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---
language:
- en
pipeline_tag: unconditional-image-generation
tags:
- Diffusion Models
- Stable Diffusion
- Perturbed-Attention Guidance
- PAG
---
# Perturbed-Attention Guidance for SDXL
<div style="display:flex">
<video loop>
<source src="pag_sdxl.mp4" type="video/mp4">
</video>
<video loop>
<source src="pag_uncond.mp4" type="video/mp4">
</video>
</div>
[Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance)
This repository is based on [Diffusers](https://huggingface.co/docs/diffusers/index). The pipeline is a modification of StableDiffusionXLPipeline to add Perturbed-Attention Guidance (PAG).
The original Perturbed-Attention Guidance for unconditional models and SD1.5 by [Hyoungwon Cho](https://huggingface.co/hyoungwoncho) is availiable at [hyoungwoncho/sd_perturbed_attention_guidance](https://huggingface.co/hyoungwoncho/sd_perturbed_attention_guidance)
## Quickstart
Loading Custom Pipeline:
```py
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)
```
Unconditional sampling with PAG:
![image/jpeg](uncond_generation_pag.jpg)
```py
output = pipe(
"",
num_inference_steps=50,
guidance_scale=0.0,
pag_scale=5.0,
pag_applied_layers=['mid']
).images
```
Sampling with PAG and CFG:
![image/jpeg](cfgpag.jpg)
```py
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
```
## Parameters
`guidance_scale` : gudiance scale of CFG (ex: `7.5`)
`pag_scale` : gudiance scale of PAG (ex: `4.0`)
`pag_applied_layers`: layer to apply perturbation (ex: ['mid'])
`pag_applied_layers_index` : index of the layers to apply perturbation (ex: ['m0', 'm1'])
## Stable Diffusion XL Demo
Soon