title: Magic 1-For-1: Generating One Minute Video Clips within One Minute
summary: In this technical report, we present Magic 1-For-1 (Magic141), an efficient
  video generation model with optimized memory consumption and inference latency.
  The key idea is simple: factorize the text-to-video generation task into two
  separate easier tasks for diffusion step distillation, namely text-to-image
  generation and image-to-video generation. We verify that with the same
  optimization algorithm, the image-to-video task is indeed easier to converge
  over the text-to-video task. We also explore a bag of optimization tricks to
  reduce the computational cost of training the image-to-video (I2V) models from
  three aspects: 1) model convergence speedup by using a multi-modal prior
  condition injection; 2) inference latency speed up by applying an adversarial
  step distillation, and 3) inference memory cost optimization with parameter
  sparsification. With those techniques, we are able to generate 5-second video
  clips within 3 seconds. By applying a test time sliding window, we are able to
  generate a minute-long video within one minute with significantly improved
  visual quality and motion dynamics, spending less than 1 second for generating
  1 second video clips on average. We conduct a series of preliminary
  explorations to find out the optimal tradeoff between computational cost and
  video quality during diffusion step distillation and hope this could be a good
  foundation model for open-source explorations. The code and the model weights
  are available at https://github.com/DA-Group-PKU/Magic-1-For-1.
paper_authors._id: 67ac23166def89f9aae56abd
paper_authors.name: Hongwei Yi
paper_authors._id: 67ac23166def89f9aae56abe
paper_authors.name: Shitong Shao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-19T09:48:07.681Z
paper_authors._id: 64aa48245fc663fee24125be
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64aa48245fc663fee24125be/8Me4rbY3XdY48p9QyEpaA.jpeg
paper_authors.fullname: ShanShan
paper_authors.type: user
paper_authors.user: sst12345
paper_authors._id: 67ac23166def89f9aae56abf
paper_authors.name: Tian Ye
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-12T09:16:12.141Z
paper_authors._id: 66015e8aa4d296af07de538e
paper_authors.avatarUrl: /avatars/a1295c631cc2646282c545859975ce4c.svg
paper_authors.fullname: Ye
paper_authors.type: user
paper_authors.user: Owen777
paper_authors._id: 67ac23166def89f9aae56ac0
paper_authors.name: Jiantong Zhao
paper_authors._id: 67ac23166def89f9aae56ac1
paper_authors.name: Qingyu Yin
paper_authors._id: 67ac23166def89f9aae56ac2
paper_authors.name: Michael Lingelbach
paper_authors._id: 67ac23166def89f9aae56ac3
paper_authors.name: Li Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-04T07:10:15.479Z
paper_authors._id: 66135a5e50350afe76beebce
paper_authors.avatarUrl: /avatars/370a4b83949355feb050c2cb0425c264.svg
paper_authors.fullname: yl2488
paper_authors.type: user
paper_authors.user: yl2488
paper_authors._id: 67ac23166def89f9aae56ac4
paper_authors.name: Yonghong Tian
paper_authors._id: 67ac23166def89f9aae56ac5
paper_authors.name: Enze Xie
paper_authors._id: 67ac23166def89f9aae56ac6
paper_authors.name: Daquan Zhou
paper_title: Magic 1-For-1: Generating One Minute Video Clips within One Minute
paper_summary: In this technical report, we present Magic 1-For-1 (Magic141), an efficient
  video generation model with optimized memory consumption and inference latency.
  The key idea is simple: factorize the text-to-video generation task into two
  separate easier tasks for diffusion step distillation, namely text-to-image
  generation and image-to-video generation. We verify that with the same
  optimization algorithm, the image-to-video task is indeed easier to converge
  over the text-to-video task. We also explore a bag of optimization tricks to
  reduce the computational cost of training the image-to-video (I2V) models from
  three aspects: 1) model convergence speedup by using a multi-modal prior
  condition injection; 2) inference latency speed up by applying an adversarial
  step distillation, and 3) inference memory cost optimization with parameter
  sparsification. With those techniques, we are able to generate 5-second video
  clips within 3 seconds. By applying a test time sliding window, we are able to
  generate a minute-long video within one minute with significantly improved
  visual quality and motion dynamics, spending less than 1 second for generating
  1 second video clips on average. We conduct a series of preliminary
  explorations to find out the optimal tradeoff between computational cost and
  video quality during diffusion step distillation and hope this could be a good
  foundation model for open-source explorations. The code and the model weights
  are available at https://github.com/DA-Group-PKU/Magic-1-For-1.
paper_ai_summary: Magic141 efficiently generates high-quality videos by factorizing the text-to-video task into text-to-image and image-to-video components, optimizing for memory, speed, and quality.
paper_ai_keywords: diffusion step distillation text-to-image generation image-to-video generation multi-modal prior condition injection adversarial step distillation parameter sparsification test time sliding window