OSV: One Step is Enough for High-Quality Image to Video Generation
Xiaofeng Mao, Zhengkai Jiang, Fu-Yun Wang, Jiangning Zhang, Hao Chen,, Mingmin Chi, Yabiao Wang, Wenhan Luo

TL;DR
This paper introduces OSV, a novel one-step video generation model that combines a two-stage training framework with a new discriminator design, achieving high-quality results with minimal inference steps.
Contribution
The work presents a unique two-stage training approach integrating consistency distillation and GAN training, along with a new video discriminator, enabling high-quality one-step video generation.
Findings
Outperforms existing methods on OpenWebVid-1M benchmark
Achieves 1-step FVD of 171.15, surpassing 8-step AnimateLCM
Approaches 25-step Stable Video Diffusion performance
Abstract
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to substantial computational and time costs. While efforts have been made to accelerate video diffusion by reducing inference steps (through techniques like consistency distillation) and GAN training (these approaches often fall short in either performance or training stability). In this work, we introduce a two-stage training framework that effectively combines consistency distillation with GAN training to address these challenges. Additionally, we propose a novel video discriminator design, which eliminates the need for decoding the video latents and improves the final performance. Our model is capable of producing high-quality videos in merely one-step, with the flexibility to perform multi-step refinement for…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Image Processing Techniques and Applications · Advanced Vision and Imaging
MethodsDiffusion
