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.

本研究探讨了在文本到图像生成模型中实现最先进的美学质量的三个关键点：增强颜色和对比度，改善多个宽高比的生成，改善以人为中心的细节。通过深入分析和实验，Playground v2.5 在各种条件和宽高比下展现了最先进的美学质量表现，优于常用的开源模型，如SDXL和Playground v2，以及闭源商业系统如DALLE 3和Midjourney v5.2。我们的模型是开源的，希望Playground v2.5的发展为旨在提升基于扩散的图像生成模型的美学质量的研究者提供有价值的指导。

Playground v2.5：提升文本到图像生成中的美学质量的三个洞察