🎉 We are thrilled to announce NextStep-1 has been selected as an ICLR 2026 Oral (Acceptance Rate: 1.18%)! 🏆
We are redefining Autoregressive Image Generation by effectively moving beyond Vector Quantization. NextStep-1 pairs a causal transformer with a lightweight Flow Matching head to predict continuous tokens directly. It is the first continuous autoregressive model to achieve SOTA performance comparable to diffusion models like Flux and SD3.5.
🔥 Code and blog for Nextstep-1.1 are coming soon! We are committed to open research to accelerate the future of AR generation.
Paper: https://t.co/CcEhZWIcSB
GitHub: https://t.co/DSEWk0olDs
HuggingFace: https://t.co/eRRk5GvNZN
quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
transformer — The neural network architecture behind modern AI models, built on attention — letting every word directly consider every other word in parallel.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
NextStep-1 predicts continuous image tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → with a flow matching head on a causal transformerThe neural network architecture behind modern AI models, built on attention — letting every word directly consider every other word in parallel.Full definition →, evidence that autoregressive generation can match diffusion models like Flux and SD3.5 without vector quantizationShrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.Full definition →.
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