Summary of Faster Image2video Generation: a Closer Look at Clip Image Embedding’s Impact on Spatio-temporal Cross-attentions, by Ashkan Taghipour et al.
Faster Image2Video Generation: A Closer Look at CLIP Image Embedding’s Impact on Spatio-Temporal Cross-Attentions
by Ashkan Taghipour, Morteza Ghahremani, Mohammed Bennamoun, Aref Miri Rekavandi, Zinuo Li, Hamid Laga, Farid Boussaid
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the impact of CLIP image embeddings on video generation quality and computational efficiency within the Stable Video Diffusion (SVD) framework. Research shows that while CLIP embeddings are crucial for aesthetic quality, they do not significantly affect subject and background consistency in generated videos. Moreover, the computationally expensive cross-attention mechanism can be replaced by a simpler linear layer, which is computed only once at the first diffusion inference step and reused throughout the process to enhance efficiency. The study introduces the VCUT approach, optimized for efficiency within the SVD architecture, eliminating temporal cross-attention and replacing spatial cross-attention with a one-time computed linear layer, significantly reducing computational load. This leads to a reduction of up to 322T Multiple-Accumulate Operations (MACs) per video and a decrease in model parameters by up to 50M, achieving a 20% reduction in latency compared to the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how CLIP image embeddings affect video generation. They found that while these embeddings are important for making videos look nice, they don’t really change what’s happening in the background and subject of the video. They also discovered that a part of the process called cross-attention is taking up too much computer power, so they came up with a way to make it faster. This new way, called VCUT, makes the video generation process more efficient by reducing the amount of work the computer has to do. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Inference
