通过惩罚图像边界激活值，提升扩散模型生成物体的完整性。
Penalizing Boundary Activation for Object Completeness in Diffusion Models
- 在去噪早期阶段惩罚图像边界激活，修复因随机裁剪导致的物体不完整问题。
- 对预训练Stable Diffusion模型改进后，物体完整性显著提升，图像质量无损。
- 无需重新训练，轻量级修改即可应用，适合快速部署到现有生成系统。
扩散模型已成为文本到图像生成的强大技术，可跨多个领域生成高质量、多样化的图像。然而，这类模型普遍存在物体生成不完整的问题，部分缺失或断裂会削弱其在下游任务中的表现。本研究深入分析了该问题，发现主要原因是训练中使用了随机裁剪（RandomCrop）数据增强方法。尽管该方法提升了模型泛化能力，却破坏了物体在训练过程中的连续性。为此，我们提出一种无需训练的解决方案：在去噪初期惩罚图像边界的激活值。该方法可轻松应用于预训练的Stable Diffusion模型，仅需少量修改且计算开销极低。大量实验表明，该方法显著提升了物体完整性与图像质量。
原文摘要 · Abstract (English)
Diffusion models have emerged as a powerful technique for text-to-image (T2I) generation, creating high-quality, diverse images across various domains. However, a common limitation in these models is the incomplete display of objects, where fragments or missing parts undermine the model's performance in downstream applications. In this study, we conduct an in-depth analysis of the incompleteness issue and reveal that the primary factor behind incomplete object generation is the usage of RandomCrop during model training. This widely used data augmentation method, though enhances model generalization ability, disrupts object continuity during training. To address this, we propose a training-free solution that penalizes activation values at image boundaries during the early denoising steps. Our method is easily applicable to pre-trained Stable Diffusion models with minimal modifications and negligible computational overhead. Extensive experiments demonstrate the effectiveness of our method, showing substantial improvements in object integrity and image quality.
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