Diffusion models have emerged as a powerful tool for generating high-quality
images from textual descriptions. Despite their successes, these models often
exhibit limited diversity in the sampled images, particularly when sampling
with a high classifier-free guidance weight. To address this issue, we present
Kaleido, a novel approach that enhances the diversity of samples by
incorporating autoregressive latent priors. Kaleido integrates an
autoregressive language model that encodes the original caption and generates
latent variables, serving as abstract and intermediary representations for
guiding and facilitating the image generation process. In this paper, we
explore a variety of discrete latent representations, including textual
descriptions, detection bounding boxes, object blobs, and visual tokens. These
representations diversify and enrich the input conditions to the diffusion
models, enabling more diverse outputs. Our experimental results demonstrate
that Kaleido effectively broadens the diversity of the generated image samples
from a given textual description while maintaining high image quality.
Furthermore, we show that Kaleido adheres closely to the guidance provided by
the generated latent variables, demonstrating its capability to effectively
control and direct the image generation process.

Kaleido 是一种改进图像生成多样性的方法，它利用自回归潜变量先验来增加样本的多样性，通过整合自回归语言模型编码原始说明并生成潜变量，以作为抽象和中间表示来指导和促进图像生成过程，从而提供更多样化的输出。

Kaleido 扩散：通过自回归潜在建模改进条件扩散模型

Kaleido Diffusion: Improving Conditional Diffusion Models with  Autoregressive Latent Modeling

Human values are crucial to human decision-making. Value pluralism is the
view that multiple correct values may be held in tension with one another
(e.g., when considering lying to a friend to protect their feelings, how does
one balance honesty with friendship?). As statistical learners, AI systems fit
to averages by default, washing out these potentially irreducible value
conflicts. To improve AI systems to better reflect value pluralism, the
first-order challenge is to explore the extent to which AI systems can model
pluralistic human values, rights, and duties as well as their interaction.
We introduce ValuePrism, a large-scale dataset of 218k values, rights, and
duties connected to 31k human-written situations. ValuePrism's contextualized
values are generated by GPT-4 and deemed high-quality by human annotators 91%
of the time. We conduct a large-scale study with annotators across diverse
social and demographic backgrounds to try to understand whose values are
represented.
With ValuePrism, we build Kaleido, an open, light-weight, and structured
language-based multi-task model that generates, explains, and assesses the
relevance and valence (i.e., support or oppose) of human values, rights, and
duties within a specific context. Humans prefer the sets of values output by
our system over the teacher GPT-4, finding them more accurate and with broader
coverage. In addition, we demonstrate that Kaleido can help explain variability
in human decision-making by outputting contrasting values. Finally, we show
that Kaleido's representations transfer to other philosophical frameworks and
datasets, confirming the benefit of an explicit, modular, and interpretable
approach to value pluralism. We hope that our work will serve as a step to
making more explicit the implicit values behind human decision-making and to
steering AI systems to make decisions that are more in accordance with them.

人类价值观和价值多元论对于人类决策至关重要。为了更好地反映价值多元论，我们介绍了 ValuePrism 数据集和 Kaleido 模型，它们可以生成、解释和评估特定语境下人类价值观、权利和责任的相关性和支持程度。我们的工作希望明确人类决策背后的隐含价值观，并引导 AI 系统做出更符合这些价值观的决策。