FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space
We introduce FSVideo, a fast speed transformer-based image-to-video (I2V) diffusion framework. We build our framework on the following key components: 1.) a new video autoencoder with highly-compressed latent space (64times64times4 spatial-temporal downsampling ratio), achieving competitive reconstruction quality; 2.) a diffusion transformer (DIT) architecture with a new layer memory design to enhance inter-layer information flow and context reuse within DIT, and 3.) a multi-resolution generation strategy via a few-step DIT upsampler to increase video fidelity. Our final model, which contains a 14B DIT base model and a 14B DIT upsampler, achieves competitive performance against other popular open-source models, while being an order of magnitude faster. We discuss our model design as well as training strategies in this report.
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FSVideo Team
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Qingyu Chen
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Zhiyuan Fang
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Haibin Huang
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Xinwei Huang
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Tong Jin
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Minxuan Lin
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2026-02-03T10:04:05.071Z
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Minxuan Lin
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Bo Liu
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Celong Liu
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Chongyang Ma
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Xing Mei
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2026-02-04T12:32:32.886Z
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Xing Mei
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Xiaohui Shen
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Yaojie Shen
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2026-02-09T08:36:20.787Z
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Yaojie Shen
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Fuwen Tan
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Angtian Wang
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Xiao Yang
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2026-03-05T09:58:48.647Z
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Xiao Yang
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Yiding Yang
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Jiamin Yuan
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Lingxi Zhang
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Yuxin Zhang
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2026-02-04T12:32:35.615Z
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Yuxin Zhang
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FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space
We introduce FSVideo, a fast speed transformer-based image-to-video (I2V) diffusion framework. We build our framework on the following key components: 1.) a new video autoencoder with highly-compressed latent space (64times64times4 spatial-temporal downsampling ratio), achieving competitive reconstruction quality; 2.) a diffusion transformer (DIT) architecture with a new layer memory design to enhance inter-layer information flow and context reuse within DIT, and 3.) a multi-resolution generation strategy via a few-step DIT upsampler to increase video fidelity. Our final model, which contains a 14B DIT base model and a 14B DIT upsampler, achieves competitive performance against other popular open-source models, while being an order of magnitude faster. We discuss our model design as well as training strategies in this report.
FSVideo is a fast transformer-based image-to-video diffusion framework that uses a compressed video autoencoder, diffusion transformer architecture with enhanced layer memory, and multi-resolution generation strategy to achieve high performance with significantly reduced computation time.
video autoencoder diffusion transformer DIT layer memory design inter-layer information flow context reuse multi-resolution generation DIT upsampler image-to-video diffusion