In this paper, we propose a novel method that only computes and caches the KVs of a small number of layers, thus significantly saving memory consumption and improving inference throughput.
This module converts the generated sequence of images into videos with smooth transitions and consistent subjects that are significantly more stable than the modules based on latent spaces only, especially in the context of long video generation.
In this work, we propose a novel Trajectory Score Matching (TSM) method that aims to solve the pseudo ground truth inconsistency problem caused by the accumulated error in Interval Score Matching (ISM) when using the Denoising Diffusion Implicit Models (DDIM) inversion process.
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
We argue that representations in AI models, particularly deep networks, are converging.
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents.
The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait.
Across three independent test datasets consisting of 1, 265 breast WSIs, 1, 946 lung WSIs, and 4, 584 liver WSIs, Tangle shows significantly better few-shot performance compared to supervised and SSL baselines.
We demonstrate that by serializing both an image and a multi-modal instruction into a textual representation it is possible to leverage LLMs to perform precise transformations of the layout and appearance of an image.
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing.