Efficient Training of Diffusion Models with Token Routing (TREAD)
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fzliu
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Summary
The article discusses TREAD, a novel method for improving the training efficiency and generative performance of diffusion models, which are widely used for visual generation. Unlike existing approaches that trade off performance for computational cost, TREAD introduces token routing to enhance both aspects simultaneously. It achieves significant speedups and competitive performance on benchmarks like ImageNet-256, without requiring architectural changes or additional parameters.
Key quotes
· 4 pulledTREAD reduces computational cost and simultaneously boosts model performance on the standard ImageNet-256 benchmark.
Our method is not limited to the common transformer-based model - it can also be applied to state-space models.
TREAD achieves a convergence speedup of 14x at 400K training iterations compared to DiT.
We achieve a competitive FID of 2.09 in a guided and 3.93 in an unguided setting.
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