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cs.CV2024
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…
cs.CV2024★ 1 cited
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Peng Gao, Le Zhuo, Dongyang Liu +17
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lac…
cs.CV2024
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…