There are several AI image generators available, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These models are trained on large datasets of images and can generate new images that are similar to the ones in the training set. Some popular AI image generation models include StyleGAN, BigGAN, and DALL-E. These models can be used for a variety of applications, such as creating realistic images for video games, generating new images for art and design, and creating synthetic data for machine learning models. This list of ten AI image generators with description, pros and cons have been curated by Workalibur with chatGPT assistance.
StyleGAN – AI image generator
Developed by NVIDIA, StyleGAN is a GAN model that can generate high-resolution images of faces, animals, and other objects. Features include the ability to control certain aspects of the generated image, such as facial expression and hair style. Prompt examples include generating a photo-realistic portrait of a specific person or creating an original character for a video game. The code for StyleGAN is available on GitHub and is open source, but the training data is not publicly available. Pros include the high-quality of the generated images, and cons include the need for large amounts of computational resources to train the model.
BigGAN – AI image generator
BigGAN is another GAN model developed by NVIDIA that can generate high-resolution images. It is trained on a large dataset of images and can generate images of a wide range of objects and scenes. Features include the ability to control the resolution and class of the generated image. Prompt examples include generating an image of a specific object such as a car, a landscape or an animal. The code for BigGAN is available on GitHub and is open source, but the training data is not publicly available. Pros include the high-quality of the generated images, and cons include the need for large amounts of computational resources to train the model.
DALL-E – AI image generator
Developed by OpenAI, DALL-E is a GPT-based model that can generate images from text prompts. Features include the ability to generate images of specific objects, scenes, and animals based on a text description. Prompt examples include generating an image of a «two-story pink house with a white fence» or «a cat wearing a bowtie». DALL-E is open source and the code is available on GitHub. Pros include the ability to generate images from text descriptions, and cons include the model’s high computational requirements.
CycleGAN – AI image generator
Developed by Berkeley AI Research, CycleGAN is a model that can perform image-to-image translation, such as converting a photo of a horse to a zebra or a painting to a photograph. Features include the ability to train on any set of images, making it a versatile model for various image-to-image translation tasks. Prompt examples include converting a black and white image to color, or a day-time image to night-time. The code for CycleGAN is available on GitHub and is open source. Pros include the ability to perform a wide range of image-to-image translations, and cons include the need for large amounts of training data.
Pix2pix – AI image generator
Developed by the Berkeley Vision and Learning Center, Pix2pix is a GAN-based model that can perform image-to-image translation. Features include the ability to generate high-quality images and the ability to train on any set of images. Prompt examples include converting a sketch to a painting, or a label map to a photograph. The code for Pix2pix is available on GitHub and is open source. Pros include the ability to perform a wide range of image-to-image translations and cons include the need for large amounts of training data.
DeepDream – AI image generator
Developed by Google, DeepDream is a model that can generate abstract, dream-like images from a given input image. Features include the ability to generate a wide range of abstract patterns and shapes. Prompt examples include generating an abstract representation of a specific object or scene. The code for DeepDream is available on GitHub and is open source. Pros include the ability to generate unique and interesting images, and cons include the lack of control over the specific features of the generated image.
ProGAN – AI image generator
Developed by NVIDIA, ProGAN is a GAN-based model that can generate high-resolution images of faces, animals, and other objects. Features include the ability to generate images at different resolutions and the ability to control certain aspects of the generated image, such as facial expression and hair style. Prompt examples include generating a photo-realistic portrait of a specific person or creating an original character for a video game. ProGAN is open source and the code is available on GitHub, but the training data is not publicly available. Pros include the high-quality of the generated images, and cons include the need for large amounts of computational resources to train the model.
pix2pixHD – AI image generator
Developed by NVIDIA, pix2pixHD is a GAN-based model that can perform image-to-image translation at high resolution. Features include the ability to generate high-quality images and the ability to train on any set of images. Prompt examples include converting a sketch to a painting, or a label map to a photograph. The code for pix2pixHD is available on GitHub and is open source. Pros include the ability to perform high-resolution image-to-image translations, and cons include the need for large amounts of training data.
SPADE – AI image generator
Developed by NVIDIA, SPADE is a model that can generate images with fine-grained control over the style and content. Features include the ability to control the style and content of the generated image and the ability to generate images at high resolutions. Prompt examples include generating an image of a specific object with a specific style, such as a car in the style of a painting. The code for SPADE is available on GitHub and is open source, but the training data is not publicly available. Pros include the ability to generate high-quality images with fine-grained control, and cons include the need for large amounts of computational resources to train the model.
