Text-to-Image (T2I) models often suffer from text-image misalignment in complex scenes involving multiple objects and attributes. Semantic binding aims to mitigate this issue by accurately associating the generated attributes and objects with their corresponding noun phrases (NPs). Existing methods rely on text or latent optimizations, yet the factors influencing semantic binding remain underexplored. Here we investigate the geometrical properties of text token embeddings and their cross-attention (CA) maps. We empirically and theoretically analyze that the geometrical properties of token embeddings, specifically both angular distances and norms, play a crucial role in CA map differentiation. Then, we propose \textbf{TeeMo}, a training-free text embedding-aware T2I framework with strong semantic binding. TeeMo consists of Causality-Aware Projection-Out (CAPO) for distinct inter-NP CA maps and Adaptive Token Mixing (ATM) with our loss to enhance inter-NP separation while maintaining intra-NP cohesion in CA maps. Extensive experiments confirm TeeMo consistently outperforms prior arts across diverse baselines and datasets.

Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understanding of cross-attention layers remains somewhat limited. In this study, we present a method for constructing Head Relevance Vectors (HRVs) that align with useful visual concepts. An HRV for a given visual concept is a vector with a length equal to the total number of cross-attention heads, where each element represents the importance of the corresponding head for the given visual concept. We develop and employ an ordered weakening analysis to demonstrate the effectiveness of HRVs as interpretable features. To demonstrate the utility of HRVs, we propose concept strengthening and concept adjusting methods and apply them to enhance three visual generative tasks. We show that misinterpretations of polysemous words in image generation can be corrected in most cases, five challenging attributes in image editing can be successfully modified, and catastrophic neglect in multi-concept generation can be mitigated. Overall, our work provides an advancement in understanding cross-attention layers and introduces new approaches for fine-controlling these layers at the head level.

Recent advancements in text-to-video (T2V) generation using diffusion models have garnered significant attention. However, existing T2V models primarily focus on simple scenes featuring a single object performing a single motion. Challenges arise in scenarios involving multiple objects with distinct motions, often leading to incorrect video-text alignment between subjects and their corresponding motions. To address this challenge, we propose \textbf{S$^2$AG-Vid}, a training-free inference-stage optimization method that improves the alignment of multiple objects with their corresponding motions in T2V models. S$^2$AG-Vid initially applies a spatial position-based, cross-attention (CA) constraint in the early stages of the denoising process, facilitating multiple nouns distinctly attending to the correct subject regions. To enhance the motion-subject binding, we implement a syntax-guided contrastive constraint in the subsequent denoising phase, aimed at improving the correlations between the CA maps of verbs and their corresponding nouns.Both qualitative and quantitative evaluations demonstrate that the proposed framework significantly outperforms baseline approaches, producing higher-quality videos with improved subject-motion consistency.