FREE: Uncertainty-Aware Autoregression for Parallel Diffusion Transformers
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908675480748032 |
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| author | Wen, Xinwan Li, Bowen Luo, Jiajun Li, Ye Wang, Zhi |
| author_facet | Wen, Xinwan Li, Bowen Luo, Jiajun Li, Ye Wang, Zhi |
| contents | Diffusion Transformers (DiTs) achieve state-of-the-art generation quality but require long sequential denoising trajectories, leading to high inference latency. Recent speculative inference methods enable lossless parallel sampling in U-Net-based diffusion models via a drafter-verifier scheme, but their acceleration is limited on DiTs due to insufficient draft accuracy during verification. To address this limitation, we analyze the DiTs' feature dynamics and find the features of the final transformer layer (top-block) exhibit strong temporal consistency and rich semantic abstraction. Based on this insight, we propose FREE, a novel framework that employs a lightweight drafter to perform feature-level autoregression with parallel verification, guaranteeing lossless acceleration with theoretical and empirical support. Meanwhile, prediction variance (uncertainty) of DiTs naturally increases in later denoising steps, reducing acceptance rates under speculative sampling. To mitigate this effect, we further introduce an uncertainty-guided relaxation strategy, forming FREE (relax), which dynamically adjusts the acceptance probability in response to uncertainty levels. Experiments on ImageNet-$512^2$ show that FREE achieves up to $1.86 \times$ acceleration, and FREE (relax) further reaches $2.25 \times$ speedup while maintaining high perceptual and quantitative fidelity in generation quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FREE: Uncertainty-Aware Autoregression for Parallel Diffusion Transformers Wen, Xinwan Li, Bowen Luo, Jiajun Li, Ye Wang, Zhi Computer Vision and Pattern Recognition Diffusion Transformers (DiTs) achieve state-of-the-art generation quality but require long sequential denoising trajectories, leading to high inference latency. Recent speculative inference methods enable lossless parallel sampling in U-Net-based diffusion models via a drafter-verifier scheme, but their acceleration is limited on DiTs due to insufficient draft accuracy during verification. To address this limitation, we analyze the DiTs' feature dynamics and find the features of the final transformer layer (top-block) exhibit strong temporal consistency and rich semantic abstraction. Based on this insight, we propose FREE, a novel framework that employs a lightweight drafter to perform feature-level autoregression with parallel verification, guaranteeing lossless acceleration with theoretical and empirical support. Meanwhile, prediction variance (uncertainty) of DiTs naturally increases in later denoising steps, reducing acceptance rates under speculative sampling. To mitigate this effect, we further introduce an uncertainty-guided relaxation strategy, forming FREE (relax), which dynamically adjusts the acceptance probability in response to uncertainty levels. Experiments on ImageNet-$512^2$ show that FREE achieves up to $1.86 \times$ acceleration, and FREE (relax) further reaches $2.25 \times$ speedup while maintaining high perceptual and quantitative fidelity in generation quality. |
| title | FREE: Uncertainty-Aware Autoregression for Parallel Diffusion Transformers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.20390 |