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<p>To address issues such as imprecise intent parsing, separation of generation and segmentation optimization, and insufficient robustness in current user intent-guided digital media content generation models, this study proposes the LLaDiSAM framework. It constructs an “understanding-generation-optimization” closed loop through the collaboration of three modules: LLaDA-V (intent parsing), DiT (diffusion generation), and FastSAM (segmentation optimization). Experiments on four datasets including LAION-5B and SA-1B show that LLaDiSAM achieves an IFA of 0.89–0.92, an FID as low as 6.67–7.21, and an mIoU of 0.88–0.91. The coefficient of variation (CV) in 10 test runs is all less than 5%, and the overall deviation rate in the “no reference + concise instruction” scenario is only 7.5%, which is significantly better than 12 baseline models such as SDXL and DALL·E 3. The existing limitations include weak sorting capability for complex multi-intents and room for improvement in inference efficiency. Future work will focus on multi-intent priority modeling and lightweight optimization of the segmentation module. This framework provides a new efficient and controllable paradigm for digital media creation, and promotes the practical application of intent-guided generation technology.</p>

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A user-intent guided diffusion-segmentation collaborative framework for controllable digital media content generation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Yantong Long,
  • Sixi Chen

摘要

To address issues such as imprecise intent parsing, separation of generation and segmentation optimization, and insufficient robustness in current user intent-guided digital media content generation models, this study proposes the LLaDiSAM framework. It constructs an “understanding-generation-optimization” closed loop through the collaboration of three modules: LLaDA-V (intent parsing), DiT (diffusion generation), and FastSAM (segmentation optimization). Experiments on four datasets including LAION-5B and SA-1B show that LLaDiSAM achieves an IFA of 0.89–0.92, an FID as low as 6.67–7.21, and an mIoU of 0.88–0.91. The coefficient of variation (CV) in 10 test runs is all less than 5%, and the overall deviation rate in the “no reference + concise instruction” scenario is only 7.5%, which is significantly better than 12 baseline models such as SDXL and DALL·E 3. The existing limitations include weak sorting capability for complex multi-intents and room for improvement in inference efficiency. Future work will focus on multi-intent priority modeling and lightweight optimization of the segmentation module. This framework provides a new efficient and controllable paradigm for digital media creation, and promotes the practical application of intent-guided generation technology.