BeautifulPrompt：面向文本到图像合成的自动提示工程
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
AI总结：
本文提出BeautifulPrompt，一种基于视觉AI反馈的强化学习深度生成模型，可从简单描述自动生成高质量提示，显著提升扩散模型生成图像的美观度，并集成至云原生平台。
摘要：
近年来，基于扩散的深度生成模型（如Stable Diffusion）在文本到图像合成方面展现了令人印象深刻的结果。然而，当前的文本到图像模型通常需要人类进行多轮提示工程，才能为实际应用生成令人满意的结果。我们提出了BeautifulPrompt，一种深度生成模型，能够从非常简单的原始描述中生成高质量提示，从而使基于扩散的模型能够生成更美观的图像。在我们的工作中，我们首先在低质量和高质量的提示对集合上对BeautifulPrompt模型进行了微调。然后，为确保我们生成的提示能够生成更美观的图像，我们进一步提出了一种基于视觉AI反馈的强化学习技术来微调我们的模型，以最大化生成提示的奖励值，其中奖励值基于PickScore和美学评分计算。我们的结果表明，从视觉AI反馈中学习有望显著提高生成提示和图像的质量。我们进一步展示了将BeautifulPrompt集成到云原生AI平台中，以在云端提供更好的文本到图像生成服务。
英文摘要：
Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often require multiple passes of prompt engineering by humans in order to produce satisfactory results for real-world applications. We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions, which enables diffusion-based models to generate more beautiful images. In our work, we first fine-tuned the BeautifulPrompt model over low-quality and high-quality collecting prompt pairs. Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores. Our results demonstrate that learning from visual AI feedback promises the potential to improve the quality of generated prompts and images significantly. We further showcase the integration of BeautifulPrompt to a cloud-native AI platform to provide better text-to-image generation service in the cloud.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
