We demonstrate generating high-dynamic range (HDR) images using the concerted
action of multiple black-box, pre-trained low-dynamic range (LDR) image
diffusion models. Common diffusion models are not HDR as, first, there is no
sufficiently large HDR image dataset available to re-train them, and second,
even if it was, re-training such models is impossible for most compute budgets.
Instead, we seek inspiration from the HDR image capture literature that
traditionally fuses sets of LDR images, called "brackets", to produce a single
HDR image. We operate multiple denoising processes to generate multiple LDR
brackets that together form a valid HDR result. To this end, we introduce an
exposure consistency term into the diffusion process to couple the brackets
such that they agree across the exposure range they share. We demonstrate HDR
versions of state-of-the-art unconditional and conditional as well as
restoration-type (LDR2HDR) generative modeling.

使用多个预训练的低动态范围 (LDR) 图像扩散模型的协同作用生成高动态范围 (HDR) 图像，并引入曝光一致性项将 LDR 图像融合，达到有效的 HDR 结果。

曝光扩散：通过一致的低动态范围去噪生成高动态范围图像

Exposure Diffusion: HDR Image Generation by Consistent LDR denoising

Diffusion models have achieved impressive results in generating diverse and
realistic data by employing multi-step denoising processes. However, the need
for accommodating significant variations in input noise at each time-step has
led to diffusion models requiring a large number of parameters for their
denoisers. We have observed that diffusion models effectively act as filters
for different frequency ranges at each time-step noise. While some previous
works have introduced multi-expert strategies, assigning denoisers to different
noise intervals, they overlook the importance of specialized operations for
high and low frequencies. For instance, self-attention operations are effective
at handling low-frequency components (low-pass filters), while convolutions
excel at capturing high-frequency features (high-pass filters). In other words,
existing diffusion models employ denoisers with the same architecture, without
considering the optimal operations for each time-step noise. To address this
limitation, we propose a novel approach called Multi-architecturE Multi-Expert
(MEME), which consists of multiple experts with specialized architectures
tailored to the operations required at each time-step interval. Through
extensive experiments, we demonstrate that MEME outperforms large competitors
in terms of both generation performance and computational efficiency.

本文提出了一种新颖的多专家方法 MEME，用于扩展扩散模型的过滤器以适应输入噪声的不同频率范围，并通过大量实验表明，MEME 在生成性能和计算效率方面优于传统方法