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Found 9 papers, 4 papers with code
LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Latent Diffusion Models (LDMs) have emerged as powerful generative models, known for delivering remarkable results under constrained computational resources.
EdgeFusion: On-Device Text-to-Image Generation
The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application.
SNP: Structured Neuron-level Pruning to Preserve Attention Scores
Conventional pruning approaches can only compress and accelerate the MSA module using head pruning, although the head is not an atomic unit.
Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods
Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs.
BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
Text-to-image (T2I) generation with Stable Diffusion models (SDMs) involves high computing demands due to billion-scale parameters.
DreamBooth Personalized Generation
Image-to-Image Translation
Arithmetic Intensity Balancing Convolution for Hardware-aware Efficient Block Design
As deep learning advances, edge devices and lightweight neural networks are becoming more important.
A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation
Virtual humans have gained considerable attention in numerous industries, e. g., entertainment and e-commerce.
Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs
Pruning effectively compresses overparameterized models.
Emotional Voice Conversion using Multitask Learning with Text-to-speech
The embedding space of seq2seq-based TTS has abundant information on the text.
