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 in fact be very competitive to latent approaches both in quality and efficiency, achieving 1.5 FID on ImageNet512 and new SOTA results on ImageNet128 and ImageNet256. We present a simple recipe for scaling end-to-end pixel-space diffusion models to high resolutions. 1: Use the sigmoid loss (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 high resolution with fewer parameters, rather than using more parameters but at a lower resolution. When combining these three steps with recently proposed tricks like guidance intervals, we obtain a family of pixel-space diffusion models we call Simple Diffusion v2 (SiD2).

本研究针对潜在扩散模型在高分辨率图像合成中的广泛应用，提出了像素空间模型在质量和效率上也非常具有竞争力的观点。通过简单的三步法，我们提出了一种新的像素空间扩散模型SiD2，达到ImageNet512上1.5 FID的卓越成果，推动了高分辨率图像处理的进展。

更简单的扩散模型 (SiD2)：在ImageNet512上实现1.5 FID的像素空间扩散