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 🧵👇
🚀🚀 I will present our latest work " HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models" next week at ICLR 2026 🇧🇷 🎉 If you’re attending, I’d love to connect and chat!
[1/8]🚨📢Introducing "HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models"; A tweak to diffusion sampling that makes images sharper, more coherent, and more realistic, especially with fewer steps or low guidance.
[8/8] Please check out the paper for more details and experiments, as well as the PyTorch pseudocode of the method.:
https://t.co/Ym2lBe9LGa
I'd like to thank all my colleagues who made this project possible.
[1/8]🚨📢Introducing "HiGS: History-Guided Sampling for Plug-and-Play Enhancement of Diffusion Models"; A tweak to diffusion sampling that makes images sharper, more coherent, and more realistic, especially with fewer steps or low guidance.
[7/8] Finally, we show that HiGS is compatible with a wide range of diffusion models and samplers (including distilled variants), and can be integrated into existing pipelines without retraining or additional sampling overhead.
@latifian_m It might be necessary given the scale of the conference. Almost 60/70 percent of reviewers haven't replied anything to the rebuttals among my papers (both as an author and as a reviewer)
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: https://t.co/m1P2eMjVFb