Can autoregressive models replace diffusion models?

To be honest, when I first heard that NextStep-1, a pure autoregressive model, made a breakthrough in image generation, I was a bit skeptical. After all, in the world of AI image generation, diffusion models have been sitting at the top for several years, from DALL-E to Stable Diffusion, they are like a group of skillful painters, through repeated "painting-correction" way to create amazing works. What about autoregressive models? For a long time, it is like an apprentice who needs all kinds of "external assistance" to complete the painting, which always makes people feel that it is a bit less independent.

The "Declaration of Independence" of Autoregressive Modeling

NextStep-1 has really made a difference. It uses a 14B-parameter Transformer backbone plus a lightweight stream matching head to achieve end-to-end image generation. The design idea is quite clever - let the Transformer be responsible for "thinking" and "creative planning", while the stream matching head is like a faithful executor, turning ideas into into concrete pixels. Interestingly, the team found that even increasing the parameters of the stream matching head from 157M to 528M had little effect on the final image quality. The Transformer is the real "artist" and the stream matching head is just the "paintbrush" in its hands.

Even more surprising was the counter-intuitive finding that adding noise regularization to the training of the Tokenizer improved the image quality. It's like saying that sometimes adding some "distractions" and "uncertainty" to the creative process can inspire better artistic expression. This idea contrasts with the diffusion model's philosophy of exact denoising.

Performance performance: more than just on paper

In terms of actual review data, NextStep-1 scored 0.73 on the GenEval benchmark and 85.28 on the DPG-Bench, which are really impressive numbers. More importantly, however, is its performance on image editing tasks - being able to understand everyday commands such as "change the office background to a sunset on the beach" and still maintain visual consistency before and after editing. This ability is unimaginable in previous autoregressive models.

However, we also have to be sober to see its limitations. That 47.6ms single token generation latency, the convergence efficiency problem in high-resolution image generation, and the dependence of supervised fine-tuning on large-scale data are all real challenges. In particular, the speed bottleneck caused by sequential decoding may be a hard problem in application scenarios that require real-time generation.

Philosophical Reflections on Technical Routes

From a deeper perspective, the dispute between autoregressive model and diffusion model actually reflects two different technical philosophies. Diffusion model is like an artisan who carves and crafts, perfecting the work through multiple iterations; while autoregressive model is more like a painter who writes with a brush and splashes ink, completing the creation in a single stroke. Which way is better? I think the question itself may be problematic.

As the Step Star team said, NextStep-1″ is not to replace the diffusion model, but to provide a new possible path for image generation". Advancements in technology are often not simply substituting relationships, but providing more options. In certain scenarios that require fast generation and interactive editing, the autoregressive model may be more advantageous; while in the pursuit of extreme image quality and the need for fine control, the diffusion model may still be a better choice.

I personally feel that the future is more likely to be the fusion of the two technologies and complementary. After all, artistic creation is inherently diverse, some works need to be carefully crafted, and some need to be completed in one fell swoop.AI image generation technology should be the same, different methods have their own merits, the key is to find the most suitable for the specific application scenarios of the solution.

It is truly exciting to see innovations like NextStep-1. It reminds us that while chasing more complex models, we should not neglect to rethink the underlying paradigm. Sometimes the simplest architecture can lead to the most unexpected breakthroughs instead.