19 papers
From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors
Liangbing Zhao, Le Zhuo, Sayak Paul +2
Instruction-based image editing has achieved remarkable success in semantic alignment, yet state-of-the-art models frequently fail to render physically plausible results when editi…
VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning
Zhong-Yu Li, Ruoyi Du, Juncheng Yan +6
Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models…
PICABench: How Far Are We from Physically Realistic Image Editing?
Yuandong Pu, Le Zhuo, Songhao Han +10
Image editing has achieved remarkable progress recently. Modern editing models could already follow complex instructions to manipulate the original content. However, beyond complet…
ProteinAE: Protein Diffusion Autoencoders for Structure Encoding
Shaoning Li, Le Zhuo, Yusong Wang +5
Developing effective representations of protein structures is essential for advancing protein science, particularly for protein generative modeling. Current approaches often grappl…
Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding
Yi Xin, Qi Qin, Siqi Luo +29
We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by util…
TIDE : Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation
Victor Shea-Jay Huang, Le Zhuo, Yi Xin +6
Diffusion Transformers (DiTs) are a powerful yet underexplored class of generative models compared to U-Net-based diffusion architectures. We propose TIDE-Temporal-aware sparse aut…