title: Wan: Open and Advanced Large-Scale Video Generative Models
summary: This report presents Wan, a comprehensive and open suite of video foundation
  models designed to push the boundaries of video generation. Built upon the
  mainstream diffusion transformer paradigm, Wan achieves significant
  advancements in generative capabilities through a series of innovations,
  including our novel VAE, scalable pre-training strategies, large-scale data
  curation, and automated evaluation metrics. These contributions collectively
  enhance the model's performance and versatility. Specifically, Wan is
  characterized by four key features: Leading Performance: The 14B model of Wan,
  trained on a vast dataset comprising billions of images and videos,
  demonstrates the scaling laws of video generation with respect to both data and
  model size. It consistently outperforms the existing open-source models as well
  as state-of-the-art commercial solutions across multiple internal and external
  benchmarks, demonstrating a clear and significant performance superiority.
  Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B
  parameters, for efficiency and effectiveness respectively. It also covers
  multiple downstream applications, including image-to-video, instruction-guided
  video editing, and personal video generation, encompassing up to eight tasks.
  Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource
  efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range
  of consumer-grade GPUs. Openness: We open-source the entire series of Wan,
  including source code and all models, with the goal of fostering the growth of
  the video generation community. This openness seeks to significantly expand the
  creative possibilities of video production in the industry and provide academia
  with high-quality video foundation models. All the code and models are
  available at https://github.com/Wan-Video/Wan2.1.
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paper_title: Wan: Open and Advanced Large-Scale Video Generative Models
paper_summary: This report presents Wan, a comprehensive and open suite of video foundation
  models designed to push the boundaries of video generation. Built upon the
  mainstream diffusion transformer paradigm, Wan achieves significant
  advancements in generative capabilities through a series of innovations,
  including our novel VAE, scalable pre-training strategies, large-scale data
  curation, and automated evaluation metrics. These contributions collectively
  enhance the model's performance and versatility. Specifically, Wan is
  characterized by four key features: Leading Performance: The 14B model of Wan,
  trained on a vast dataset comprising billions of images and videos,
  demonstrates the scaling laws of video generation with respect to both data and
  model size. It consistently outperforms the existing open-source models as well
  as state-of-the-art commercial solutions across multiple internal and external
  benchmarks, demonstrating a clear and significant performance superiority.
  Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B
  parameters, for efficiency and effectiveness respectively. It also covers
  multiple downstream applications, including image-to-video, instruction-guided
  video editing, and personal video generation, encompassing up to eight tasks.
  Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource
  efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range
  of consumer-grade GPUs. Openness: We open-source the entire series of Wan,
  including source code and all models, with the goal of fostering the growth of
  the video generation community. This openness seeks to significantly expand the
  creative possibilities of video production in the industry and provide academia
  with high-quality video foundation models. All the code and models are
  available at https://github.com/Wan-Video/Wan2.1.
paper_ai_summary: Wan, a comprehensive suite of video foundation models built on the diffusion transformer paradigm, advannces video generation by introducing a novel VAE, scalable pre-training strategies, and large-scale data curation, offering superior performance and versatility across various applications with both large and efficient models.
paper_ai_keywords: diffusion transformer VAE scalable pre-training large-scale data curation video generation image-to-video instruction-guided video editing personal video generation VRAM