I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models
In the rapidly evolving domain of digital content generation, the focus has
shifted from text-to-image (T2I) models to more advanced video diffusion
models, notably text-to-video (T2V) and image-to-video (I2V). This paper
addresses the intricate challenge posed by I2V: converting static images into
dynamic, lifelike video sequences while preserving the original image fidelity.
Traditional methods typically involve integrating entire images into diffusion
processes or using pretrained encoders for cross attention. However, these
approaches often necessitate altering the fundamental weights of T2I models,
thereby restricting their reusability. We introduce a novel solution, namely
I2V-Adapter, designed to overcome such limitations. Our approach preserves the
structural integrity of T2I models and their inherent motion modules. The
I2V-Adapter operates by processing noised video frames in parallel with the
input image, utilizing a lightweight adapter module. This module acts as a
bridge, efficiently linking the input to the model's self-attention mechanism,
thus maintaining spatial details without requiring structural changes to the
T2I model. Moreover, I2V-Adapter requires only a fraction of the parameters of
conventional models and ensures compatibility with existing community-driven
T2I models and controlling tools. Our experimental results demonstrate
I2V-Adapter's capability to produce high-quality video outputs. This
performance, coupled with its versatility and reduced need for trainable
parameters, represents a substantial advancement in the field of AI-driven
video generation, particularly for creative applications.
658e3b153f3faee8ecaaac26
Xun Guo
658e3b153f3faee8ecaaac27
Mingwu Zheng
658e3b153f3faee8ecaaac28
Liang Hou
claimed_verified
2025-07-01T10:17:14.426Z
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Liang Hou
user
lianghou
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Yuan Gao
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2024-06-06T23:41:51.506Z
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Haibin Huang
user
brotherhuang
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Yufan Deng
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Chongyang Ma
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Weiming Hu
658e3b153f3faee8ecaaac2d
Zhengjun Zha
658e3b153f3faee8ecaaac2e
Haibin Huang
658e3b153f3faee8ecaaac2f
Pengfei Wan
658e3b153f3faee8ecaaac30
Di Zhang
I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models
In the rapidly evolving domain of digital content generation, the focus has
shifted from text-to-image (T2I) models to more advanced video diffusion
models, notably text-to-video (T2V) and image-to-video (I2V). This paper
addresses the intricate challenge posed by I2V: converting static images into
dynamic, lifelike video sequences while preserving the original image fidelity.
Traditional methods typically involve integrating entire images into diffusion
processes or using pretrained encoders for cross attention. However, these
approaches often necessitate altering the fundamental weights of T2I models,
thereby restricting their reusability. We introduce a novel solution, namely
I2V-Adapter, designed to overcome such limitations. Our approach preserves the
structural integrity of T2I models and their inherent motion modules. The
I2V-Adapter operates by processing noised video frames in parallel with the
input image, utilizing a lightweight adapter module. This module acts as a
bridge, efficiently linking the input to the model's self-attention mechanism,
thus maintaining spatial details without requiring structural changes to the
T2I model. Moreover, I2V-Adapter requires only a fraction of the parameters of
conventional models and ensures compatibility with existing community-driven
T2I models and controlling tools. Our experimental results demonstrate
I2V-Adapter's capability to produce high-quality video outputs. This
performance, coupled with its versatility and reduced need for trainable
parameters, represents a substantial advancement in the field of AI-driven
video generation, particularly for creative applications.
I2V-Adapter enables the conversion of images to videos by integrating a lightweight adapter into existing T2I models, preserving motion modules and reducing parameter requirements.
video diffusion models text-to-video image-to-video diffusion processes pretrained encoders cross attention T2I models I2V-Adapter noised video frames self-attention mechanism parameter requirements community-driven T2I models controlling tools high-quality video outputs