Awesome

Elucidating the Exposure Bias in Diffusion Models

This is the codebase for our paper Elucidating the Exposure Bias in Diffusion Models. We add Epsilon Scaling on LDM for improved sampling.

Sampling with Epsilon Scalig (ES)

After downloading the vanilla LDM ckpt, do sampling by specifying eps_scaler, custom_steps and eta respectively

For example, we use 100 NFE, eta=1 and eps_scaler=1 for celeba256 sampling below:

python scripts/sample_diffusion.py -r models/ldm/celeba256/model.ckpt \
--logdir sam_celeba256_100steps_eps_const_1000 --eps_scaler 1.000 \
--n_samples 64 --batch_size 64 --custom_steps 100 --eta 1

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.

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

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

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).

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

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>.

BibTeX

@article{ning2023elucidating,
  title={Elucidating the exposure bias in diffusion models},
  author={Ning, Mang and Li, Mingxiao and Su, Jianlin and Salah, Albert Ali and Ertugrul, Itir Onal},
  journal={arXiv preprint arXiv:2308.15321},
  year={2023}
}