Image-to-Image

Use Cases

Style transfer

One of the most popular use cases of image to image is the style transfer. Style transfer models can convert a regular 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, 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.

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 Model Database.js to infer image-to-image models on Model Database Hub.

import { HfInference } from "@Model Database/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 outputs of diffusion models only with a text prompt is a challenging problem. ControlNet is a neural network type that provides an image based control to diffusion models. These controls can be edges or landmarks in an 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.

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 where the associated paper and the GitHub repository can be found.

Below images show some of the examples shared in the paper that can be obtained using Pix2Pix. There are various cases this model can be applied on. 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).

Useful Resources

• Train your ControlNet with diffusers 🧨
• Ultra fast ControlNet with 🧨 Diffusers

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 and Osman Alenbey.