Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion
Persistence Forcing uses specialized diffusion features to reach FID 1.63 on ImageNet 256.
Pixel-space diffusion Transformers typically refine features uniformly, despite images needing compact global structure and richer local detail. Heterogeneous refinement produces persistent features for global structure and active features for high-frequency detail. Persistence Forcing lets persistent features condition active ones and adds guidance that complements classifier-free guidance. On ImageNet, PerF-L reaches FID 1.91 versus JiT-H's 1.86 with half the parameters, and PerF-H reaches 1.63 at 256×256 and 1.76 at 512×512.
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