update description for 1.45B spaces demo

Co-authored-by: ablattmann <andreas.blattmann@iwr.uni-heidelberg.de>

2022-04-08 03:51:05 -04:00

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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

News

April 2022

More pre-trained LDMs are available:
- A 1.45B model trained on the LAION-400M database.
- A class-conditional model on ImageNet, achieving a FID of 3.6 when using classifier-free guidance Available via a colab notebook .
- Our 1.45B latent diffusion LAION model was integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo:

Requirements

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

conda env create -f environment.yaml
conda activate ldm

Pretrained Models

A general list of all available checkpoints is available in via our model zoo. If you use any of these models in your work, we are always happy to receive a citation.

Text-to-Image

Download the pre-trained weights (5.7GB)

mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt

and sample with

python scripts/txt2img.py --prompt "a virus monster is playing guitar, oil on canvas" --ddim_eta 0.0 --n_samples 4 --n_iter 4 --scale 5.0  --ddim_steps 50

This will save each sample individually as well as a grid of size n_iter x n_samples at the specified output location (default: outputs/txt2img-samples). Quality, sampling speed and diversity are best controlled via the scale, ddim_steps and ddim_eta arguments. As a rule of thumb, higher values of scale produce better samples at the cost of a reduced output diversity.
Furthermore, increasing ddim_steps generally also gives higher quality samples, but returns are diminishing for values > 250. Fast sampling (i.e. low values of ddim_steps) while retaining good quality can be achieved by using --ddim_eta 0.0.

Beyond 256²

For certain inputs, simply running the model in a convolutional fashion on larger features than it was trained on can sometimes result in interesting results. To try it out, tune the H and W arguments (which will be integer-divided by 8 in order to calculate the corresponding latent size), e.g. run

python scripts/txt2img.py --prompt "a sunset behind a mountain range, vector image" --ddim_eta 1.0 --n_samples 1 --n_iter 1 --H 384 --W 1024 --scale 5.0

to create a sample of size 384x1024. Note, however, that controllability is reduced compared to the 256x256 setting.

The example below was generated using the above command.

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.

Class-Conditional ImageNet

Available via a notebook .

Unconditional Models

We also provide a script for sampling from unconditional LDMs (e.g. LSUN, FFHQ, ...). 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>

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)}.

Model Zoo

Pretrained Autoencoding Models

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>.

Coming Soon...

More inference scripts for conditional LDMs.
In the meantime, you can play with our colab notebook https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing

Comments

Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase and https://github.com/lucidrains/denoising-diffusion-pytorch. Thanks for open-sourcing!
The implementation of the transformer encoder is from x-transformers by lucidrains.

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}
}

16 KiB Raw Blame History

Latent Diffusion Models

News

April 2022

Requirements

Pretrained Models

Text-to-Image

Beyond 256²

Inpainting

Class-Conditional ImageNet

Unconditional Models

Train your own LDMs

Data preparation

Faces

LSUN

ImageNet

Model Training

Training autoencoder models

Training LDMs

Model Zoo

Pretrained Autoencoding Models

Get the models

Pretrained LDMs

Get the models

Coming Soon...

Comments

BibTeX

16 KiB

Raw Blame History