Recent text-to-image generative models can generate high-fidelity images from text prompts. However, these models struggle to consistently generate the same objects in different contexts with the same appearance. Consistent object generation is important to many downstream tasks like generating comic book illustrations with consistent characters and setting. Numerous approaches attempt to solve this problem by extending the vocabulary of diffusion models through fine-tuning. However, even lightweight fine-tuning approaches can be prohibitively expensive to run at scale and in real-time. We introduce a method called ObjectComposer for generating compositions of multiple objects that resemble user-specified images. Our approach is training-free, leveraging the abilities of preexisting models. We build upon the recent BLIP-Diffusion model, which can generate images of single objects specified by reference images. ObjectComposer enables the consistent generation of compositions containing multiple specific objects simultaneously, all without modifying the weights of the underlying models.

现有的文本到图像生成模型在不同语境中生成相同外观的对象方面存在困难，本论文介绍了一种名为ObjectComposer的方法，通过不修改底层模型的权重，训练了一种无需fine-tuning的方法，可一次生成多个特定对象的组合。

ObjectComposer: 一致生成多个对象而无需进行微调