Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery. The surge of personalization techniques that has followed has also allowed us to imagine unique concepts in new scenes. However, an intriguing question remains: How can we generate a new, imaginary concept that has never been seen before? In this paper, we present the task of creative text-to-image generation, where we seek to generate new members of a broad category (e.g., generating a pet that differs from all existing pets). We leverage the under-studied Diffusion Prior models and show that the creative generation problem can be formulated as an optimization process over the output space of the diffusion prior, resulting in a set of “prior constraints”. To keep our generated concept from converging into existing members, we incorporate a question-answering Vision-Language Model (VLM) that adaptively adds new constraints to the optimization problem, encouraging the model to discover increasingly more unique creations. Finally, we show that our prior constraints can also serve as a strong mixing mechanism allowing us to create hybrids between generated concepts, introducing even more flexibility into the creative process.

最近的文本到图像生成模型使我们能够将文字转化为充满活力、迷人的图像。随之而来的个性化技术的激增也让我们能够在新场景中想象出独特的概念。然而，一个有趣的问题仍然存在：我们如何才能产生一个以前从未见过的新的、想象的概念？在本文中，我们提出了创造性文本到图像生成的任务，我们寻求生成一个广泛类别的新成员（例如，生成与所有现有宠物不同的宠物）。我们利用尚未充分研究的扩散先验模型，并表明创意生成问题可以表述为扩散先验输出空间的优化过程，从而产生一组“先验约束”。为了防止我们生成的概念与现有成员融合，我们采用了问答视觉语言模型（VLM），它自适应地为优化问题添加新的约束，鼓励模型发现越来越独特的创作。最后，我们表明，我们的先验约束也可以充当强大的混合机制，使我们能够在生成的概念之间创建混合，从而为创作过程引入更多的灵活性。