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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, BjΓΆrn Ommer
* equal contribution


July 2022

April 2022


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.

Retrieval Augmented Diffusion Models

rdm-figure We include inference code to run our retrieval-augmented diffusion models (RDMs) as described in

To get started, install the additionally required python packages into your ldm environment

pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0
pip install git+

and download the trained weights (preliminary ceckpoints):

mkdir -p models/rdm/rdm768x768/
wget -O models/rdm/rdm768x768/model.ckpt

As these models are conditioned on a set of CLIP image embeddings, our RDMs support different inference modes, which are described in the following.

RDM with text-prompt only (no explicit retrieval needed)

Since CLIP offers a shared image/text feature space, and RDMs learn to cover a neighborhood of a given example during training, we can directly take a CLIP text embedding of a given prompt and condition on it. Run this mode via

python scripts/  --prompt "a happy bear reading a newspaper, oil on canvas"

RDM with text-to-image retrieval

To be able to run a RDM conditioned on a text-prompt and additionally images retrieved from this prompt, you will also need to download the corresponding retrieval database. We provide two distinct databases extracted from the Openimages- and ArtBench- datasets. Interchanging the databases results in different capabilities of the model as visualized below, although the learned weights are the same in both cases.

Download the retrieval-databases which contain the retrieval-datasets (Openimages (~11GB) and ArtBench (~82MB)) compressed into CLIP image embeddings:

mkdir -p data/rdm/retrieval_databases
wget -O data/rdm/retrieval_databases/
wget -O data/rdm/retrieval_databases/
unzip data/rdm/retrieval_databases/ -d data/rdm/retrieval_databases/
unzip data/rdm/retrieval_databases/ -d data/rdm/retrieval_databases/

We also provide trained ScaNN search indices for ArtBench. Download and extract via

mkdir -p data/rdm/searchers
wget -O data/rdm/searchers/
unzip data/rdm/searchers/ -d data/rdm/searchers

Since the index for OpenImages is large (~21 GB), we provide a script to create and save it for usage during sampling. Note however, that sampling with the OpenImages database will not be possible without this index. Run the script via

python scripts/

Retrieval based text-guided sampling with visual nearest neighbors can be started via

python scripts/  --prompt "a happy pineapple" --use_neighbors --knn <number_of_neighbors> 

Note that the maximum supported number of neighbors is 20. The database can be changed via the cmd parameter --database which can be [openimages, artbench-art_nouveau, artbench-baroque, artbench-expressionism, artbench-impressionism, artbench-post_impressionism, artbench-realism, artbench-renaissance, artbench-romanticism, artbench-surrealism, artbench-ukiyo_e]. For using --database openimages, the above script (scripts/ must be executed before. Due to their relatively small size, the artbench datasetbases are best suited for creating more abstract concepts and do not work well for detailed text control.

Coming Soon

  • better models
  • more resolutions
  • image-to-image retrieval



Download the pre-trained weights (5.7GB)

mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt

and sample with

python scripts/ --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.
Faster sampling (i.e. even lower values of ddim_steps) while retaining good quality can be achieved by using --ddim_eta 0.0 and --plms (see Pseudo Numerical Methods for Diffusion Models on Manifolds).

Beyond 256Β²

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/ --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. text2img-figure-conv



Download the pre-trained weights

wget -O models/ldm/inpainting_big/last.ckpt

and sample with

python scripts/ --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 . class-conditional

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/ -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


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


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 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.


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:

β”œβ”€β”€ n01440764
β”‚   β”œβ”€β”€ n01440764_10026.JPEG
β”‚   β”œβ”€β”€ n01440764_10027.JPEG
β”‚   β”œβ”€β”€ ...
β”œβ”€β”€ n01443537
β”‚   β”œβ”€β”€ n01443537_10007.JPEG
β”‚   β”œβ”€β”€ n01443537_10014.JPEG
β”‚   β”œβ”€β”€ ...
β”œβ”€β”€ ...

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 --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 --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
f=4, VQ (Z=8192, d=3) 1.06 658131 25.21 +/- 4.17 0.72 +/- 0.26 no attention
f=8, VQ (Z=16384, d=4) 1.14 971043 23.07 +/- 3.99 1.17 +/- 0.36
f=8, VQ (Z=256, d=4) 1.49 1608649 22.35 +/- 3.81 1.26 +/- 0.37
f=16, VQ (Z=16384, d=8) 5.15 1101166 20.83 +/- 3.61 1.73 +/- 0.43
f=4, KL 0.27 176991 27.53 +/- 4.54 0.55 +/- 0.24
f=8, KL 0.90 246803 24.19 +/- 4.19 1.02 +/- 0.35
f=16, KL (d=16) 0.87 442998 24.08 +/- 4.22 1.07 +/- 0.36
f=32, KL (d=64) 2.04 406763 22.27 +/- 3.93 1.41 +/- 0.40

Get the models

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

bash scripts/

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
FFHQ Unconditional Image Synthesis LDM-VQ-4 (200 DDIM steps, eta=1) 4.98 (4.98) 4.50 (4.50) 0.73 0.50
LSUN-Churches Unconditional Image Synthesis LDM-KL-8 (400 DDIM steps, eta=0) 4.02 (4.02) 2.72 0.64 0.52
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
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** *: 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 finetuned from LAION
OpenImages Super-resolution LDM-VQ-4 N/A N/A N/A N/A BSR image degradation
OpenImages Layout-to-Image Synthesis LDM-VQ-4 (200 DDIM steps, eta=0) 32.02 15.92 N/A N/A
Landscapes Semantic Image Synthesis LDM-VQ-4 N/A N/A N/A N/A
Landscapes Semantic Image Synthesis LDM-VQ-4 N/A N/A N/A N/A finetuned on resolution 512x512

Get the models

The LDMs listed above can jointly be downloaded and extracted via

bash scripts/

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

Coming Soon...



      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and BjΓΆrn Ommer},

  doi = {10.48550/ARXIV.2204.11824},
  url = {},
  author = {Blattmann, Andreas and Rombach, Robin and Oktay, Kaan and Ommer, BjΓΆrn},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Retrieval-Augmented Diffusion Models},
  publisher = {arXiv},
  year = {2022},  
  copyright = { perpetual, non-exclusive license}


High-Resolution Image Synthesis with Latent Diffusion Models







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