Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design.
The key insight driving QServe is that the efficiency of LLM serving on GPUs is critically influenced by operations on low-throughput CUDA cores.
This underscores the potential of DocRes across a broader spectrum of document image restoration tasks.
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing.
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime.
Ranked #1 on Feature Upsampling on ImageNet
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation.
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
To this end, we design a two-stage framework that draws a concept image first, followed by a reference-informed 3D modeling stage.
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.
A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks.