DeepFloyd IF is an open-source text-to-image generation model developed by the DeepFloyd research team under Stability AI. IF is a modular neural network based on a cascading approach. It consists of multiple neural modules that are independent neural networks designed to perform specific tasks and are assembled within a single architecture to create a synergy.
IF generates high-resolution images by cascading up from a low-resolution foundation model, then improving it step by step through a series of upscaling models to create stunning high-resolution images. The foundation and super-resolution models of IF use a diffusion model that introduces random noise into the data through a Markov chain stepping process, and then reverse the process to generate new data samples from the noise.
IF operates in the pixel space rather than relying on latent image representation diffusion (such as stable diffusion, which spreads the image in the latent space).
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