---
license: openrail++
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- controlnet
---

# Aesthetic ControlNet

ControlNet is a method that can be used to condition diffusion models on arbitrary input features, such as image edges, segmentation maps, or human poses.
For more information about ControlNet, please have a look at this [thread](https://twitter.com/krea_ai/status/1626672218477559809) or at the original [work](https://arxiv.org/pdf/2302.05543.pdf) by Lvmin Zhang and Maneesh Agrawala.

Aesthetic ControlNet is a version of this technique that uses image features extracted using a [Canny edge detector](https://docs.opencv.org/4.x/da/d22/tutorial_py_canny.html) to guide a text-to-image diffusion model trained with aesthetic data.

![Example](./examples.jpg)


### Diffusers
Install the following dependencies and then run the code below:

```bash
pip install opencv-python numpy git+https://github.com/huggingface/diffusers.git
```


```py
import cv2
import numpy as np
from diffusers import StableDiffusionControlNetPipeline, EulerAncestralDiscreteScheduler
from diffusers.utils import load_image

image = load_image("https://huggingface.co/krea/aesthetic-controlnet/resolve/main/krea.jpg")

image = np.array(image)

low_threshold = 100
high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)

pipe = StableDiffusionControlNetPipeline.from_pretrained("krea/aesthetic-controlnet").to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

output = pipe(
    "fantasy flowers",
    canny_image,
    num_inference_steps=20,
    guidance_scale=4,
    width=768,
    height=768,
)

result = output.images[0]
result.save("result.png")
```

## Misuse and Malicious Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

## Sout-outs
Thanks to [@thibaudz](https://twitter.com/thibaudz) for creating a version of controlnet compatible with Stable Diffusion 2.1.