ACM Multimedia 2025 Grand Challenge report for Image-to-Video Generation Model Acceleration

摘要

Recently, MGTV organized the Image-to-Video Model Acceleration Challenge, calling for participants to propose optimization solutions for the Wan 2.1-14B model. The challenge emphasizes techniques such as quantization and GPU acceleration to improve the model's inference efficiency. As AIGC technology advances rapidly, video generation large models exhibit great potential in content creation, yet they face critical challenges of high computing power consumption, long inference time, and excessive VRAM usage during inference, which severely hinder content production efficiency. This challenge aims to explore approaches for efficient video generation under limited computing resources, requiring participants to reduce the model's computing power and VRAM demands while improving inference speed, all without compromising generation quality. To support participants' development and evaluation, the challenge provides a baseline framework and test dataset. For further details, please refer to the official challenge website (https://challenge.ai.mgtv.com/#/track/53).