TinySR diffusion model achieves real-time image super-resolution with 83% parameter reduction

Researchers have developed TinySR, a novel diffusion model designed for real-world image super-resolution that achieves real-time performance with significantly reduced computational cost and model size. The model employs techniques such as dynamic inter-block activation, an expansion-corrosion strategy for depth pruning, and VAE compression through channel pruning and attention removal. TinySR offers up to a 5.68x speedup and an 83% parameter reduction compared to its teacher model, TSD-SR, while maintaining high perceptual quality. AI

IMPACT This research introduces a more efficient diffusion model for image super-resolution, potentially enabling real-time applications and reducing hardware requirements.

RANK_REASON Publication of a research paper on a new AI model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• diffusion models
• OSEDiff
• real-world image super-resolution
• TinySR
• TSD-SR

• paper
• model release

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