37 citations · 85 across the 9 of their papers we have counts for
9 papers
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability
Xuelin Qian, Yu Wang, Simian Luo +9
Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. In this paper, we extend auto-regressive models to 3D…
PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models
Junsong Chen, Yue Wu, Simian Luo +5
This technical report introduces PIXART-δ, a text-to-image synthesis framework that integrates the Latent Consistency Model (LCM) and ControlNet into the advanced PIXART-α model. P…
LCM-LoRA: A Universal Stable-Diffusion Acceleration Module
Simian Luo, Yiqin Tan, Suraj Patil +6
Latent Consistency Models (LCMs) have achieved impressive performance in accelerating text-to-image generative tasks, producing high-quality images with minimal inference steps. LC…
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
Simian Luo, Yiqin Tan, Longbo Huang +2
Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and lea…
Large Trajectory Models are Scalable Motion Predictors and Planners
Qiao Sun, Shiduo Zhang, Danjiao Ma +7
Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding div…
Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models
Simian Luo, Chuanhao Yan, Chenxu Hu +1
The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. H…
ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory
Chenxu Hu, Jie Fu, Chenzhuang Du +3
Large language models (LLMs) with memory are computationally universal. However, mainstream LLMs are not taking full advantage of memory, and the designs are heavily influenced by…
Learning Versatile 3D Shape Generation with Improved AR Models
Simian Luo, Xuelin Qian, Yanwei Fu +5
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the…
QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation
Yuxin Hong, Xuelin Qian, Simian Luo +2
This paper studies the task of conditional Human Motion Animation (cHMA). Given a source image and a driving video, the model should animate the new frame sequence, in which the pe…