SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute
  in Linear Diffusion Transformer
Authors: name: Enze Xie, name: Junsong Chen, name: Yuyang Zhao, name: Jincheng Yu, name: Ligeng Zhu, name: Yujun Lin, name: Zhekai Zhang, name: Muyang Li, name: Junyu Chen, name: Han Cai, name: Bingchen Liu, name: Daquan Zhou, name: Song Han

This paper presents SANA-1.5, a linear Diffusion Transformer for efficient
scaling in text-to-image generation. Building upon SANA-1.0, we introduce three
key innovations: (1) Efficient Training Scaling: A depth-growth paradigm that
enables scaling from 1.6B to 4.8B parameters with significantly reduced
computational resources, combined with a memory-efficient 8-bit optimizer. (2)
Model Depth Pruning: A block importance analysis technique for efficient model
compression to arbitrary sizes with minimal quality loss. (3) Inference-time
Scaling: A repeated sampling strategy that trades computation for model
capacity, enabling smaller models to match larger model quality at inference
time. Through these strategies, SANA-1.5 achieves a text-image alignment score
of 0.72 on GenEval, which can be further improved to 0.80 through inference
scaling, establishing a new SoTA on GenEval benchmark. These innovations enable
efficient model scaling across different compute budgets while maintaining high
quality, making high-quality image generation more accessible.