Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in
  Text-to-Image Generation
Daiqing Li Aleks Kamko Ehsan Akhgari Ali Sabet Linmiao Xu Suhail Doshi
In this work, we share three insights for achieving state-of-the-art
aesthetic quality in text-to-image generative models. We focus on three
critical aspects for model improvement: enhancing color and contrast, improving
generation across multiple aspect ratios, and improving human-centric fine
details. First, we delve into the significance of the noise schedule in
training a diffusion model, demonstrating its profound impact on realism and
visual fidelity. Second, we address the challenge of accommodating various
aspect ratios in image generation, emphasizing the importance of preparing a
balanced bucketed dataset. Lastly, we investigate the crucial role of aligning
model outputs with human preferences, ensuring that generated images resonate
with human perceptual expectations. Through extensive analysis and experiments,
Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic
quality under various conditions and aspect ratios, outperforming both
widely-used open-source models like SDXL and Playground v2, and closed-source
commercial systems such as DALLE 3 and Midjourney v5.2. Our model is
open-source, and we hope the development of Playground v2.5 provides valuable
guidelines for researchers aiming to elevate the aesthetic quality of
diffusion-based image generation models.