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In the rapidly evolving domain of digital content generation, the focus hasshifted from text-to-image (T2I) models to more advanced video diffusionmodels, notably text-to-video (T2V) and image-to-video (I2V). This paperaddresses the intricate challenge posed by I2V: converting static images intodynamic, lifelike video sequences while preserving the original image fidelity.Traditional methods typically involve integrating entire images into diffusionprocesses or using pretrained encoders for cross attention. However, theseapproaches often necessitate altering the fundamental weights of T2I models,thereby restricting their reusability. We introduce a novel solution, namelyI2V-Adapter, designed to overcome such limitations. Our approach preserves thestructural integrity of T2I models and their inherent motion modules. TheI2V-Adapter operates by processing noised video frames in parallel with theinput image, utilizing a lightweight adapter module. This module acts as abridge, efficiently linking the input to the model's self-attention mechanism,thus maintaining spatial details without requiring structural changes to theT2I model. Moreover, I2V-Adapter requires only a fraction of the parameters ofconventional models and ensures compatibility with existing community-drivenT2I models and controlling tools. Our experimental results demonstrateI2V-Adapter's capability to produce high-quality video outputs. Thisperformance, coupled with its versatility and reduced need for trainableparameters, represents a substantial advancement in the field of AI-drivenvideo generation, particularly for creative applications.

I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models