5 papers
Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention
Wenhu Zhang, Yiming Wu, Huanyu Wang +6
Diffusion Language Models (DLMs) enable globally coherent, bidirectional, and controllable text generation, offering advantages over traditional autoregressive LLMs, while scaling…
ChatDiT: A Training-Free Baseline for Task-Agnostic Free-Form Chatting with Diffusion Transformers
Lianghua Huang, Wei Wang, Zhi-Fan Wu +7
Recent research arXiv:2410.15027 arXiv:2410.23775 has highlighted the inherent in-context generation capabilities of pretrained diffusion transformers (DiTs), enabling them to seam…
IDEA-Bench: How Far are Generative Models from Professional Designing?
Chen Liang, Lianghua Huang, Jingwu Fang +7
Real-world design tasks - such as picture book creation, film storyboard development using character sets, photo retouching, visual effects, and font transfer - are highly diverse…
In-Context LoRA for Diffusion Transformers
Lianghua Huang, Wei Wang, Zhi-Fan Wu +6
Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. Ho…
Group Diffusion Transformers are Unsupervised Multitask Learners
Lianghua Huang, Wei Wang, Zhi-Fan Wu +6
While large language models (LLMs) have revolutionized natural language processing with their task-agnostic capabilities, visual generation tasks such as image translation, style t…