Inertial　measurement　units　(IMU)　in　the　capturing　device　can　record　the　motion　information　of　the　device,　with　gyroscopes　measuring　angular　velocity　and　accelerometers　measuring　acceleration.　However,　conventional　deblurring　methods　seldom　incorporate　IMU　data,　and　existing　approaches　that　utilize　IMU　information　often　face　challenges　in　fully　leveraging　this　valuable　data,　resulting　in　noise　issues　from　the　sensors.　To　address　these　issues,　in　this　paper,　we　propose　a　multi-stage　deblurring　network　named　INformer,　which　combines　inertial　information　with　the　Transformer　architecture.　Specifically,　we　design　an　IMU-image　Attention　Fusion　(IAF)　block　to　merge　motion　information　derived　from　inertial　measurements　with　blurry　image　features　at　the　attention　level.　Furthermore,　we　introduce　an　Inertial-Guided　Deformable　Attention　(IGDA)　block　for　utilizing　the　motion　information　features　as　guidance　to　adaptively　adjust　the　receptive　field,　which　can　further　refine　the　corresponding　blur　kernel　for　pixels.　Extensive　experiments　on　comprehensive　benchmarks　demonstrate　that　our　proposed　method　performs　favorably　against　state-of-the-art　deblurring　approaches.