6 citations · 23 across the 16 of their papers we have counts for
17 papers
Meta-CoT: Enhancing Granularity and Generalization in Image Editing
Shiyi Zhang, Yiji Cheng, Tiankai Hang +8
Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) proc…
Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling
Ximing Xing, Ziteng Xue, Zhenxi Li +8
Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on…
Generative Visual Chain-of-Thought for Image Editing
Zijin Yin, Tiankai Hang, Yiji Cheng +9
Existing image editing methods struggle to perceive where to edit, especially under complex scenes and nuanced spatial instructions. To address this issue, we propose Generative Vi…
HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising
Kai Zou, Dian Zheng, Hongbo Liu +3
Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity whi…
Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing
Runze He, Yiji Cheng, Tiankai Hang +11
In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful executi…
TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
Yu Xu, Hongbin Yan, Juan Cao +11
Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise betwe…
PromptEnhancer: A Simple Approach to Enhance Text-to-Image Models via Chain-of-Thought Prompt Rewriting
Linqing Wang, Ximing Xing, Yiji Cheng +13
Recent advancements in text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in generating high-fidelity images. However, these models often struggle to fa…
Incorporating Pre-trained Diffusion Models in Solving the Schrödinger Bridge Problem
Zhicong Tang, Tiankai Hang, Shuyang Gu +2
This paper aims to unify Score-based Generative Models (SGMs), also known as Diffusion models, and the Schrödinger Bridge (SB) problem through three reparameterization techniques:…
Fast Autoregressive Models for Continuous Latent Generation
Tiankai Hang, Jianmin Bao, Fangyun Wei +1
Autoregressive models have demonstrated remarkable success in sequential data generation, particularly in NLP, but their extension to continuous-domain image generation presents si…
Improved Noise Schedule for Diffusion Training
Tiankai Hang, Shuyang Gu, Xin Geng +1
Diffusion models have emerged as the de facto choice for generating high-quality visual signals across various domains. However, training a single model to predict noise across var…
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization
Zhanhao Liang, Yuhui Yuan, Shuyang Gu +5
Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct preference optimization (DPO), wh…
Simplified Diffusion Schrödinger Bridge
Zhicong Tang, Tiankai Hang, Shuyang Gu +2
This paper introduces a novel theoretical simplification of the Diffusion Schrödinger Bridge (DSB) that facilitates its unification with Score-based Generative Models (SGMs), addre…
CCA: Collaborative Competitive Agents for Image Editing
Tiankai Hang, Shuyang Gu, Dong Chen +2
This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to exec…
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
Zigang Geng, Binxin Yang, Tiankai Hang +8
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge…
Efficient Diffusion Training via Min-SNR Weighting Strategy
Tiankai Hang, Shuyang Gu, Chen Li +5
Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence. In this paper, we discovered th…
Language-Guided Face Animation by Recurrent StyleGAN-based Generator
Tiankai Hang, Huan Yang, Bei Liu +3
Recent works on language-guided image manipulation have shown great power of language in providing rich semantics, especially for face images. However, the other natural informatio…
Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions
Hongwei Xue, Tiankai Hang, Yanhong Zeng +5
We study joint video and language (VL) pre-training to enable cross-modality learning and benefit plentiful downstream VL tasks. Existing works either extract low-quality video fea…