[
    {
        "id": "osp-21145",
        "type": "article-journal",
        "title": "Embedding Prediction Helps Image Generation",
        "author": [
            {
                "family": "Xu",
                "given": "Sihan"
            },
            {
                "family": "Xie",
                "given": "Ji"
            },
            {
                "family": "Wang",
                "given": "Zilin"
            },
            {
                "family": "Shen",
                "given": "Hui"
            },
            {
                "family": "Yu",
                "given": "Stella X."
            }
        ],
        "URL": "https://omanscience.com/ar/articles/embedding-prediction-helps-image-generation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    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 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."
    }
]