Deploying　data　plane　programs　across　the　network　is　typically　formulated　as　a　mixed-integer　programming　task,　leading　to　a　long　execution　time.　In　response,　existing　studies　carefully　tailor　heuristics　for　specific　task　properties　such　as　objectives.　However,　they　suffer　from　poor　solution　quality　under　dynamic　task　deployment　since　they　overfit　specific　task　properties.　Recently,　generative　diffusion　models　have　been　widely　adopted　in　network　optimizations　due　to　their　strong　adaptability　and　generalization.　Accordingly,　in　this　poster,　we　propose　a　diffusion　model-based　framework　for　data　plane　program　deployment　tasks.　Our　key　idea　is　to　leverage　the　reverse　denoising　process　of　diffusion　models　to　react　to　dynamic　task　changes　at　runtime　while　maintaining　high　solution　quality.　Preliminary　results　on　our　testbed　show　that　we　reduce　latency　by　66.67%　and　resource　overhead　by　58.62%　during　dynamic　deployment.　©　2025　IEEE.