New Advancements in Stable Diffusion: Single-Step Image Generation Model Achieves Fast High-Quality Output

Recently, researchers at the Massachusetts Institute of Technology (MIT) have announced a significant breakthrough – the Single-Step Diffusion Model (DMD), which can generate high-quality images 30 times faster than traditional methods. The core of this new model lies in using two diffusion models as guides, reducing the distribution gap between generated images and real images, thus achieving efficient training for single-step generation.

The DMD model not only improves image generation speed but also performs remarkably well in a wide range of benchmark tests, achieving Fréchet inception distance (FID) scores very close to complex models in specific category image generation tests on ImageNet (only 0.3). Although there are still slight quality differences in some more complex text-to-image applications, its performance in industrial-scale text-to-image generation is remarkable.

Stable Diffusion, as an open-source text-to-image model, has attracted attention for its fast and high-quality generation capabilities. The design of this model allows ordinary consumers to run it at home using GPUs, making it easy for everyone to create stunning artworks. Stable Diffusion not only excels in generating images but can also be used for