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Found 6 papers, 4 papers with code
Unveiling Redundancy in Diffusion Transformers (DiTs): A Systematic Study
Our experimental analysis reveals substantial variations in the distribution of redundancy across diffusion steps among different DiT models.
xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism
Parallel inference is essential for real-time DiTs deployments, but relying on a single parallel method is impractical due to poor scalability at large scales.
PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference
This paper presents PipeFusion, an innovative parallel methodology to tackle the high latency issues associated with generating high-resolution images using diffusion transformers (DiTs) models.
Anatomizing Deep Learning Inference in Web Browsers
The gap on mobile CPU and mobile GPU is 15. 8 times and 7. 8 times, respectively.
BiBench: Benchmarking and Analyzing Network Binarization
Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width.
Informative Sample-Aware Proxy for Deep Metric Learning
Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies.
