不重训模型，仅换骨干网络就让身份保真生成快5.9倍。
When Few Steps Are Enough: Training-Free Acceleration of Identity-Preserved Generation
- 用冻结的InfuseNet适配器替换骨干网络，禁用分类器引导。
- 4-8步内完成身份特征保留，延迟降5.9倍，身份相似度提升0.028。
- 适合追求高效高保真的个性化图像生成应用。
身份保真图像生成通常依赖多步扩散模型，导致部署成本高。我们发现，对于基于FLUX的身份条件生成，这种开销往往可省。一个通过开发集迁移训练的冻结InfuseNet身份适配器，可直接迁移到简化版schnell骨干网络而无需重训。仅需两行代码修改——更换骨干路径并关闭分类器自由引导——即可实现5.9倍延迟降低，同时在ArcFace身份相似度上提升+0.028，在LPIPS上降低-0.016，优于标准28步基线。分析去噪轨迹发现，身份保真在早期（4-8步）即进入有效阶段，后续步骤主要优化视觉细节、锐度与对比度。适配器消融实验证实身份形成依赖身份适配器；注意力流归一化探针显示，条件贡献随采样过程递减。对SDXL和SD1.5的初步风格与物体适配器测试也显示中间步骤后收益递减。结果表明，简化骨干网络替换是一种简单、免训练的高效-保真权衡策略。
原文摘要 · Abstract (English)
Identity-preserved image generation is typically built on many-step diffusion backbones, making personalized generation expensive at deployment time. We show that this cost is often unnecessary for identity-conditioned FLUX generation. A frozen InfuseNet identity adapter trained with dev transfers directly to the distilled schnell backbone without retraining. This two-line replacement -- changing the backbone path and disabling classifier-free guidance -- reduces latency by 5.9x while improving ArcFace identity similarity by +0.028 and lpips by -0.016 over the standard 28-step dev baseline. To explain why this works, we analyze the denoising trajectory and find that identity fidelity enters an early effective regime, often within 4-8 steps, while later steps primarily refine visual detail, sharpness, and contrast. Adapter ablations confirm that identity formation depends on the identity adapter, while attention-stream norm probes suggest that the relative conditioning contribution decreases as sampling proceeds. Preliminary style-adapter and object-adapter sweeps on SDXL and SD1.5 show similar diminishing returns after intermediate steps. These results position distilled backbone replacement as a simple, training-free strategy for improving the efficiency-fidelity tradeoff of identity-preserved generation.
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