arXiv：cs.AI· Desen Sun, Jason Hon, Jintao Zhang, Sihang Liu·· 5 小时前
HybridStitch：面向高效扩散生成的 Token 自适应模型拼接
HybridStitch: Token-Adaptive Model Stitching for Efficient Diffusion Generation
AI 导读
HybridStitch 是一个无需训练的框架，通过 token 级反馈为每个更新选择由哪个模型提供，从而按 token 位置分配模型容量。它在四个主要基准上实现 1.63–1.68 倍图像生成加速和 1.15–1.58 倍 dLLM 吞吐提升，dLLM 解码查询开销估算下降 50–66%，生成质量相当。
正文
Abstract:Iterative diffusion generation requires different amounts of model capacity at different token positions. Prior model combinations allocate capacity by denoising step or by drafting and verification role. We present HybridStitch, a training-free framework that uses token-level feedback to select the model supplying each adopted update. Continuous image refinement and discrete language commitment operate on a shared generation state, while model-specific KV buffers provide attention context for selected queries. HybridStitch achieves 1.63--1.68$\times$ faster image generation and 1.15--1.58$\times$ diffusion large language model (dLLM) throughput on four main benchmarks. Estimated dLLM decode query cost decreases by 50--66% with comparable generation quality.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.07815 [cs.CV] |
| (or arXiv:2603.07815v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.07815 arXiv-issued DOI via DataCite |
Submission history
From: Desen Sun [view email]
[v1]
Sun, 8 Mar 2026 21:34:50 UTC (2,851 KB)
[v2]
Tue, 13 Oct 2026 17:09:25 UTC (263 KB)
[v3]
Wed, 14 Oct 2026 04:33:47 UTC (263 KB)
来源：arXiv：cs.AI · arxiv.org