The proposed masked cross-attention mechanism can be further improved to better handle identity mixing and ensure more consistent preservation of individual identities by incorporating additional constraints and regularization techniques. One approach could be to introduce a more sophisticated attention mechanism that dynamically adjusts the weights assigned to different identities based on their relevance to the text prompt. This could involve incorporating a mechanism that penalizes the mixing of identities by encouraging the model to focus on individual identities more distinctly. Additionally, introducing constraints on the attention weights to ensure that each identity receives adequate attention could help mitigate identity mixing issues. Furthermore, exploring techniques from self-attention mechanisms in transformer models could provide insights into improving the attention mechanism's performance in handling multiple identities effectively.