Hugging Face GitHub Launch Blog Documentation License : Apache 2.0 Authors : Google DeepMind DiffusionGemma is a generative model built by Google DeepMind. Based on the 26B A4B Mixture of Experts (MoE) Gemma 4 architecture, DiffusionGemma generates tokens using discrete diffusion. This open weights model is multimodal, handling text, image, and video inputs to generate text output. Built on a MoE foundation, DiffusionGemma is designed to improve generation speed (tokens per second) while remaining deployable across various hardware environments. DiffusionGemma builds upon the architectural and capability advancements of Gemma 4, introducing several core features: Discrete Text Diffusion – Shifts from token by token autoregression to block autoregressive multi canvas sampling. It generates text by iteratively denoising blocks of tokens (a 'canvas') in parallel, significantly increasing decoding speed. Multimodal Input Processing – Processes interleaved text, image (with variable aspect ratio and resolution support), and video inputs to generate text outputs. Encoder Decoder Architecture – Utilizes an autoregressive encoder to process and cache the prompt context, paired with a decod…
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