Papers › Generative Modeling with Explicit Memory

Generative Modeling with Explicit Memory

11 Dec 2024arXiv:2412.08781archive 2025-07-28

Yi Tang, Peng Sun, Zhenglin Cheng, Tao Lin

Recent studies indicate that the denoising process in deep generative diffusion models implicitly learns and memorizes semantic information from the data distribution. These findings suggest that capturing more complex data distributions requires larger neural networks, leading to a substantial increase in computational demands, which in turn become the primary bottleneck in both training and inference of diffusion models. To this end, we introduce \textbf{G}enerative \textbf{M}odeling with \textbf{E}xplicit \textbf{M}emory (GMem), leveraging an external memory bank in both training and sampling phases of diffusion models. This approach preserves semantic information from data distributions, reducing reliance on neural network capacity for learning and generalizing across diverse datasets. The results are significant: our GMem enhances both training, sampling efficiency, and generation quality. For instance, on ImageNet at 256 ×256 resolution, GMem accelerates SiT training by over 46.7×, achieving the performance of a SiT model trained for $7M$ steps in fewer than $150K$ steps. Compared to the most efficient existing method, REPA, GMem still offers a 16× speedup, attaining an FID score of 5.75 within $250K$ steps, whereas REPA requires over $4M$ steps. Additionally, our method achieves state-of-the-art generation quality, with an FID score of {3.56} without classifier-free guidance on ImageNet 256×256. Our code is available at \url{https://github.com/LINs-lab/GMem}.

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