Penalizing Boundary Activation for Object Completeness in Diffusion Models

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 can undermine the model's performance in downstream applications such as dataset synthesis and video generation using 2D prior-based models. % that demand visual accuracy, such as e-commerce product imaging and realistic digital content creation.In this study, we conduct the in-depth analysis of this issue and reveal that the primary culprit behind incomplete object generation is $\textit{RandomCrop}$. This data augmentation method, widely used in diffusion models, though enhances model generalization ability, disrupts object continuity during training. To address this, we propose a training-free solution that penalizes activation values occurring 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.