扩散模型研究者终于找到了训练瓶颈的隐藏位置——残差连接，8.75倍加速意味着更低的训练成本，做图像生成的团队值得关注这个新路由方案。
研究发现图像扩散Transformer训练效率低下的根源在于残差连接，而非注意力或编码器。残差连接导致信号膨胀、梯度消失和特征冗余，尤其不适合扩散模型这种多步去噪任务。作者提出扩散自适应路由（Diffusion-Adaptive Routing），让每层根据去噪时间步动态选择前层输出，从而在相同图像质量下减少8.75倍训练迭代。该工作没有引入新数据集或注意力机制，而是质疑了从语言Transformer继承的残差结构。
Image diffusion Transformers train poorly because …
Image diffusion Transformers train poorly because their layers pass information in a fixed, outdated way.
Now they can train much faster by changing how layers share information.
With this paper, the same image quality arrived with 8.75x fewer training iterations.
The surprise is not that Diffusion Transformers had an inefficiency, but where it was hiding.
Researchers have spent years refining attention, conditioning, tokenization, objectives, and autoencoders, while leaving the residual stream mostly untouched because it looked like plumbing rather than intelligence.
In a standard residual stack, every layer keeps adding its output to the running stream, which sounds harmless until the stream’s magnitude swells, gradients fade backward, and neighboring blocks begin saying nearly the same thing.
That is bad for any Transformer, but it is especially awkward for diffusion, because denoising is not one fixed task repeated at every step.
The authors found 3 signs that this old setup hurts the model: signals get too large going forward, learning signals fade going backward, and nearby blocks often produce almost the same features.
Their fix is Diffusion-Adaptive Routing, a replacement that lets each layer choose which earlier layer outputs to use, and the choice changes with the denoising timestep.
The big deal is that the paper does not add a new image dataset, loss, tokenizer, or attention trick, but instead questions the old residual connection that most models kept copying from language Transformers. ----
Link – arxiv. org/abs/2605.20708
Title: "Rethinking Cross-Layer Information Routing in Diffusion Transformers"