1 citations · 2 across the 22 of their papers we have counts for
24 papers
Golden Anchor for Video Generation
Jinyuan Xu, Shitong Shao, Lichen Bai +4
Video generation supports digital content creation, with applications in virtual avatars, AI-driven filmmaking, interactive game engines, and world models. State-of-the-art systems…
Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation
Zekai Zhang, Jiahao Li, Jie Zhang +18
While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or dependent on up-to-date knowl…
Qwen-Image-2.0-RL Technical Report
Yixian Xu, Kaiyuan Gao, Yuxiang Chen +25
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual qu…
Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation
Niantong Li, Guangzheng Hu, Weixu Qiao +35
Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no…
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
Jie Zhang, Xiaoyue Chen, Anzhe Chen +36
We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically ground…
Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation
Tianhe Wu, Kun Yan, Zikai Zhou +23
Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially reducing inference overhead while enabli…
LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention
Shitong Shao, Zikai Zhou, Haopeng Li +4
Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottleneck. In this work, we propo…
Qwen-Image-VAE-2.0 Technical Report
Zekai Zhang, Deqing Li, Kuan Cao +27
We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To a…
Qwen-Image-2.0 Technical Report
Bing Zhao, Chenfei Wu, Deqing Li +72
We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…
Exploring Data-Free LoRA Transferability for Video Diffusion Models
Yuchen Wang, Wenliang Zhong, Lichen Bai +6
Video diffusion models leveraging step distillation or causal distillation have achieved remarkable performance. However, adapting existing LoRAs to these variants remains a critic…
Reflective Flow Sampling Enhancement
Zikai Zhou, Muyao Wang, Shitong Shao +4
The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, text-to-image diffusion models trained with flow matching algorithms, su…
Guidance Matters: Rethinking the Evaluation Pitfall for Text-to-Image Generation
Dian Xie, Shitong Shao, Lichen Bai +5
Classifier-free guidance (CFG) has helped diffusion models achieve great conditional generation in various fields. Recently, more diffusion guidance methods have emerged with impro…
PISA: Piecewise Sparse Attention Is Wiser for Efficient Diffusion Transformers
Haopeng Li, Shitong Shao, Wenliang Zhong +4
Diffusion Transformers are fundamental for video and image generation, but their efficiency is bottlenecked by the quadratic complexity of attention. While block sparse attention a…
Optimizing Few-Step Generation with Adaptive Matching Distillation
Lichen Bai, Zikai Zhou, Shitong Shao +5
Distribution Matching Distillation (DMD) is a powerful acceleration paradigm, yet its stability is often compromised in Forbidden Zone, regions where the real teacher provides unre…
Accelerating Diffusion Model Training under Minimal Budgets: A Condensation-Based Perspective
Rui Huang, Shitong Shao, Zikai Zhou +6
Diffusion models have achieved remarkable performance on a wide range of generative tasks, yet training them from scratch is notoriously resource-intensive, typically requiring mil…
Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction
Xinlong Zhai, Chunchen Wang, Ruijia Wang +7
Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application. There are two perspectives of input data widely use…
CoRe^2: Collect, Reflect and Refine to Generate Better and Faster
Shitong Shao, Zikai Zhou, Dian Xie +4
Making text-to-image (T2I) generative model sample both fast and well represents a promising research direction. Previous studies have typically focused on either enhancing the vis…
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits
Zikai Zhou, Qizheng Zhang, Hermann Kumbong +1
Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning (PEFT) methods like…
Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection
Lichen Bai, Shitong Shao, Zikai Zhou +4
Diffusion models, the most popular generative paradigm so far, can inject conditional information into the generation path to guide the latent towards desired directions. However,…
Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer
Shitong Shao, Zikai Zhou, Tian Ye +3
Text-to-image diffusion models (DMs) develop at an unprecedented pace, supported by thorough theoretical exploration and empirical analysis. Unfortunately, the discrepancy between…
Golden Noise for Diffusion Models: A Learning Framework
Zikai Zhou, Shitong Shao, Lichen Bai +4
Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some nois…
IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis
Shitong Shao, Zikai Zhou, Lichen Bai +2
The multi-step sampling mechanism, a key feature of visual diffusion models, has significant potential to replicate the success of OpenAI's Strawberry in enhancing performance by i…
Elucidating the Design Space of Dataset Condensation
Shitong Shao, Zikai Zhou, Huanran Chen +1
Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity…
Rethinking Centered Kernel Alignment in Knowledge Distillation
Zikai Zhou, Yunhang Shen, Shitong Shao +2
Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches…