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

July 2022

• Inference code and model weights to run our retrieval-augmented diffusion models are now available. See this section. ### April 2022

• Thanks to Katherine Crowson, classifier-free guidance received a ~2x speedup and the PLMS sampler is available. See also this PR.

• Our 1.45B latent diffusion LAION model was integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo:

• 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 .

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.

Retrieval Augmented Diffusion Models

We include inference code to run our retrieval-augmented diffusion models (RDMs) as described in https://arxiv.org/abs/2204.11824.

To get started, install the additionally required python packages into your ldm environment shell script pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0 pip install git+https://github.com/arogozhnikov/einops.git and download the trained weights (preliminary ceckpoints):

bash mkdir -p models/rdm/rdm768x768/ wget -O models/rdm/rdm768x768/model.ckpt https://ommer-lab.com/files/rdm/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/knn2img.py --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: bash mkdir -p data/rdm/retrieval_databases wget -O data/rdm/retrieval_databases/artbench.zip https://ommer-lab.com/files/rdm/artbench_databases.zip wget -O data/rdm/retrieval_databases/openimages.zip https://ommer-lab.com/files/rdm/openimages_database.zip unzip data/rdm/retrieval_databases/artbench.zip -d data/rdm/retrieval_databases/ unzip data/rdm/retrieval_databases/openimages.zip -d data/rdm/retrieval_databases/ We also provide trained ScaNN search indices for ArtBench. Download and extract via bash mkdir -p data/rdm/searchers wget -O data/rdm/searchers/artbench.zip https://ommer-lab.com/files/rdm/artbench_searchers.zip unzip data/rdm/searchers/artbench.zip -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 bash python scripts/train_searcher.py

Retrieval based text-guided sampling with visual nearest neighbors can be started via python scripts/knn2img.py --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/train_searcher.py) 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

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.
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 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

shell script 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

shell script 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.
shell script 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/lsunchurches.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/lsunbedrooms.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/srbsr.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/layout2imgmodel.zip | | | Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semanticsynthesis256.zip | | | Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semanticsynthesis.zip | finetuned on resolution 512x512 |

Get the models

The LDMs listed above can jointly be downloaded and extracted via

shell script 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} }

@misc{https://doi.org/10.48550/arxiv.2204.11824, doi = {10.48550/ARXIV.2204.11824}, url = {https://arxiv.org/abs/2204.11824}, author = {Blattmann, Andreas and Rombach, Robin and Oktay, Kaan and Ommer, Björn}, 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 = {arXiv.org perpetual, non-exclusive license} }

```