Text to image generators

the new model is built on something called Würstchen architecture which “combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models.

an interesting three-stage approach, setting new benchmarks for quality, flexibility, fine-tuning, and efficiency with a focus on further eliminating hardware barriers

Additionally, we are releasing training and inference code that can be found on the Stability GitHub page to allow further customization of the model and its outputs. The model is available for inference in the diffusers library

While Stable Diffusion uses a singular model, Stable Cascade is a pipeleine of three smaller models which are called Stages A, B, and C. Venture Beat notes that this modular architecture allows for efficiency and customization

The model will use diffusion transformer architecture rather than U-Net architecture

Stable Diffusion 3 will also utilize something called “flow matching”, a technique that generates images by transitioning random noise into a coherent image — focusing on the overall direction of the image.