Elucidating the Exposure Bias in Diffusion Models

This is the codebase for our paper Elucidating the Exposure Bias in Diffusion Models
https://arxiv.org/abs/2308.15321

The repository is heavily based on openai/guided-diffusion with the sampling solution Epsion Scaling (ADM-ES)
Feel free to check out our Epsilon Scaling repository for EDM: EDM-ES

Installation

The installation is the same with guided-diffusion

git clone https://github.com/forever208/DDPM-IP.git
cd DDPM-IP
conda create -n ADM python=3.8
conda activate ADM
pip install -e .
(note that, pytorch 1.10~1.13 is recommended as our experiments in paper were done with pytorch 1.10 and pytorch 2.0 has not been tested by us in this repo)

# install the missing packages
conda install mpi4py
conda install numpy
pip install Pillow
pip install opencv-python

Download ADM base models

We have released checkpoints for the main models in the paper. Before using these models, please review the corresponding model card to understand the intended use and limitations of these models.

Here are the download links for each model checkpoint:

• CIFAR-10 32x32: ADM.pt
• LSUN tower 64x64: ADM.pt
• FFHQ 128x128: ADM.pt
• ImageNet 64x64: ADM.pt
• ImageNet 128x128: ADM.pt
• ImageNet 128x128 classifier: ADM-classifier.pt

Sampling by ADM-ES

To sample from these models, you can use the image_sample.py for unconditional/conditional sampling or classifier_sample.py for classifier guidance sampling

We provide the full parameters for sampling below. --eps_scaler=1.0 refers to the ADM baseline.
For the Epsilon Scaling schedule kt+b, e set k=0 as default below, i.e. eps_scaler=b. If you want to try with kt+b schedule, uncomment the code in line 176-177 of gaussian_diffusion.py

CIFAR-10 (uncond) (1GPU, 100 steps, eps_scaler=1.017, sampling 50k images with batch_size 512):

mpirun -n 1 python scripts/image_sample.py \
--image_size 32 --timestep_respacing 100 --eps_scaler 1.017 \
--model_path MODEL_PATH \
--num_channels 128 --num_head_channels 32 --num_res_blocks 3 --attention_resolutions 16,8 \
--resblock_updown True --use_new_attention_order True --learn_sigma True --dropout 0.3 \
--diffusion_steps 1000 --noise_schedule cosine --use_scale_shift_norm True --batch_size 512 --num_samples 50000```

LSUN tower 64x64 (uncond):

mpirun -n 1 python scripts/image_sample.py \
--image_size 64 --timestep_respacing 100  --eps_scaler 1.006 \
--model_path MODEL_PATH \
--use_fp16 True --num_channels 192 --num_head_channels 64 --num_res_blocks 3 \
--attention_resolutions 32,16,8 --resblock_updown True --use_new_attention_order True \
--learn_sigma True --dropout 0.1 --diffusion_steps 1000 --noise_schedule cosine --use_scale_shift_norm True \
--rescale_learned_sigmas True --batch_size 256 --num_samples 50000

FFHQ 128x128 (uncond):

mpirun -n 1 python scripts/image_sample.py \
--image_size 128 --timestep_respacing 100 --eps_scaler 1.005 \
--model_path MODEL_PATH \
--use_fp16 True --num_channels 256 --num_head_channels 64 --num_res_blocks 3 \
--attention_resolutions 32,16,8 --resblock_updown True --use_new_attention_order True \
--learn_sigma True --dropout 0.1 --diffusion_steps 1000 --noise_schedule cosine --use_scale_shift_norm True \
--rescale_learned_sigmas True --batch_size 128 --num_samples 50000

ImageNet 64x64 (cond)

mpirun -n 1 python scripts/image_sample.py \
--image_size 64 --timestep_respacing 100 --class_cond True --eps_scaler 1.006 \
--model_path MODEL_PATH \
--use_fp16 True --num_channels 192 --num_head_channels 64 --num_res_blocks 3 \
--attention_resolutions 32,16,8 --resblock_updown True --use_new_attention_order True \
--learn_sigma True --dropout 0.1 --diffusion_steps 1000 --noise_schedule cosine --use_scale_shift_norm True \
--rescale_learned_sigmas True --batch_size 256 --num_samples 50000

ImageNet 128x128 (cond with classifier guidance)

mpirun -n 1 python scripts/classifier_sample.py \
--image_size 128 --timestep_respacing 100 --eps_scaler 1.004 \
--model_path MODEL_PATH \
--classifier_path CLASSIFIER_PATH --classifier_scale 0.5 --class_cond True \
--use_fp16 True --num_channels 256 --num_heads 4 --num_res_blocks 2 \
--attention_resolutions 32,16,8 --resblock_updown True \
--learn_sigma True --diffusion_steps 1000 --noise_schedule linear --use_scale_shift_norm True \
--batch_size 100 --num_samples 50000

Calculating FID

Please refer to README.md for the full instructions

FID Results

We provide the FID results and parameter settings of eps_scaler below

Exposure Bias

If you want to compute and plot the Epsilon L2-norm, checkout to branch epsilon_consecutive_sampling and epsilon_each_step_sampling.

If you want to compute and plot exposure bias metric delta_t, checkout to branch x_t_distribution_consecutive_sampling and x_t_distribution_each_step.

The figure below shows the corresponding results on LSUN tower dataset.

Prepare datasets

Please refer to README.md for the detailed data preparation.

Citation

@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}
}

If you want to know our training solution to exposure bias, feel free to check out our ICML 2023 paper and repository:

@article{ning2023input,
  title={Input Perturbation Reduces Exposure Bias in Diffusion Models},
  author={Ning, Mang and Sangineto, Enver and Porrello, Angelo and Calderara, Simone and Cucchiara, Rita},
  journal={arXiv preprint arXiv:2301.11706},
  year={2023}
}