Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models
On the map: Diffusion models → Video generation
Abstract (arXiv)
We present a neural network structure, FramePack, to train next-frame (or next-frame-section) prediction models for video generation. FramePack compresses input frame contexts with frame-wise importance so that more frames can be encoded within a fixed context length, with more important frames having longer contexts. The frame importance can be measured using time proximity, feature similarity, or hybrid metrics. The packing method allows for inference with thousands of frames and training with relatively large batch sizes. We also present drift prevention methods to address observation bias (error accumulation), including early-established endpoints, adjusted sampling orders, and discrete history representation. Ablation studies validate the effectiveness of the anti-drifting methods in both single-directional video streaming and bi-directional video generation. Finally, we show that existing video diffusion models can be finetuned with FramePack, and analyze the differences between different packing schedules.
Key facts. Cited by 143 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 61 of its 74 references that are in the map. First posted on arXiv 17 Apr 2025.
Builds on
61 of 74 references.
Closest ancestors · 19
Same direction, newest first
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- 2023NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation22 Mar
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Other references · 42
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- 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 484
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- 2024Mind the Time: Temporally-Controlled Multi-Event Video Generationcited by 40
- 2024DreamFactory: Pioneering Multi-Scene Long Video Generation with a Multi-Agent Frameworkcited by 35
- 2024StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaborationcited by 31
- 2024TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generationcited by 25
- 2024Synchronized Video Storytelling: Generating Video Narrations with Structured Storylinecited by 19
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