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![AINativeF Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::1795402815298486272.png) AI Native Foundation [@AINativeF](/creator/twitter/AINativeF) on x 2108 followers
Created: 2025-07-25 03:00:16 UTC

XX. PUSA V1.0: Surpassing Wan-I2V with $XXX Training Cost by Vectorized Timestep Adaptation

🔑 Keywords: Video Diffusion Models, Temporal Modeling, Vectorized Timestep Adaptation, Zero-shot Multi-task Capabilities, Text-to-Video Generation

💡 Category: Generative Models

🌟 Research Objective:
   - The paper aims to enhance video diffusion models using a novel vectorized timestep adaptation approach, known as Pusa, to improve video generation efficiency and versatility.

🛠️ Research Methods:
   - The approach leverages vectorized timestep adaptation (VTA) within the video diffusion framework, enabling fine-grained temporal control while preserving the capabilities of the base model.

💬 Research Conclusions:
   - Pusa achieves significant improvements in video generation efficiency, outperforming existing models such as Wan-I2V-14B with remarkably low training costs and dataset size, and demonstrates versatile zero-shot multi-task capabilities, including text-to-video generation, without task-specific training.

👉 Paper link:

![](https://pbs.twimg.com/media/Gwq-RKhXwAAiCrU.jpg)

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**Related Topics**
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[Post Link](https://x.com/AINativeF/status/1948579020863352890)