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Last month, along with a comprehensive suite of new AI tools and innovations, Google DeepMind unveiled Gemini Diffusion. This experimental research model uses a diffusion-based approach to generate text. Traditionally, large language models (LLMs) like GPT and Gemini itself have relied on autoregression, a step-by-step approach where each word is generated based on the previous one. Diffusion language models (DLMs), also known as diffusion-based large language models (dLLMs), leverage a method more commonly seen in image generation, starting with random noise and gradually refining it into a coherent output. This approach dramatically increases generation speed and can improve coherency and consistency.
Gemini Diffusion is currently available as an experimental demo; sign up for the waitlist here to get access.
(Editor’s note: We’ll be unpacking paradigm shifts like diffusion-based language models—and what it takes to run them in production—at VB Transform, June 24–25 in San Francisco, alongside Google DeepMind, LinkedIn and other enterprise AI leaders.)
Understanding diffusion vs. autoregression
Diffusion and autoregression are fundamentally different approaches. The autoregressive approach generates text sequentially, with tokens predicted one at a time. While this method ensures strong coherence and context tracking, it can be computationally intensive and slow, especially for long-form content.
Diffusion models, by contrast, begin with random noise, which is gradually denoised into a coherent output. When applied to language, the technique has several advantages. Blocks of text can be processed in parallel, potentially producing entire segments or sentences at a much higher rate.
Gemini Diffusion can reportedly generate 1,000-2,000 tokens per second. In contrast, Gemini 2.5 Flash has an average output speed of 272.4 tokens per second. Additionally, mistakes in generation can be corrected during the refining process, improving accuracy and reducing the number of hallucinations. There may be trade-offs in terms of fine-grained accuracy and token-level control; however, the increase in speed will be a game-changer for numerous applications.
How does diffusion-based text generation work?
During training, DLMs work by gradually corrupting a sentence with noise over many steps, until the original sentence is rendered entirely unrecognizable. The model is then trained to reverse this process, step by step, reconstructing the original sentence from increasingly noisy versions. Through the iterative refinement, it learns to model the entire distribution of plausible sentences in the training data.
While the specifics of Gemini Diffusion have not yet been disclosed, the typical training methodology for a diffusion model involves these key stages:
Forward diffusion: With each sample in the training dataset, noise is added progressively …