Morteza Sadat

Excited to present two papers at #ICLR2025 in Singapore 🇸🇬! If you are attending, please drop by our posters to talk about diffusion models 🔥 Details in the thread 🧵👇

NeurIPS releasing reviews one by one.

Excited to share minHiWave, my reimplementation of the HiWave paper! Clean code, minimal setup, and ready to run. Check it out: github.com/laihongphuc/mi…

[LG] Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales S Sadat, T Vontobel, F Salehi, R M. Weber [ETH Zürich & DisneyResearch|Studios] (2025) arxiv.org/abs/2506.19713

Preprint of today: Sadat et al., "Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales" -- arxiv.org/abs/2506.19713 Classifier Free Guidance affects generation differently at different frequencies -- set it to low for low frequency, and high for high!

Preprint of today: Vontobel et al., "HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling" -- arxiv.org/abs/2506.20452 This one also uses wavelets and frequency-dependent guidance. Generate->Invert->Add high-freq details for each part.

[1/7]⚡️Check out our recent work — "Token Perturbation Guidance for Diffusion Models" A simple yet effective method based on token shuffling for extending the benefits of CFG to broader settings, including unconditional generation. arXiv: arxiv.org/abs/2506.10036

Thanks, AK, for sharing our work! Also, feel free to check out our related paper, where we explore how frequency analysis can generally enhance image generation in diffusion models: x.com/Msadat97/statu…