Despite recent significant strides achieved by diffusion-based Text-to-Image (T2I) models, current systems are still less capable of ensuring decent compositional generation aligned with text prompts, particularly for the multi-object generation. This work illuminates the fundamental reasons for such misalignment, pinpointing issues related to low attention activation scores and mask overlaps. While previous research efforts have individually tackled these issues, we assert that a holistic approach is paramount. Thus, we propose two novel objectives, the Separate loss and the Enhance loss, that reduce object mask overlaps and maximize attention scores, respectively. Our method diverges from conventional test-time-adaptation techniques, focusing on finetuning critical parameters, which enhances scalability and generalizability. Comprehensive evaluations demonstrate the superior performance of our model in terms of image realism, text-image alignment, and adaptability, notably outperforming prominent baselines. Ultimately, this research paves the way for T2I diffusion models with enhanced compositional capacities and broader applicability.

翻译：尽管近期基于扩散的文本到图像（T2I）模型取得了显著进展，但现有系统在确保与文本提示高度一致的组合生成（尤其是多对象生成）方面仍存在不足。本研究揭示了这种不一致的根本原因，指出了与注意力激活分数偏低和掩膜重叠相关的问题。虽然以往的研究分别针对这些问题进行了探讨，但我们认为采取整体性方法至关重要。为此，我们提出了两种新颖的损失函数——分离损失和增强损失，分别旨在减少对象掩膜重叠和最大化注意力激活分数。我们的方法有别于传统的测试时自适应技术，专注于关键参数的微调，从而提升了可扩展性和泛化能力。全面的评估表明，我们的模型在图像真实感、文本-图像对齐及适应性方面均表现出卓越性能，显著优于主流基线模型。最终，本研究为具备更强组合能力与更广泛应用前景的T2I扩散模型铺平了道路。