Text-to-image models are machine learning models that can generate images from natural language descriptions. For example, if you give a text-to-image model a prompt like “a cat wearing a hat”, it will try to create an image that matches that description as closely as possible.
These models have become more advanced and realistic in recent years, thanks to the development of deep neural networks, large-scale datasets, and powerful computing resources. Ranking text-to-image models is not an easy task, as different models may have different strengths and weaknesses, such as image quality, diversity, resolution, speed, and creativity.
- DALL-E 2: An improved version of DALL-E, developed by OpenAI. It can create realistic images and art from a description in natural language. It can also combine concepts, attributes, and styles in various ways, such as creating anthropomorphic versions of animals and objects, rendering text, and applying transformations to existing images.
- Imagen: A text-to-image generation model that uses diffusion models and large transformer language models. Imagen is based on the research paper “Imagen: Text-to-Image Diffusion Models” by Google Research, Brain Team.
- Muse: A text-to-image generation model that uses masked generative transformers. Muse can create realistic and diverse images from natural language descriptions. It can also edit images in various ways, such as inpainting, outpainting, and mask-free editing.
- DreamBooth: Developed by researchers from Google Research and Boston University in 2022. It can take a small set of images of a specific subject use them to train a text-to-image model to generate more images of that subject based on natural language.
- Stable Diffusion: It is based on a kind of diffusion model called a latent diffusion model, which is trained to remove noise from images in an iterative process. It is one of the first text-to-image models that can run on consumer hardware and has its code and model weights publicly available.