This is the official implementation of the paper "Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance",
by Donghoon Ahn*, Hyoungwon Cho*, Jaewon Min, Wooseok Jang, Jungwoo Kim, Seonhwa Kim, Hyunhee Park, Kyonghwan Jin†, Seungryong Kim†.
Perturbed-Attention Guidance significantly enhances the sample quality of diffusion models without requiring external conditions, such as class labels or text prompts, or additional training. This proves particularly valuable in unconditional generation settings, where classifier-free guidance (CFG) is inapplicable. Our guidance can be utilized to enhance performance in various downstream tasks that leverage unconditional diffusion models, including ControlNet with an empty prompt and image restoration tasks like super-resolution and inpainting.
For more information, check out the project page and the paper.
2024-04-15: The 🧨 Diffusers pipeline for SDXL is now available, thanks to the awesome work of @multimodalart!
2024-04-12: The ComfyUI / SD WebUI Forge node is now available, thanks to the awesome work of @pamparamm!
2024-04-07: The application to PSLD is now available!
2024-03-31: The SD WebUI (automatic1111) extension is now available, thanks to the awesome work of @v0xie!
This repository is based on SusungHong/Self-Attention-Guidance, which is based on openai/guided-diffusion. The environment setup and the pretrained models are the same as the original repository. The main difference is that the sampling code is modified to support perturbed-attention guidance. Please refer to Using PAG in Guided-Diffusion for environment setup and sampling.
If you're interested in utilizing PAG with Stable Diffusion, we have made available a 🤗🧨diffusers community pipeline on the HuggingFace Hub. There's no need to download the entire source code; simply specifying the custom_pipeline argument to hyoungwoncho/sd_perturbed_attention_guidance with the latest diffusers library (v0.27) is all that's required. Example code is provided in sd_pag_demo.ipynb.
You can try a demo in Colab!
Loading Custom Pipeline
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance",
torch_dtype=torch.float16,
safety_checker=None
)
device="cuda"
pipe = pipe.to(device)
prompts = ["a corgi"]Sampling with PAG
output = pipe(
prompts,
width=512,
height=512,
num_inference_steps=50,
guidance_scale=0.0,
pag_scale=5.0,
pag_applied_layers_index=['m0']
).images[0]Sampling with PAG and CFG
output = pipe(
prompts,
width=512,
height=512,
num_inference_steps=50,
guidance_scale=4.0,
pag_scale=3.0,
pag_applied_layers_index=['m0']
).images[0]You can try a demo on Hugging Face Spaces!
Loading Custom Pipeline
from diffusers import StableDiffusionXLPipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
torch_dtype=torch.float16
)
device="cuda"
pipe = pipe.to(device)Sampling with PAG
output = pipe(
"",
num_inference_steps=25,
guidance_scale=0.0,
pag_scale=5.0,
pag_applied_layers=['mid']
).images[0]Sampling with PAG and CFG
output = pipe(
"the spirit of a tamagotchi wandering in the city of Vienna",
num_inference_steps=25,
guidance_scale=4.0,
pag_scale=3.0,
pag_applied_layers=['mid']
).images[0]The following commands are for setting up the environment using conda.
- Python 3.9
- PyTorch 1.11.0, Torchvision 0.12.0
- NVIDIA RTX 3090
conda create -n pag python=3.9
conda activate pag
conda install gxx_linux-64 #
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
If you have any issues with the environment setup, please refer to the original repository or create an issue. We will gladly check it.
Pretrained weights for ImageNet can be downloaded from the repository. Download and place them in the ./models/ directory.
Run the baseline sampling code first to check if the environment is set up correctly.
sh run/sample_uncond_ddim25_baseline.sh
The sampling code is modified to support perturbed-attention guidance. The following command samples from the pretrained model.
sh run/sample_uncond_ddpm250.sh
sh run/sample_cond_ddpm250.sh
sh run/sample_uncond_ddim25.sh
sh run/sample_cond_ddim25.sh
If mpiexec is installed, you can use the following command to sample from multiple GPUs.
sh run/sample_uncond_ddim25@multigpu.sh
it is same with run/sample_uncond_ddim25.sh except for the following part.
GPU_COUNT=8 # number of GPUs to use
export NCCL_P2P_DISABLE=1 # for multi-node sampling
mpiexec -n $GPU_COUNT
~ same code ~
--gpu_offset 0 # change --gpu to --gpu_offset
~ same code ~
Implementations applying PAG to downstream tasks are provided here.
Thanks to LituRout/PSLD, we applied PAG to PSLD based on the repository.
PSLD is a framework to solve linear inverse problems leveraging pre-trained latent diffusion models. Please refer to Min-Jaewon/PSLD_PAG to use PSLD with PAG.
Below pipeline is a modification of Stable Diffusion Inpainting pipeline to support PAG. You can try various pretrained weights for inpainting. Please refer to "Inpainting" section of an official document for details.
Loading Custom Pipeline
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance_inpaint",
torch_dtype=torch.float16,
safety_checker=None
)
device="cuda"
pipe = pipe.to(device)
Inpainting with PAG
output = pipe(
prompts,
image=init_image,
mask_image=mask_image,
num_inference_steps=50,
guidance_scale=0.0,
pag_scale=3.0,
pag_applied_layers_index=['u0']
).images[0]
Below pipeline is a modification of Stable Diffusion Upscaling pipeline to support PAG. You can try various pretrained weights for upscaling or super-resolution. Please refer to "Image-to-upscaler-to-super-resolution" section of an official document for details.
Loading Custom Pipeline
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance_sr",
torch_dtype=torch.float16,
safety_checker=None
)
device="cuda"
pipe = pipe.to(device)
Super-Resolution with PAG
output = pipe(
prompts,
image=lr_image,
num_inference_steps=50,
guidance_scale=0.0,
pag_scale=2.0,
pag_applied_layers_index=['u2']
).images[0]
ControlNet is a neural network structure to control diffusion models by adding extra conditions. The pipeline is a modification of StableDiffusionControlNetPipeline to support image generation with ControlNet and Perturbed-Attention Guidance (PAG).
In addition to the examples provided with Openpose, you can also generate using various conditions. Please refer to "ControlNet" section of an official document for details.
Loading ControlNet and Custom Piepline:
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose",
torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance_controlnet",
controlnet=controlnet,
torch_dtype=torch.float16
)
device="cuda"
pipe = pipe.to(device)
Prepare Conditional Images:
from controlnet_aux import OpenposeDetector
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
original_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(original_image)
prompts=""
Conditional Generation with ControlNet and PAG:
output = pipe(
prompts,
image=openpose_image,
num_inference_steps=50,
guidance_scale=0.0,
pag_scale=4.0,
pag_applied_layers_index=["m0"]
).images[0]
Thanks to the exceptional efforts of @v0xie and @pamparamm, you can now easily incorporate PAG into your pipelines or workflows.
WebUI (Automatic1111): v0xie/sd-webui-incantations
ComfyUI / SD WebUI Forge: pamparamm/sd-perturbed-attention
Diffusers SDXL: multimodalart/sdxl_perturbed_attention_guidance
If you find our work useful in your research, please cite our work as:
@article{ahn2024self,
title={Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance},
author={Ahn, Donghoon and Cho, Hyoungwon and Min, Jaewon and Jang, Wooseok and Kim, Jungwoo and Kim, SeonHwa and Park, Hyun Hee and Jin, Kyong Hwan and Kim, Seungryong},
journal={arXiv preprint arXiv:2403.17377},
year={2024}
}