DeepFloyd IF

DeepFloyd IF is an open-source text to image generation model developed by the DeepFloyd research team under StabilityAI. It is a modular neural network based on cascading methods.
IF is constructed by multiple neural modules (independent neural networks that handle specific tasks), which are combined within a single architecture to produce synergistic effects.
IF generates high-resolution images in a cascading manner: starting from the base model that generates low resolution samples, and then being enhanced by a series of upgraded models to create stunning high-resolution images.
The basic and super-resolution models of IF adopt diffusion models, which use Markov chain steps to introduce random noise into the data, and then reverse the process to generate new data samples from the noise.
IF operates within pixel space, rather than relying on latent diffusion represented by latent images (such as stable diffusion).