Paper Detail
Sihan Xu, Ji Xie, Zilin Wang, Hui Shen, Stella X. Yu
In diffusion transformers, a class label or a text prompt is embedded once, and the same condition is reused at every denoising step. We ask whether predicted embeddings can serve as this condition instead. Next-Embedding Predictive Autoregression (NEPA) trains a Transformer to predict the next continuous embedding in a sequence. In generation, the clean image follows the noisy image, so its embeddings are the next embeddings after the condition and the noisy image. We train a NEPA model to predict them all at once with Multi-Embedding Prediction, and in Embedding Conditioned Generation, a DiT generator is conditioned on these predictions, recomputed at every denoising step, so the conditioning signal adapts to the current noisy state. Experiments on class-conditional ImageNet $256\times256$ study the condition of the generator, the design of Multi-Embedding Prediction, and the scaling of both models. The NEPA model adds a second network to every sampling step; with it, and combined with REPA, our final model, NEPA-DiT-XL, reaches an FID of 1.32 using about a third of the training compute of REPA.
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@article{xu2026embedding,
title = {Embedding Prediction Helps Image Generation},
author = {Sihan Xu and Ji Xie and Zilin Wang and Hui Shen and Stella X. Yu},
year = {2026},
abstract = {In diffusion transformers, a class label or a text prompt is embedded once, and the same condition is reused at every denoising step. We ask whether predicted embeddings can serve as this condition instead. Next-Embedding Predictive Autoregression (NEPA) trains a Transformer to predict the next continuous embedding in a sequence. In generation, the clean image follows the noisy image, so its embeddings are the next embeddings after the condition and the noisy image. We train a NEPA model to pred},
url = {https://arxiv.org/abs/2610.02203},
keywords = {cs.CV, cs.LG, Embedding, Computer science, Autoregressive model, Artificial intelligence, Transformer},
eprint = {2610.02203},
archiveprefix = {arXiv},
}
{}