AlignVid: Training-Free Attention Scaling for Semantic Fidelity in Text-Guided Image-to-Video Generation

Text-guided image-to-video generation has made substantial progress, yet it still struggles to execute text-specified edits that require substantial changes to a reference image ($\mathit{\text{e.g.,}}\mathit{\text{object}}\mathit{\text{addition,}}\mathit{\text{removal,}}\mathit{\text{or}}\mathit{\text{modification}}$). Empirically, our analysis reveals that this stems from $\mathbf{\text{visual}}\mathbf{\text{dominance}}$, where the reference image causes severe attention dispersion, inhibiting the model’s ability to incorporate new semantic information. To address this, we propose $\mathbf{\text{AlignVid}}$, a training-free intervention that re-calibrates the model’s internal attention distribution. Drawing on an energy-based perspective of attention, AlignVid employs Attention Scaling Modulation ($\mathbf{\text{ASM}}$) to reduce attention entropy and concentrate focus on semantic tokens, alongside Guidance Scheduling ($\mathbf{\text{GS}}$) to maintain generation stability. To rigorously assess this capability, we present $\mathbf{\text{OmitI2V}}$, a comprehensive benchmark for evaluating prompt adherence across object modification, addition, and deletion. Extensive experiments demonstrate that AlignVid effectively enhances semantic fidelity with negligible computational overhead. Code and the OmitI2V benchmark are available at https://github.com/LAW1223/AlignVid.