HybridStitch: Token-Adaptive Model Stitching for Efficient Diffusion Generation
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 dec...
| Authors | Desen Sun, Jason Hon, Jintao Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 March 2026 |
| Last updated at source | 14 October 2026 |
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