arXiv Machine Learning July 10, 2026 By Abdullah Al Shafi, Sumaiya Rahim Suma

Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion

Read the original on arXiv Machine Learning →

arXiv:2607. 08241v1 Announce Type: cross Abstract: Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantization (PTQ) methods treat CFG models as single-branch networks, ignoring the paired conditional/unconditional structure that CFG inference fundamentally relies on.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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