The 10 Best AI Image Generators In 2023

An AI image generator is a computer program designed to create visual content. Generative adversarial networks, variational autoencoders, style transfer, and text-to-image production are only few of the AI-based methods for creating graphics.

Neural networks, a type of machine learning model motivated by the brain's structure and function, are commonly used by these systems to figure out how to generate images.

Some can generate images from a textual description, while others can copy the aesthetic from one image and apply it to another.

What Is an AI Image Generator?

AI image generators are computer programs that use artificial intelligence to generate new images. These images can be created from scratch, or they can be based on existing images or other inputs.

AI image generators can be used for a variety of purposes, such as creating original artwork, enhancing photographs, or generating realistic images for use in computer graphics or machine learning applications.

There are several types of AI image generators, including, VAEs or variational autoencoders, autoregressive models and GANs or generative adversarial networks. These models use different techniques to generate images, such as learning from a dataset of real images, using random noise as input, or combining elements from multiple sources.

AI image generators can produce a wide range of images, including photorealistic images, abstract artwork, and stylized images. They are used in fields such as computer graphics, art, and machine learning, and have the potential to revolutionize the way we create and manipulate images.

Top AI Image Generators

Several famous AI image generators have garnered significant attention in recent years. These include:

1. GANs (Generative Adversarial Networks)

These are network architectures neural that consist of two networks: a discriminator and a generator. The GANs generator creates new images, while its discriminator evaluates the authenticity of the images. GANs can generate highly realistic images and have been used to create synthetic images of people, animals, and landscapes.

Advantages

• GANs can generate high-quality images that are difficult to distinguish from real ones.
• They can generate a wide range of images and can be trained on specific categories (e.g. faces, animals, etc.)

Disadvantages

• GANs can be difficult to train and require a lot of computational power.
• The generated images may contain artifacts or distortions.
• GANs may generate images that are not representative of the training data, leading to biases in the generated images.

2. DALL-E

This is an AI image generator developed by OpenAI that uses a transformer-based language model to generate images from text descriptions. DALL-E is can generate a wide variety of images, which includes objects, animals, and scenes, based on a given text description.

Advantages

• DALL-E can generate a wide range of images based on a text description. Including images that are not present in the training data.

Disadvantages

• The generated images may not always be high quality or visually appealing.
• DALL-E may generate images that are not representative of the text description, leading to misunderstandings or misinterpretations of the input text.

3. BigGAN

This is a large-scale GAN developed by DeepMind that is capable of generating high-resolution images. BigGAN has been used to generate images of objects, animals, and landscapes, and has been able to generate images with high levels of detail and realism.

Advantages

• BigGAN can generate high-resolution images with high levels of detail and realism. 

Disadvantages

• BigGAN requires a lot of computational power and may be difficult to train. The generated images may contain artifacts or distortions. 

4. pix2pix

This is an image-to-image translation neural network developed by researchers at UC Berkeley.

It can be used to convert images from one domain to another, such as converting sketches to photographs or converting photographs to stylized images.

Advantages

• pix2pix can generate high-quality images that are difficult to distinguish from real ones.
• It can be trained on specific domains to perform specific image-to-image translations.

Disadvantages

• pix2pix requires a large amount of training data to generate accurate results.
• It may at times generate images that are not representative of the input data, leading to biases in the generated images.

5. StyleGAN

This is a GAN developed by NVIDIA that can generate high-quality images of human faces with a high level of detail and realism. StyleGAN has been used to generate synthetic celebrity faces and has been able to generate images that are difficult to distinguish from real ones.

Advantages

• It can generate a wide range of images of human faces and can be trained on specific categories (e.g. different age groups, ethnicities, etc.)

Disadvantages

• StyleGAN requires a lot of computational power and may be difficult to train.
• The generated images may contain artifacts or distortions.
• StyleGAN may generate images that are not representative of the training data, leading to biases in the generated images. 

6. Deep Dream

This is an AI image generator developed by Google that uses a convolutional neural network (CNN) to generate images based on a given input image. Deep Dream can generate images with a dream-like or surreal quality by modifying the features detected by CNN.

Advantages

• Deep Dream can generate unique and surreal images.
• It is relatively easy to use and requires minimal training data.

Disadvantages

• The generated images may not always be of high quality or visually appealing.
• It's possible that Deep Dream will produce visuals that don't accurately depict the supplied data, leading to incorrect conclusions being drawn.

7. Neural style transfer

This is a technique that uses convolutional neural networks (CNN) to transmit the style of an image to another image, resulting in a new image with a hybrid of the two styles. Neural style transfer is used to create a wide variety of images, including stylized portraits and landscapes.

Advantages

• Neural style transfer can generate unique and visually appealing images.
• It is relatively easy to use and requires minimal training data.

Disadvantages

• The generated images may not always be of high quality or visually appealing.
• Neural style transfer may not always accurately capture the style of the input images, leading to misunderstandings or misinterpretations of the input images.

8. CycleGAN

This is a GAN developed by researchers at Stanford University that can perform image-to-image translation without the need for paired training data. CycleGAN has been used to perform a wide range of image-to-image translations, such as converting photographs to paintings or converting horses to zebras.

Advantages

• CycleGAN is able to perform image-to-image translation without the need for paired training data.
• It can be trained on specific domains to perform specific image-to-image translations.

Disadvantages

• CycleGAN may generate images that are not of high quality or visually appealing.
• CycleGAN may require a large amount of training data to generate accurate results. 

9. FaceApp

This is an AI image generator developed by a Russian company that can apply a variety of filters and transformations to images of human faces. FaceApp has gained popularity for its ability to generate images of people at different ages or with different hairstyles, makeup, or facial expressions.

Advantages

• FaceApp can generate a wide range of images of human faces with different filters and transformations.
• It is relatively easy to use and requires minimal training data.

Disadvantages

• FaceApp may generate images that are not of high quality or visually appealing.
• FaceApp has faced controversy over its data privacy practices.

10. Prisma

This is an AI image generator developed by a Russian company that can apply a variety of filters and transformations to images. Prisma has gained popularity for its ability to generate stylized images of landscapes, animals, and other objects.

Advantages

• Prisma can generate a wide range of stylized images.
• It is relatively easy to use and requires minimal training data. 

Disadvantages

• Prisma has faced controversy over its data privacy practices.

Conclusion

AI image generators are powerful tools that can generate a wide range of images, but they also have their limitations.

It is important to carefully consider the pros and cons of each AI image generator and to use them appropriately to ensure that the generated images are of high quality and accurately represent the desired content.