OpenAI recently released new research showing major speed improvements in continuous-time consistency models. These models can now generate high-quality images 50 times faster than previous approaches.
What makes this development important? Traditional diffusion models need many steps to create an image, similar to slowly developing a photograph. This new approach, called simplified continuous-time consistency models (sCM), can produce equally good results in just one or two steps.
The improvements come from three main changes:
1. A simpler mathematical foundation that combines existing approaches in a more efficient way
2. Better training stability through improved network design and progressive training schedules
3. Successfully scaling up to massive models with 1.5 billion parameters while maintaining speed
The results are impressive – these models can create a high-quality image in just 0.11 seconds on an A100 GPU. They achieve this while matching or beating the sample quality of slower approaches across multiple standard benchmarks.
Perhaps most importantly, the computational cost drops to less than 10% of what traditional models require. This makes deployment much more practical for real applications.
This advancement builds on other recent improvements in AI image generation. We’ve seen similar pushes for efficiency in models like DALL-E 3 [Read more about DALL-E 3 here](https://adam.holter.com/dall-e-3-mid/) and the emergence of specialized video generators [covered in our Haiper 2.0 analysis](https://adam.holter.com/haiper-2-0-pushing-ai-video-generation-forward/).
The core takeaway is that image generation is becoming dramatically faster and more efficient while maintaining quality. This opens up new possibilities for real-time creative applications that weren’t previously feasible.
I’ll be testing these models as they become available and sharing concrete examples of how they perform in real-world scenarios. Subscribe to stay updated on hands-on testing and practical applications.
