# Latent Diffusion Models [arXiv](https://arxiv.org/abs/2112.10752) | [BibTeX](#bibtex)

[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)
[Robin Rombach](https://github.com/rromb)\*, [Andreas Blattmann](https://github.com/ablattmann)\*, [Dominik Lorenz](https://github.com/qp-qp)\, [Patrick Esser](https://github.com/pesser), [BjΓΆrn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)
\* equal contribution

## News ### July 2022 - Inference code and model weights to run our [retrieval-augmented diffusion models](https://arxiv.org/abs/2204.11824) are now available. See [this section](#retrieval-augmented-diffusion-models). ### April 2022 - Thanks to [Katherine Crowson](https://github.com/crowsonkb), classifier-free guidance received a ~2x speedup and the [PLMS sampler](https://arxiv.org/abs/2202.09778) is available. See also [this PR](https://github.com/CompVis/latent-diffusion/pull/51). - Our 1.45B [latent diffusion LAION model](#text-to-image) was integrated into [Huggingface Spaces πŸ€—](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/multimodalart/latentdiffusion) - More pre-trained LDMs are available: - A 1.45B [model](#text-to-image) trained on the [LAION-400M](https://arxiv.org/abs/2111.02114) database. - A class-conditional model on ImageNet, achieving a FID of 3.6 when using [classifier-free guidance](https://openreview.net/pdf?id=qw8AKxfYbI) Available via a [colab notebook](https://colab.research.google.com/github/CompVis/latent-diffusion/blob/main/scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin]. ## Requirements A suitable [conda](https://conda.io/) 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](#model-zoo). If you use any of these models in your work, we are always happy to receive a [citation](#bibtex). ## Retrieval Augmented Diffusion Models ![rdm-figure](assets/rdm-preview.jpg) We include inference code to run our retrieval-augmented diffusion models (RDMs) as described in [https://arxiv.org/abs/2204.11824](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-](https://storage.googleapis.com/openimages/web/index.html) and [ArtBench-](https://github.com/liaopeiyuan/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](https://storage.googleapis.com/openimages/web/index.html) (~11GB) and [ArtBench](https://github.com/liaopeiyuan/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](https://github.com/google-research/google-research/tree/master/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 ``` 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 ![text2img-figure](assets/txt2img-preview.png) 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](https://arxiv.org/abs/2202.09778)). #### 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/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. ![text2img-figure-conv](assets/txt2img-convsample.png) ## Inpainting ![inpainting](assets/inpainting.png) 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 `_mask.png` like the examples provided in `data/inpainting_examples`. ## Class-Conditional ImageNet Available via a [notebook](scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin]. ![class-conditional](assets/birdhouse.png) [colab]: [colab-cin]: ## Unconditional Models We also provide a script for sampling from unconditional LDMs (e.g. LSUN, FFHQ, ...). Start it via ```shell script CUDA_VISIBLE_DEVICES= python scripts/sample_diffusion.py -r models/ldm//model.ckpt -l -n <\#samples> --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](https://github.com/CompVis/taming-transformers#celeba-hq) repository. ### LSUN The LSUN datasets can be conveniently downloaded via the script available [here](https://github.com/fyu/lsun). We performed a custom split into training and validation images, and provide the corresponding filenames at [https://ommer-lab.com/files/lsun.zip](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](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: ``` ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ β”œβ”€β”€ n01440764 β”‚ β”œβ”€β”€ n01440764_10026.JPEG β”‚ β”œβ”€β”€ n01440764_10027.JPEG β”‚ β”œβ”€β”€ ... β”œβ”€β”€ n01443537 β”‚ β”œβ”€β”€ n01443537_10007.JPEG β”‚ β”œβ”€β”€ n01443537_10014.JPEG β”‚ β”œβ”€β”€ ... β”œβ”€β”€ ... ``` If you haven't extracted the data, you can also place `ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into `${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/_`. ### 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= python main.py --base configs/autoencoder/.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](https://github.com/CompVis/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= python main.py --base configs/latent-diffusion/.yaml -t --gpus 0, ``` where ```` 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 ![rec2](assets/reconstruction2.png) 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/` ## 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](https://github.com/openai/guided-diffusion) | | 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 ```shell script bash scripts/download_models.sh ``` The models can then be found in `models/ldm/`. ## 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](https://github.com/openai/guided-diffusion) and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). Thanks for open-sourcing! - The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). ## 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 BjΓΆrn 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, 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 = {arXiv.org perpetual, non-exclusive license} } ```