This is the implementation of the paper Improving Sample Quality of Diffusion Models Using Self-Attention Guidance by Hong et al. To gain insight from our exploration of the self-attention maps of diffusion models and for detailed explanations, please see our Paper and Project Page.
This repository is based on openai/guided-diffusion, and we modified feature extraction code from yandex-research/ddpm-segmentation to get the self-attention maps. The major implementation of our method is in ./guided_diffusion/gaussian_diffusion.py and ./guided_diffusion/unet.py.
All you need is to setup the environment, download existing models, and sample from them using our implementation. Neither further training nor a dataset is needed to apply self-attention guidance!
2023-08-14: This repository supports DDIM sampling with SAG.
2023-02-19: The Gradio Demo🤗 of SAG for Stable Diffusion is now available
2023-02-16: The Stable Diffusion pipeline of SAG is now available at huggingface/diffusers 🤗🧨
2023-02-01: The demo for Stable Diffusion is now available in Colab.
- Python 3.8, PyTorch 1.11.0
- 8 x NVIDIA RTX 3090 (set
backend="gloo"in./guided_diffusion/dist_util.pyif P2P access is not available)
git clone https://github.com/KU-CVLAB/Self-Attention-Guidance
conda create -n sag python=3.8 anaconda
conda activate sag
conda install mpi4py
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install blobfile
Pretrained weights for ImageNet and LSUN can be downloaded from the repository. Download and place them in the ./models/ directory.
You can sample from pretrained diffusion models with self-attention guidance by changing SAG_FLAGS in the following commands. Note that sampling with --guide_scale 1.0 means sampling without self-attention guidance. Below are the 4 examples.
- ImageNet 128x128 model (
--classifier_guidance Falsedeactivates classifier guidance):
SAMPLE_FLAGS="--batch_size 64 --num_samples 10000 --timestep_respacing 250"
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 128 --learn_sigma True --noise_schedule linear --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
SAG_FLAGS="--guide_scale 1.1 --guide_start 250 --sel_attn_block output --sel_attn_depth 8 --blur_sigma 3 --classifier_guidance True"
mpiexec -n $NUM_GPUS python classifier_sample.py $SAG_FLAGS $MODEL_FLAGS --classifier_scale 0.5 --classifier_path models/128x128_classifier.pt --model_path models/128x128_diffusion.pt $SAMPLE_FLAGS
- ImageNet 256x256 model (
--class_cond Truefor conditional models):
SAMPLE_FLAGS="--batch_size 16 --num_samples 10000 --timestep_respacing 250"
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
SAG_FLAGS="--guide_scale 1.5 --guide_start 250 --sel_attn_block output --sel_attn_depth 2 --blur_sigma 9 --classifier_guidance False"
mpiexec -n $NUM_GPUS python classifier_sample.py $SAG_FLAGS $MODEL_FLAGS --classifier_scale 0.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion_uncond.pt $SAMPLE_FLAGS
- LSUN Cat model (respaced to 250 steps):
SAMPLE_FLAGS="--batch_size 16 --num_samples 10000 --timestep_respacing 250"
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
SAG_FLAGS="--guide_scale 1.05 --guide_start 250 --sel_attn_block output --sel_attn_depth 2 --blur_sigma 9 --classifier_guidance False"
mpiexec -n $NUM_GPUS python image_sample.py $SAG_FLAGS $MODEL_FLAGS --model_path models/lsun_cat.pt $SAMPLE_FLAGS
- LSUN Horse model (respaced to 250 steps):
SAMPLE_FLAGS="--batch_size 16 --num_samples 10000 --timestep_respacing 250"
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
SAG_FLAGS="--guide_scale 1.01 --guide_start 250 --sel_attn_block output --sel_attn_depth 2 --blur_sigma 9 --classifier_guidance False"
mpiexec -n $NUM_GPUS python image_sample.py $SAG_FLAGS $MODEL_FLAGS --model_path models/lsun_horse.pt $SAMPLE_FLAGS
- ImageNet 128x128 model (DDIM 25 steps):
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --image_size 128 --learn_sigma True --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True --classifier_scale 1.0 --classifier_use_fp16 True"
SAMPLE_FLAGS="--batch_size 8 --num_samples 8 --timestep_respacing ddim25 --use_ddim True"
SAG_FLAGS="--guide_scale 1.1 --guide_start 25 --sel_attn_block output --sel_attn_depth 8 --blur_sigma 3 --classifier_guidance True"
mpiexec -n $NUM_GPUS python classifier_sample.py \
--model_path models/128x128_diffusion.pt \
--classifier_path models/128x128_classifier.pt \
$MODEL_FLAGS $CLASSIFIER_FLAGS $SAMPLE_FLAGS $SAG_FLAGS
Compatibility of self-attention guidance (SAG) and classifier guidance (CG) on ImageNet 128x128 model:
| SAG | CG | FID | sFID | Precision | Recall |
|---|---|---|---|---|---|
| 5.91 | 5.09 | 0.70 | 0.65 | ||
| V | 2.97 | 5.09 | 0.78 | 0.59 | |
| V | 5.11 | 4.09 | 0.72 | 0.65 | |
| V | V | 2.58 | 4.35 | 0.79 | 0.59 |
Results on pretrained models:
| Model | # of steps | Self-attention guidance scale | FID | sFID | IS | Precision | Recall |
|---|---|---|---|---|---|---|---|
| ImageNet 256×256 (Uncond.) | 250 | 0.0 (baseline) 0.5 0.8 |
26.21 20.31 20.08 |
6.35 5.09 5.77 |
39.70 45.30 45.56 |
0.61 0.66 0.68 |
0.63 0.61 0.59 |
| ImageNet 256×256 (Cond.) | 250 | 0.0 (baseline) 0.2 |
10.94 9.41 |
6.02 5.28 |
100.98 104.79 |
0.69 0.70 |
0.63 0.62 |
| LSUN Cat 256×256 | 250 | 0.0 (baseline) 0.05 |
7.03 6.87 |
8.24 8.21 |
- - |
0.60 0.60 |
0.53 0.50 |
| LSUN Horse 256×256 | 250 | 0.0 (baseline) 0.01 |
3.45 3.43 |
7.55 7.51 |
- - |
0.68 0.68 |
0.56 0.55 |
@inproceedings{hong2023improving,
title={Improving sample quality of diffusion models using self-attention guidance},
author={Hong, Susung and Lee, Gyuseong and Jang, Wooseok and Kim, Seungryong},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={7462--7471},
year={2023}
}