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Found 8 papers, 3 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.
Shortened LLaMA: A Simple Depth Pruning for Large Language Models
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
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.
Semi-supervised Disentanglement with Independent Vector Variational Autoencoders
We aim to separate the generative factors of data into two latent vectors in a variational autoencoder.
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition
Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator.
