Recent advancements in generative models have revolutionized the field of artificial intelligence, enabling the creation of highly-realistic and detailed images. In this study, we propose a novel Mask Conditional Text-to-Image Generative Model (MCGM) that leverages the power of conditional diffusion models to generate pictures with specific poses. Our model builds upon the success of the Break-a-scene [1] model in generating new scenes using a single image with multiple subjects and incorporates a mask embedding injection that allows the conditioning of the generation process. By introducing this additional level of control, MCGM offers a flexible and intuitive approach for generating specific poses for one or more subjects learned from a single image, empowering users to influence the output based on their requirements. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our proposed model in generating high-quality images that meet predefined mask conditions and improving the current Break-a-scene generative model.

本研究解决了现有生成模型在生成特定姿势图像时的局限性。我们提出的掩膜条件文本到图像生成模型（MCGM）通过引入掩膜嵌入注入技术，提供对生成过程的灵活控制，使用户能够基于需求生成高质量图像。实验证明，MCGM有效提升了当前Break-a-scene生成模型的性能。

MCGM：掩膜条件文本到图像生成模型