Latent Consistency Distillation (LCD) has emerged as a promising paradigm for
efficient text-to-image synthesis. By distilling a latent consistency model
(LCM) from a pre-trained teacher latent diffusion model (LDM), LCD facilitates
the generation of high-fidelity images within merely 2 to 4 inference steps.
However, the LCM's efficient inference is obtained at the cost of the sample
quality. In this paper, we propose compensating the quality loss by aligning
LCM's output with human preference during training. Specifically, we introduce
Reward Guided LCD (RG-LCD), which integrates feedback from a reward model (RM)
into the LCD process by augmenting the original LCD loss with the objective of
maximizing the reward associated with LCM's single-step generation. As
validated through human evaluation, when trained with the feedback of a good
RM, the 2-step generations from our RG-LCM are favored by humans over the
50-step DDIM samples from the teacher LDM, representing a 25 times inference
acceleration without quality loss.
As directly optimizing towards differentiable RMs can suffer from
over-optimization, we overcome this difficulty by proposing the use of a latent
proxy RM (LRM). This novel component serves as an intermediary, connecting our
LCM with the RM. Empirically, we demonstrate that incorporating the LRM into
our RG-LCD successfully avoids high-frequency noise in the generated images,
contributing to both improved FID on MS-COCO and a higher HPSv2.1 score on
HPSv2's test set, surpassing those achieved by the baseline LCM.

通过奖励引导，补偿样本质量损失，提高 Latent Consistency Distillation (LCD) 方法的图像生成质量，同时使用潜变量代理奖励模型 (latent proxy RM, LRM) 作为中介，连接 Latent Consistency Model (LCM) 和奖励模型 (RM) 来避免高频噪声，从而实现更高质量的大规模图像生成。

奖励引导的潜变量一致性蒸馏

Reward Guided Latent Consistency Distillation

This paper presents $\textbf{R}$epresentation-$\textbf{C}$onditioned image
$\textbf{G}$eneration (RCG), a simple yet effective image generation framework
which sets a new benchmark in class-unconditional image generation. RCG does
not condition on any human annotations. Instead, it conditions on a
self-supervised representation distribution which is mapped from the image
distribution using a pre-trained encoder. During generation, RCG samples from
such representation distribution using a representation diffusion model (RDM),
and employs a pixel generator to craft image pixels conditioned on the sampled
representation. Such a design provides substantial guidance during the
generative process, resulting in high-quality image generation. Tested on
ImageNet 256$\times$256, RCG achieves a Frechet Inception Distance (FID) of
3.31 and an Inception Score (IS) of 253.4. These results not only significantly
improve the state-of-the-art of class-unconditional image generation but also
rival the current leading methods in class-conditional image generation,
bridging the long-standing performance gap between these two tasks. Code is
available at this https URL

这篇论文介绍了一种名为 RCG 的图像生成框架，通过自监督学习的表示分布条件进行图像生成，实现了高质量的图像生成，取得了显著的结果。