SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute
  in Linear Diffusion Transformer
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.
679d7fae1f4b90cfa7b74d0b
Enze Xie
679d7fae1f4b90cfa7b74d0c
Junsong Chen
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2025-03-20T10:54:32.699Z
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Chen
user
Lawrence-cj
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Yuyang Zhao
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Jincheng Yu
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Ligeng Zhu
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Yujun Lin
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Zhekai Zhang
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Muyang Li
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Junyu Chen
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Han Cai
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Bingchen Liu
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Daquan Zhou
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Song Han
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute
  in Linear Diffusion Transformer
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.
SANA-1.5, a linear Diffusion Transformer, achieves state-of-the-art text-to-image generation with efficient scaling through depth growth, model pruning, and inference-time scaling.
Diffusion Transformer depth-growth paradigm memory-efficient optimizer model depth pruning block importance analysis repeated sampling strategy text-image alignment GenEval benchmark