HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing
📰 ArXiv cs.AI
Learn how HiLo-Token compresses high-low frequency tokens for efficient image editing and reduces latency in generative AI models
Action Steps
- Apply HiLo-Token compression to reduce latency in image editing tasks
- Configure Diffusion Transformers to utilize HiLo-Token for improved efficiency
- Test the performance of HiLo-Token on various image editing benchmarks
- Compare the results with traditional convolution-based U-Nets
- Implement HiLo-Token in image editing software to enhance user experience
Who Needs to Know This
Computer vision engineers and researchers can benefit from this technique to improve the performance of image editing tools, while product managers can consider its potential to enhance user experience
Key Insight
💡 HiLo-Token reduces latency in generative AI models for image editing by compressing high-low frequency tokens
Share This
📸🔍 HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing #computerVision #AI
Key Takeaways
Learn how HiLo-Token compresses high-low frequency tokens for efficient image editing and reduces latency in generative AI models
Full Article
Title: HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing
Abstract:
arXiv:2606.13898v1 Announce Type: cross Abstract: Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and Lightroom. However, current generative AI models face significant latency challenges, which become even more pronounced when transitioning from convolution-based U-Nets to Diffusion Transformers (DiTs). In our evaluation on hundreds of representative image editing sampl
Abstract:
arXiv:2606.13898v1 Announce Type: cross Abstract: Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and Lightroom. However, current generative AI models face significant latency challenges, which become even more pronounced when transitioning from convolution-based U-Nets to Diffusion Transformers (DiTs). In our evaluation on hundreds of representative image editing sampl
DeepCamp AI