GMem: A Modular Approach for Ultra-Efficient Generative Models
This addresses efficiency issues in generative modeling for AI researchers, offering a novel method to reduce computational demands.
The paper tackles the computational bottleneck in diffusion models by introducing GMem, a modular approach that decouples memory capacity from the model, achieving a 50x training speedup on ImageNet 256x256 with FID=7.66 in under 28 hours and state-of-the-art FID=1.53 in 160 hours.
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 GMem: A Modular Approach for Ultra-Efficient Generative Models. Our approach GMem decouples the memory capacity from model and implements it as a separate, immutable memory set that preserves the essential semantic information in the data. The results are significant: GMem enhances both training, sampling efficiency, and diversity generation. This design on one hand reduces the reliance on network for memorize complex data distribution and thus enhancing both training and sampling efficiency. On ImageNet at $256 \times 256$ resolution, GMem achieves a $50\times$ training speedup compared to SiT, reaching FID $=7.66$ in fewer than $28$ epochs ($\sim 4$ hours training time), while SiT requires $1400$ epochs. Without classifier-free guidance, GMem achieves state-of-the-art (SoTA) performance FID $=1.53$ in $160$ epochs with only $\sim 20$ hours of training, outperforming LightningDiT which requires $800$ epochs and $\sim 95$ hours to attain FID $=2.17$.