2 citations · 3 across the 12 of their papers we have counts for
4 papers · 1 filter
I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow
Ruoyi Du, Dongyang Liu, Le Zhuo +4
Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models. However, progress…
Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining
Dongyang Liu, Shitian Zhao, Le Zhuo +7
We present Lumina-mGPT, a family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic imag…
VEnhancer: Generative Space-Time Enhancement for Video Generation
Jingwen He, Tianfan Xue, Dongyang Liu +6
We present VEnhancer, a generative space-time enhancement framework that improves the existing text-to-video results by adding more details in spatial domain and synthetic detailed…
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Le Zhuo, Ruoyi Du, Han Xiao +19
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and vi…