🏢 National University of Singapore

CLEAR: Conv-Like Linearization boosts pre-trained Diffusion Transformers, achieving 6.3x faster 8K image generation with minimal quality loss.

TinyFusion, a novel learnable depth pruning method, crafts efficient shallow diffusion transformers with superior post-fine-tuning performance, achieving a 2x speedup with less than 7% of the original…

Collaborative Decoding (CoDe) dramatically boosts visual auto-regressive model efficiency.

OminiControl: A minimal, universal framework efficiently integrates image conditions into diffusion transformers, enabling diverse and precise control over image generation.

AnchorAttention enhances long-context LLMs by mitigating BFloat16’s disruptive effects on RoPE, improving performance and speeding up training.

Boost large language model training speed by 51% with Vocabulary Parallelism, a novel technique that balances computation and memory usage across pipeline stages.

GenXD: A unified model generating high-quality 3D & 4D scenes from any number of images, advancing the field of dynamic scene generation.