TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
📰 ArXiv cs.AI
Learn how TOC-SR achieves efficient image super-resolution using compact diffusion models, reducing computational costs and improving practical deployment
Action Steps
- Build a compact diffusion backbone using a sixteen-channel latent diffusion model
- Apply task-optimal compact diffusion for image super-resolution
- Configure the TOC-SR framework for efficient one-step super-resolution model deployment
- Test the performance of TOC-SR on benchmark datasets
- Compare the results with existing diffusion models for image super-resolution
Who Needs to Know This
Computer vision engineers and researchers can benefit from this work to improve image super-resolution tasks, while software engineers can apply the efficient one-step model deployment
Key Insight
💡 Compact diffusion models can achieve efficient image super-resolution with reduced computational costs
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📸💻 TOC-SR: Efficient image super-resolution using compact diffusion models! 🚀
Key Takeaways
Learn how TOC-SR achieves efficient image super-resolution using compact diffusion models, reducing computational costs and improving practical deployment
Full Article
Title: TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
Abstract:
arXiv:2605.02767v1 Announce Type: cross Abstract: Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-resolution. However, their large model size and iterative sampling procedures make them computationally expensive for practical deployment. In this work, we present TOC-SR, a framework for building efficient one-step super-resolution models by first discovering a compact diffusion backbone. Starting from a sixteen-channel latent diffusion m
Abstract:
arXiv:2605.02767v1 Announce Type: cross Abstract: Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-resolution. However, their large model size and iterative sampling procedures make them computationally expensive for practical deployment. In this work, we present TOC-SR, a framework for building efficient one-step super-resolution models by first discovering a compact diffusion backbone. Starting from a sixteen-channel latent diffusion m
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