| # Latent Diffusion Models | |
| [arXiv](https://arxiv.org/abs/2112.10752) | [BibTeX](#bibtex) | |
| <p align="center"> | |
| <img src=assets/results.gif /> | |
| </p> | |
| [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)<br/> | |
| [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), | |
| [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/> | |
| \* equal contribution | |
| <p align="center"> | |
| <img src=assets/modelfigure.png /> | |
| </p> | |
| ## News | |
| ### April 2022 | |
| - 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). | |
| ## 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` (added by Katherine Crowson, see [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778)). | |
| #### 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](scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin]. | |
|  | |
| [colab]: <https://colab.research.google.com/assets/colab-badge.svg> | |
| [colab-cin]: <https://colab.research.google.com/github/CompVis/latent-diffusion/blob/main/scripts/latent_imagenet_diffusion.ipynb> | |
| ## 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](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: | |
| ``` | |
| ${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/ | |
| ├── n01440764 | |
| │ ├── n01440764_10026.JPEG | |
| │ ├── n01440764_10027.JPEG | |
| │ ├── ... | |
| ├── n01443537 | |
| │ ├── n01443537_10007.JPEG | |
| │ ├── n01443537_10014.JPEG | |
| │ ├── ... | |
| ├── ... | |
| ``` | |
| If you haven't extracted the data, you can also place | |
| `ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into | |
| `${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](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=<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/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/<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](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 Björn Ommer}, | |
| year={2021}, | |
| eprint={2112.10752}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |