SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Enze Xie, Junsong Chen, Yuyang Zhao, Jincheng YU, Ligeng Zhu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, Bingchen Liu, Daquan Zhou, Song Han

TL;DR: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Abstract: 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.

Lay Summary:

Creating high-quality images from text descriptions typically requires massive computing power, putting advanced AI image generation out of reach for many researchers and developers.

We developed SANA-1.5, a smarter AI system that achieves top-tier image generation while being dramatically more efficient. Our key breakthroughs include:

1. A "growing" training method that builds larger models using 60% less computing power
2. A compression technique that shrinks models without losing quality
3. A clever sampling trick that lets smaller models temporarily boost their capabilities

These innovations allow SANA-1.5 to match or exceed the performance of systems like Stable Diffusion XL while being more accessible. On standard tests, it achieves record-breaking accuracy in matching images to text descriptions (80% alignment score when using our sampling boost).

By making advanced image generation more efficient, SANA-1.5 helps democratize AI creativity - enabling more researchers to experiment with the technology and developers to integrate it into applications without needing expensive hardware.

Link To Code: https://github.com/NVlabs/Sana

Primary Area: Deep Learning->Generative Models and Autoencoders

Keywords: Diffusion Model, Inference Scaling, Linear Attention

Submission Number: 1929