SANA-Streaming: Real-time Streaming Video Editing with Hybrid Diffusion Transformer
SANA-Streaming is a system-algorithm co-designed framework for high-resolution real-time streaming video editing on consumer GPUs. It features a Hybrid Diffusion Transformer, Cycle-Reverse Regularization, and efficient system co-design, achieving 24 FPS at 1280x704 on an RTX 5090. Experiments show significant improvements in temporal coherence and throughput over state-of-the-art.
[2605.30409] SANA-Streaming: Real-time Streaming Video Editing with Hybrid Diffusion Transformer
[Submitted on 28 May 2026]
Title:SANA-Streaming: Real-time Streaming Video Editing with Hybrid Diffusion Transformer
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Abstract:Real-time streaming video-to-video editing (V2V) is critical for interactive applications such as live broadcasting and gaming, yet it remains a formidable challenge due to the stringent requirements for temporal consistency and inference throughput. In this paper, we present SANA-Streaming, a system-algorithm co-designed framework for high-resolution, real-time streaming video editing on consumer GPUs, with the following three core designs: (1) Hybrid Diffusion Transformer architecture introduces softmax attention in part of the blocks to improve local modeling capabilities while preserving the efficiency of linear layers. (2) Cycle-Reverse Regularization is a novel training strategy that enforces semantic consistency by predicting source frames from generated content via flow matching, improving temporal consistency without requiring paired long edited videos. (3) Efficient System Co-design combines fused GDN kernels and Mixed-Precision Quantization (MPQ) optimized for the NVIDIA Blackwell (RTX 5090) architecture. By profiling real-world throughput, our MPQ maximizes Tensor Core utilization while maintaining generation quality. The resulting system achieves real-time 1280 x 704 resolution editing at 24 end-to-end FPS on a single RTX 5090 GPU, with the DiT core running at 58 FPS. Experimental results demonstrate that our co-design approach significantly outperforms existing SOTA methods in both temporal coherence and system throughput.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.30409 [cs.CV]
(or arXiv:2605.30409v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.30409
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuyang Zhao [view email] [v1] Thu, 28 May 2026 17:59:17 UTC (3,861 KB)
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