6 papers
Monkey King Bang: A Unified Scientific Multimodal Foundation Model
Hesen Chen, Xinyu Su, Xiaomeng Yang +11
Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition. Existing systems are either specia…
DiverseDiT: Towards Diverse Representation Learning in Diffusion Transformers
Mengping Yang, Zhiyu Tan, Binglei Li +3
Recent breakthroughs in Diffusion Transformers (DiTs) have revolutionized the field of visual synthesis due to their superior scalability. To facilitate DiTs' capability of capturi…
Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing
Hao Yang, Zhiyu Tan, Jia Gong +7
We present Omni-Video 2, a scalable and computationally efficient model that connects pretrained multimodal large-language models (MLLMs) with video diffusion models for unified vi…
SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models
Hesen Chen, Junyan Wang, Zhiyu Tan +1
Modern diffusion models encounter a fundamental trade-off between training efficiency and generation quality. While existing representation alignment methods, such as REPA, acceler…
E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models
Zhiyu Tan, WenXu Qian, Hesen Chen +3
Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remarkable success across various div…
Raccoon: Multi-stage Diffusion Training with Coarse-to-Fine Curating Videos
Zhiyu Tan, Junyan Wang, Hao Yang +4
Text-to-video generation has demonstrated promising progress with the advent of diffusion models, yet existing approaches are limited by dataset quality and computational resources…