Stable Diffusion

Unofficial Implementation of Stable Diffusion

Currently, the config and code in official Stable Diffusion is incompleted.

Thus, the repo aims to reproduce SD on different generation task. I highly recommend you to read the config to understand each fuction.

Task1 Unconditional Image Synthesis
Task2 Class-conditional Image Synthesis
Task3 Inpainting
Task4 Super-resolution
Task5 Text-to-Image
Task6 Layout-to-Image Synthesis
Task7 Semantic Image Synthesis
Task8 Image-to-Image
Task9 Depth-to-Image

If you find it useful, please cite their original paper.

arXiv | BibTeX

High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer

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.

STAGE1: Autoencoder

Training on your own dataset can be beneficial to get better tokens and hence better images for your domain. Those are the steps to follow to make this work:

install the repo with conda env create -f environment.yaml, conda activate ldm and pip install -e .
put your .jpg files in a folder your_folder
create 2 text files a xx_train.txt and xx_test.txt that point to the files in your training and test set respectively (for example find $(pwd)/your_folder -name "*.jpg" > train.txt)
adapt configs/custom_vqgan.yaml to point to these 2 files
run python main.py --base configs/custom_vqgan.yaml -t True --gpus 0,1 to train on two GPUs. Use --gpus 0, (with a trailing comma) to train on a single GPU.

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.

STAGE2: Diffusion Model

Unconditional

Training

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)}.

inference

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 pre_trained_models/ldm/<model_spec>/model.ckpt -l <logdir> -n <\#samples> --batch_size <batch_size> -c <\#ddim steps> -e <\#eta>

Class-conditional

Data preparation

train

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,

inference

class2img.py

ImageNet

Available via a notebook .

Inpainting

data preparation

python scripts/generate_llama_mask/gen_mask_dataset.py --config scripts/generate_llama_mask/data_gen_configs/random_medium_512.yaml --indir latent-diffusion/data/ --outdir /opt/data/private/latent-diffusion/data/x_inpaint --ext jpg
python scripts/generate_llama_mask/generate_csv.py --llama_masked_outdir /opt/data/private/latent-diffusion/data/INPAINTING/captain_inpaint/ --csv_out_path data/INPAINTING/x.csv

train

python main.py --base configs/latent-diffusion/inpainting_example_overfit.yaml -t Ture --gpus 0,1, -x xxx

inference

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.

Super-resolution

https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing

data preparation

train

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,

inference

Text-to-Image

data preparation

data/coco_images.txt
data/coco_txt.txt

train

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/txt2img/txt2img-sdv1.yaml -t --gpus 0,

inference

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.

Layout-to-Image

Data preparation

COCO format

train

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,

inference

python layout2img.py

Semantic-to-Image

data preparation

train

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,

inference

python mask2img.py

Image-to-Image

Image Modification with Stable Diffusion

By using a diffusion-denoising mechanism as first proposed by SDEdit, the model can be used for different tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script, we provide a script to perform image modification with Stable Diffusion.

The following describes an example where a rough sketch made in Pinta is converted into a detailed artwork.

python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8

Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.

Input

Outputs

This procedure can, for example, also be used to upscale samples from the base model.

Depth-to-Image

data preparation

train

inference

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 .

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

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):

mkdir -p pre_trained_models/rdm/rdm768x768/
wget -O pre_trained_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:

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

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

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

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>.

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

Comments

My codebase for the diffusion models builds heavily on
- OpenAI's ADM codebase
- 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}
}

Name		Name	Last commit message	Last commit date
Latest commit History 47 Commits
assets		assets
configs		configs
data		data
ldm		ldm
scripts		scripts
README.md		README.md
Stable_Diffusion_v1_Model_Card.md		Stable_Diffusion_v1_Model_Card.md
Stable_Diffusion_v2_Model_Card.md		Stable_Diffusion_v2_Model_Card.md
environment.yaml		environment.yaml
main.py		main.py
requirements.txt		requirements.txt
setup.py		setup.py

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Stable Diffusion

Unofficial Implementation of Stable Diffusion

Requirements

Pretrained Models

STAGE1: Autoencoder

Data preparation

Faces

LSUN

ImageNet

Model Training

Training autoencoder models

STAGE2: Diffusion Model

Unconditional

Training

inference

Class-conditional

Data preparation

train

inference

ImageNet

Inpainting

data preparation

train

inference

Super-resolution

data preparation

train

inference

Text-to-Image

data preparation

train

inference

Beyond 256²

Layout-to-Image

Data preparation

train

inference

Semantic-to-Image

data preparation

train

inference

Image-to-Image

Image Modification with Stable Diffusion

Depth-to-Image

data preparation

train

inference

News

July 2022

April 2022

Retrieval Augmented Diffusion Models

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

RDM with text-to-image retrieval

Coming Soon

Model Zoo

Pretrained Autoencoding Models

Get the models

Pretrained LDMs

Get the models

Comments

BibTeX

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