You've probably heard of Stable Diffusion for image creation. Now imagine the same method applied to text or code generation.
No more token-by-token generation (autoregressive): the model produces everything en bloc, then "refines" the answer in several passes. Result:
- Speed multiplied by 10 (over 1,000 tokens/sec)
- Lower inference costs
- More global corrections (the model sees and reviews the whole answer)
Why is this a major leap?
- AI agents, which link tasks and reasoning, can work significantly faster and avoid long waits for generation
- Developers gain in productivity thanks to virtually instantaneous code generation, with easy corrections
- Companies could deploy these models on a larger scale, without blowing their budget
The secret?
It is inspired by the AI diffusion technique for images: we start with a textual "draft" and gradually refine it until we obtain a coherent answer. The model is no longer constrained by a strictly linear progression.
This could democratize as-yet unexplored use cases
The challenge The key is to ensure ethical alignment and impeccable quality, especially with such a "turbo". Speed shouldn't come at the expense of reliability.
A philosophical note We humans often write word after word, like GPT (autoregressive) models. Recent approaches, where AI takes the time to "think" before revealing a more complete answer, are reminiscent of the way we write a draft and then correct it. But diffusion goes even further: it considers the whole text (or code) as a whole, and refines it layer by layer. It's a fascinating mechanism, perhaps opening the way to other ways of structuring thought... and "thinking". Worth exploring!
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By Jérémy BRON, AI Director, Silamir Group
