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arXiv · 2410.19324

Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion

Emiel Hoogeboom·Thomas Mensink·Jonathan Heek·Kay Lamerigts·Ruiqi Gao·Tim Salimans
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Abstract

Latent diffusion models have become the popular choice for scaling up diffusion models for high resolution image synthesis. Compared to pixel-space models that are trained end-to-end, latent models are perceived to be more efficient and to produce higher image quality at high resolution. Here we challenge these notions, and show that pixel-space models can be very competitive to latent models both in quality and efficiency, achieving 1.5 FID on ImageNet512 and new SOTA results on ImageNet128, ImageNet256 and Kinetics600. We present a simple recipe for scaling end-to-end pixel-space diffusion models to high resolutions. 1: Use the sigmoid loss-weighting (Kingma & Gao, 2023) with our prescribed hyper-parameters. 2: Use our simplified memory-efficient architecture with fewer skip-connections. 3: Scale the model to favor processing the image at a high resolution with fewer parameters, rather than using more parameters at a lower resolution. Combining these with guidance intervals, we obtain a family of pixel-space diffusion models we call Simpler Diffusion (SiD2).

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Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts, Ruiqi Gao, Tim Salimans. 2024-10-25. Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion. https://arxiv.org/abs/2410.19324

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