GROW: Watermark Generation with Progressive Guidance for Diffusion Models

Digital watermarking is a cornerstone for copyright protection. With the rapid advancement of generative models like diffusion models, in-generation and training-free watermarking techniques have garnered more attention for their endogeneity and convenience. These methods typically embed a watermark into the initial noise, where watermark extraction relies on Denoising Diffusion Implicit Models (DDIM) inversion. However, the computationally intensive extraction process severely hinders their path toward practical deployment. To overcome this critical bottleneck, we propose GROW, a novel training-free paradigm that reframes watermarking from a one-shot embedding'' to a progressivegrowth''. By progressively guiding using frequency-domain gradients, GROW naturally weaves the watermark into the image, which enables inversion-free extraction. Comprehensive experiments on multiple datasets show that GROW not only achieves superior robustness and imperceptibility but also offers a detection speed nearly 100x faster than inversion-based techniques. The code will be made publicly available.