  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.
