8 citations · 17 across the 8 of their papers we have counts for
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LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Thibault Castells, Hyoung-Kyu Song, Bo-Kyeong Kim +1
Latent Diffusion Models (LDMs) have emerged as powerful generative models, known for delivering remarkable results under constrained computational resources. However, deploying LDM…
EdgeFusion: On-Device Text-to-Image Generation
Thibault Castells, Hyoung-Kyu Song, Tairen Piao +6
The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application. To tackle this challenge, recent…
Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods
Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim +4
Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs. Width pruning reduces the size of projection weight mat…
BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
Bo-Kyeong Kim, Hyoung-Kyu Song, Thibault Castells +1
Text-to-image (T2I) generation with Stable Diffusion models (SDMs) involves high computing demands due to billion-scale parameters. To enhance efficiency, recent studies have reduc…
Arithmetic Intensity Balancing Convolution for Hardware-aware Efficient Block Design
Shinkook Choi, Junkyeong Choi
As deep learning advances, edge devices and lightweight neural networks are becoming more important. To reduce latency in the AI accelerator, it's essential to not only reduce FLOP…
Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs
Bo-Kyeong Kim, Shinkook Choi, Hancheol Park
Pruning effectively compresses overparameterized models. Despite the success of pruning methods for discriminative models, applying them for generative models has been relatively r…