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We introduce Deformable Convolution v4 (DCNv4), a highly efficient andeffective operator designed for a broad spectrum of vision applications. DCNv4addresses the limitations of its predecessor, DCNv3, with two key enhancements:1. removing softmax normalization in spatial aggregation to enhance its dynamicproperty and expressive power and 2. optimizing memory access to minimizeredundant operations for speedup. These improvements result in a significantlyfaster convergence compared to DCNv3 and a substantial increase in processingspeed, with DCNv4 achieving more than three times the forward speed. DCNv4demonstrates exceptional performance across various tasks, including imageclassification, instance and semantic segmentation, and notably, imagegeneration. When integrated into generative models like U-Net in the latentdiffusion model, DCNv4 outperforms its baseline, underscoring its possibilityto enhance generative models. In practical applications, replacing DCNv3 withDCNv4 in the InternImage model to create FlashInternImage results in up to 80%speed increase and further performance improvement without furthermodifications. The advancements in speed and efficiency of DCNv4, combined withits robust performance across diverse vision tasks, show its potential as afoundational building block for future vision models.

Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications