8192, d=3) 0.58 533066 27.43 +/- 4.26 0.53 +/- 0.21 https://ommer-lab.com/files/latent-diffusion/vq-f4.zip
f=4, VQ (Z=8192, d=3) 1.06 658131 25.21 +/- 4.17 0.72 +/- 0.26 https://heibox.uni-heidelberg.de/f/9c6681f64bb94338a069/?dl=1 no attention
f=8, VQ (Z=16384, d=4) 1.14 971043 23.07 +/- 3.99 1.17 +/- 0.36 https://ommer-lab.com/files/latent-diffusion/vq-f8.zip
f=8, VQ (Z=256, d=4) 1.49 1608649 22.35 +/- 3.81 1.26 +/- 0.37 https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip
f=16, VQ (Z=16384, d=8) 5.15 1101166 20.83 +/- 3.61 1.73 +/- 0.43 https://heibox.uni-heidelberg.de/f/0e42b04e2e904890a9b6/?dl=1
f=4, KL 0.27 176991 27.53 +/- 4.54 0.55 +/- 0.24 https://ommer-lab.com/files/latent-diffusion/kl-f4.zip
f=8, KL 0.90 246803 24.19 +/- 4.19 1.02 +/- 0.35 https://ommer-lab.com/files/latent-diffusion/kl-f8.zip
f=16, KL (d=16) 0.87 442998 24.08 +/- 4.22 1.07 +/- 0.36 https://ommer-lab.com/files/latent-diffusion/kl-f16.zip
f=32, KL (d=64) 2.04 406763 22.27 +/- 3.93 1.41 +/- 0.40 https://ommer-lab.com/files/latent-diffusion/kl-f32.zip

Get the models

Running the following script downloads und extracts all available pretrained autoencoding models.

bash scripts/download_first_stages.sh

The first stage models can then be found in models/first_stage_models/<model_spec>

Pretrained LDMs

Datset Task Model FID IS Prec Recall Link Comments
CelebA-HQ Unconditional Image Synthesis LDM-VQ-4 (200 DDIM steps, eta=0) 5.11 (5.11) 3.29 0.72 0.49 https://ommer-lab.com/files/latent-diffusion/celeba.zip
FFHQ Unconditional Image Synthesis LDM-VQ-4 (200 DDIM steps, eta=1) 4.98 (4.98) 4.50 (4.50) 0.73 0.50 https://ommer-lab.com/files/latent-diffusion/ffhq.zip
LSUN-Churches Unconditional Image Synthesis LDM-KL-8 (400 DDIM steps, eta=0) 4.02 (4.02) 2.72 0.64 0.52 https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip
LSUN-Bedrooms Unconditional Image Synthesis LDM-VQ-4 (200 DDIM steps, eta=1) 2.95 (3.0) 2.22 (2.23) 0.66 0.48 https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip
ImageNet Class-conditional Image Synthesis LDM-VQ-8 (200 DDIM steps, eta=1) 7.77(7.76)* /15.82** 201.56(209.52)* /78.82** 0.84* / 0.65** 0.35* / 0.63** https://ommer-lab.com/files/latent-diffusion/cin.zip *: w/ guiding, classifier_scale 10 **: w/o guiding, scores in bracket calculated with script provided by ADM
Conceptual Captions Text-conditional Image Synthesis LDM-VQ-f4 (100 DDIM steps, eta=0) 16.79 13.89 N/A N/A https://ommer-lab.com/files/latent-diffusion/text2img.zip finetuned from LAION
OpenImages Super-resolution LDM-VQ-4 N/A N/A N/A N/A https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip BSR image degradation
OpenImages Layout-to-Image Synthesis LDM-VQ-4 (200 DDIM steps, eta=0) 32.02 15.92 N/A N/A https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip
Landscapes Semantic Image Synthesis LDM-VQ-4 N/A N/A N/A N/A https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip
Landscapes Semantic Image Synthesis LDM-VQ-4 N/A N/A N/A N/A https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip finetuned on resolution 512x512

Get the models

The LDMs listed above can jointly be downloaded and extracted via

bash scripts/download_models.sh

The models can then be found in models/ldm/<model_spec>.

Sampling with unconditional models

We provide a first script for sampling from our unconditional models. Start it via

CUDA_VISIBLE_DEVICES=<GPU_ID> python scripts/sample_diffusion.py -r models/ldm/<model_spec>/model.ckpt -l <logdir> -n <\#samples> --batch_size <batch_size> -c <\#ddim steps> -e <\#eta>

Inpainting

inpainting

Download the pre-trained weights

wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1

and sample with

python scripts/inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results

indir should contain images *.png and masks <image_fname>_mask.png like the examples provided in data/inpainting_examples.

Train your own LDMs

Data preparation

Faces

For downloading the CelebA-HQ and FFHQ datasets, proceed as described in the taming-transformers repository.

LSUN

The LSUN datasets can be conveniently downloaded via the script available here. We performed a custom split into training and validation images, and provide the corresponding filenames at https://ommer-lab.com/files/lsun.zip. After downloading, extract them to ./data/lsun. The beds/cats/churches subsets should also be placed/symlinked at ./data/lsun/bedrooms/./data/lsun/cats/./data/lsun/churches, respectively.

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.

Model Training

Logs and checkpoints for trained models are saved to logs/<START_DATE_AND_TIME>_<config_spec>.

Training autoencoder models

Configs for training a KL-regularized autoencoder on ImageNet are provided at configs/autoencoder. Training can be started by running

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec>.yaml -t --gpus 0,

where config_spec is one of {autoencoder_kl_8x8x64(f=32, d=64), autoencoder_kl_16x16x16(f=16, d=16), autoencoder_kl_32x32x4(f=8, d=4), autoencoder_kl_64x64x3(f=4, d=3)}.

For training VQ-regularized models, see the taming-transformers repository.

Training LDMs

In configs/latent-diffusion/ we provide configs for training LDMs on the LSUN-, CelebA-HQ, FFHQ and ImageNet datasets. Training can be started by running

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,

where <config_spec> is one of {celebahq-ldm-vq-4(f=4, VQ-reg. autoencoder, spatial size 64x64x3),ffhq-ldm-vq-4(f=4, VQ-reg. autoencoder, spatial size 64x64x3), lsun_bedrooms-ldm-vq-4(f=4, VQ-reg. autoencoder, spatial size 64x64x3), lsun_churches-ldm-vq-4(f=8, KL-reg. autoencoder, spatial size 32x32x4),cin-ldm-vq-8(f=8, VQ-reg. autoencoder, spatial size 32x32x4)}.

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BibTeX

@misc{rombach2021highresolution,
      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2021},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}