title: Training-Free Efficient Video Generation via Dynamic Token Carving
summary: Despite the remarkable generation quality of video Diffusion Transformer
  (DiT) models, their practical deployment is severely hindered by extensive
  computational requirements. This inefficiency stems from two key challenges:
  the quadratic complexity of self-attention with respect to token length and the
  multi-step nature of diffusion models. To address these limitations, we present
  Jenga, a novel inference pipeline that combines dynamic attention carving with
  progressive resolution generation. Our approach leverages two key insights: (1)
  early denoising steps do not require high-resolution latents, and (2) later
  steps do not require dense attention. Jenga introduces a block-wise attention
  mechanism that dynamically selects relevant token interactions using 3D
  space-filling curves, alongside a progressive resolution strategy that
  gradually increases latent resolution during generation. Experimental results
  demonstrate that Jenga achieves substantial speedups across multiple
  state-of-the-art video diffusion models while maintaining comparable generation
  quality (8.83times speedup with 0.01\% performance drop on VBench). As a
  plug-and-play solution, Jenga enables practical, high-quality video generation
  on modern hardware by reducing inference time from minutes to seconds --
  without requiring model retraining. Code:
  https://github.com/dvlab-research/Jenga
paper_authors._id: 682fe14abafb480b9595da32
paper_authors.name: Yuechen Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:29.629Z
paper_authors._id: 6418554a0956be7233a1023e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6418554a0956be7233a1023e/9EKN0GoOpcDbvBDmAQEJf.png
paper_authors.fullname: zhang yuechen
paper_authors.type: user
paper_authors.user: julianjuaner
paper_authors._id: 682fe14abafb480b9595da33
paper_authors.name: Jinbo Xing
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:27.131Z
paper_authors._id: 64770e86d7cf39f2e937ae9a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64770e86d7cf39f2e937ae9a/pLqGg2z1KzQxCGpMwds-9.jpeg
paper_authors.fullname: Jinbo Xing
paper_authors.type: user
paper_authors.user: Doubiiu
paper_authors._id: 682fe14abafb480b9595da34
paper_authors.name: Bin Xia
paper_authors._id: 682fe14abafb480b9595da35
paper_authors.name: Shaoteng Liu
paper_authors._id: 682fe14abafb480b9595da36
paper_authors.name: Bohao Peng
paper_authors._id: 682fe14abafb480b9595da37
paper_authors.name: Xin Tao
paper_authors._id: 682fe14abafb480b9595da38
paper_authors.name: Pengfei Wan
paper_authors._id: 682fe14abafb480b9595da39
paper_authors.name: Eric Lo
paper_authors._id: 682fe14abafb480b9595da3a
paper_authors.name: Jiaya Jia
paper_title: Training-Free Efficient Video Generation via Dynamic Token Carving
paper_summary: Despite the remarkable generation quality of video Diffusion Transformer
  (DiT) models, their practical deployment is severely hindered by extensive
  computational requirements. This inefficiency stems from two key challenges:
  the quadratic complexity of self-attention with respect to token length and the
  multi-step nature of diffusion models. To address these limitations, we present
  Jenga, a novel inference pipeline that combines dynamic attention carving with
  progressive resolution generation. Our approach leverages two key insights: (1)
  early denoising steps do not require high-resolution latents, and (2) later
  steps do not require dense attention. Jenga introduces a block-wise attention
  mechanism that dynamically selects relevant token interactions using 3D
  space-filling curves, alongside a progressive resolution strategy that
  gradually increases latent resolution during generation. Experimental results
  demonstrate that Jenga achieves substantial speedups across multiple
  state-of-the-art video diffusion models while maintaining comparable generation
  quality (8.83times speedup with 0.01\% performance drop on VBench). As a
  plug-and-play solution, Jenga enables practical, high-quality video generation
  on modern hardware by reducing inference time from minutes to seconds --
  without requiring model retraining. Code:
  https://github.com/dvlab-research/Jenga
paper_ai_summary: Jenga, a novel inference pipeline for video Diffusion Transformer models, combines dynamic attention carving and progressive resolution generation to significantly speed up video generation while maintaining high quality.
paper_ai_keywords: video Diffusion Transformer (DiT) self-attention diffusion models dynamic attention carving progressive resolution generation block-wise attention 3D space-filling curves