activity
20232026
most citedPixArt-: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

8 citations · 19 across the 12 of their papers we have counts for

collaborators

11 papers

cs.CV2026

Object-Aware Background-Controlled Editing via Weighted Velocity Guidance

Wuji Wang, Yue Wu, Chenhao Yi +1

Training-free image editing steers diffusion or flow-matching generative models at inference time by modifying prompt-conditioned denoising velocities. Existing velocity-based edit…

cs.CV2026

CARE-Edit: Condition-Aware Routing of Experts for Contextual Image Editing

Yucheng Wang, Zedong Wang, Yuetong Wu +2

Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogeneous demands (e.g., local vs gl…

cs.CV2024★ 1 cited

T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation

Kaiyue Sun, Kaiyi Huang, Xian Liu +4

Text-to-video (T2V) generative models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored.…

cs.CV2024★ 3 cited

DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis

Yao Teng, Yue Wu, Han Shi +5

Diffusion models have achieved great success in image generation, with the backbone evolving from U-Net to Vision Transformers. However, the computational cost of Transformers is q…

cs.CV2024

Editing Massive Concepts in Text-to-Image Diffusion Models

Tianwei Xiong, Yue Wu, Enze Xie +2

Text-to-image diffusion models suffer from the risk of generating outdated, copyrighted, incorrect, and biased content. While previous methods have mitigated the issues on a small…

cs.CV2024

PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Junsong Chen, Chongjian Ge, Enze Xie +7

In this paper, we introduce PixArt-Σ, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-Σrepresents a significant advancement over…

cs.AI2024★ 3 cited

A Survey of Reasoning with Foundation Models

Jiankai Sun, Chuanyang Zheng, Enze Xie +31

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It…

cs.CV2024★ 1 cited

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…

cs.CV2023

Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation

Shentong Mo, Enze Xie, Yue Wu +3

Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3…

cs.CV2023★ 3 cited

Drag-A-Video: Non-rigid Video Editing with Point-based Interaction

Yao Teng, Enze Xie, Yue Wu +3

Video editing is a challenging task that requires manipulating videos on both the spatial and temporal dimensions. Existing methods for video editing mainly focus on changing the a…

cs.CV2023★ 8 cited

PixArt-: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Junsong Chen, Jincheng Yu, Chongjian Ge +8

The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community…