2 citations · 3 across the 19 of their papers we have counts for
22 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…
G-LoRA: Organizing Reward-Weighted Video Data with Gradient-Guided Grouped LoRA
Jia Song, Wenhow Li, Lichen Bai +2
Post-training foundation video models on heterogeneous reward-weighted data usually assume that all data categories induce compatible updates. This assumption is fragile when categ…
MaineCoon: Pursuing A Real-Time Audio-Visual Social World Model
Lichen Bai, Tianhao Zhang, Shitong Shao +14
As an increasing majority of global video content is consumed on social platforms for interactive social purposes, video generation models built for social worlds are important but…
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…
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…
Efficient Video Diffusion Models: Advancements and Challenges
Shitong Shao, Lichen Bai, Pengfei Wan +2
Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative video synthesis, but their practical deployment remains constrained by severe inferenc…
CRAFT: Aligning Diffusion Models with Fine-Tuning Is Easier Than You Think
Zening Sun, Zhengpeng Xie, Lichen Bai +3
Aligning Diffusion models has achieved remarkable breakthroughs in generating high-quality, human preference-aligned images. Existing techniques, such as supervised fine-tuning (SF…
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…
Multiphysics Bench: Benchmarking and Investigating Scientific Machine Learning for Multiphysics PDEs
Changfan Yang, Lichen Bai, Yinpeng Wang +2
Solving partial differential equations (PDEs) with machine learning has recently attracted great attention, as PDEs are fundamental tools for modeling real-world systems that range…
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…
Weak-to-Strong Diffusion with Reflection
Lichen Bai, Masashi Sugiyama, Zeke Xie
The goal of diffusion generative models is to align the learned distribution with the real data distribution through gradient score matching. However, inherent limitations in train…
Learning from Ambiguous Data with Hard Labels
Zeke Xie, Zheng He, Nan Lu +5
Real-world data often contains intrinsic ambiguity that the common single-hard-label annotation paradigm ignores. Standard training using ambiguous data with these hard labels may…
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…
Alignment of Diffusion Models: Fundamentals, Challenges, and Future
Buhua Liu, Shitong Shao, Bao Li +6
Diffusion models have emerged as the leading paradigm in generative modeling, excelling in various applications. Despite their success, these models often misalign with human inten…
Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization
Zipeng Qi, Lichen Bai, Haoyi Xiong +1
Diffusion models that can generate high-quality data from randomly sampled Gaussian noises have become the mainstream generative method in both academia and industry. Are randomly…