packages/tasks/src/image-to-image/about.md

## Use Cases

### Style transfer

One of the most popular use cases of image-to-image is style transfer. Style transfer models can convert a normal photography into a painting in the style of a famous painter.

## Task Variants

### Image inpainting

Image inpainting is widely used during photography editing to remove unwanted objects, such as poles, wires, or sensor
dust.

### Image colorization

Old or black and white images can be brought up to life using an image colorization model.

### Super Resolution

Super-resolution models increase the resolution of an image, allowing for higher-quality viewing and printing.

## Inference

You can use pipelines for image-to-image in 🧨diffusers library to easily use image-to-image models. See an example for `StableDiffusionImg2ImgPipeline` below.

```python
from PIL import Image
from diffusers import StableDiffusionImg2ImgPipeline

model_id_or_path = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe = pipe.to(cuda)

init_image = Image.open("mountains_image.jpeg").convert("RGB").resize((768, 512))
prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
images[0].save("fantasy_landscape.png")
```

You can use [huggingface.js](https://github.com/huggingface/huggingface.js) to infer image-to-image models on Hugging Face Hub.

```javascript
import { HfInference } from "@huggingface/inference";

const inference = new HfInference(HF_ACCESS_TOKEN);
await inference.imageToImage({
	data: await (await fetch("image")).blob(),
	model: "timbrooks/instruct-pix2pix",
	parameters: {
		prompt: "Deblur this image",
	},
});
```

## ControlNet

Controlling the outputs of diffusion models only with a text prompt is a challenging problem. ControlNet is a neural network model that provides image-based control to diffusion models. Control images can be edges or other landmarks extracted from a source image.

Many ControlNet models were trained in our community event, JAX Diffusers sprint. You can see the full list of the ControlNet models available [here](https://huggingface.co/spaces/jax-diffusers-event/leaderboard).

## Most Used Model for the Task

Pix2Pix is a popular model used for image-to-image translation tasks. It is based on a conditional-GAN (generative adversarial network) where instead of a noise vector a 2D image is given as input. More information about Pix2Pix can be retrieved from this [link](https://phillipi.github.io/pix2pix/) where the associated paper and the GitHub repository can be found.

The images below show some examples extracted from the Pix2Pix paper. This model can be applied to various use cases. It is capable of relatively simpler things, e.g., converting a grayscale image to its colored version. But more importantly, it can generate realistic pictures from rough sketches (can be seen in the purse example) or from painting-like images (can be seen in the street and facade examples below).

![Examples](https://huggingface.co/datasets/huggingfacejs/tasks/resolve/main/image-to-image/pix2pix_examples.jpg)

## Useful Resources

- [Image-to-image guide with diffusers](https://huggingface.co/docs/diffusers/using-diffusers/img2img)
- [Train your ControlNet with diffusers 🧨](https://huggingface.co/blog/train-your-controlnet)
- [Ultra fast ControlNet with 🧨 Diffusers](https://huggingface.co/blog/controlnet)

## References

[1] P. Isola, J. -Y. Zhu, T. Zhou and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5967-5976, doi: 10.1109/CVPR.2017.632.

This page was made possible thanks to the efforts of [Paul Gafton](https://github.com/Paul92) and [Osman Alenbey](https://huggingface.co/osman93).