Scaling Diffusion Transformers Efficiently via μP
Diffusion Transformers have emerged as the foundation for vision generative
models, but their scalability is limited by the high cost of hyperparameter
(HP) tuning at large scales. Recently, Maximal Update Parametrization (muP)
was proposed for vanilla Transformers, which enables stable HP transfer from
small to large language models, and dramatically reduces tuning costs. However,
it remains unclear whether muP of vanilla Transformers extends to diffusion
Transformers, which differ architecturally and objectively. In this work, we
generalize standard muP to diffusion Transformers and validate its
effectiveness through large-scale experiments. First, we rigorously prove that
muP of mainstream diffusion Transformers, including DiT, U-ViT,
PixArt-alpha, and MMDiT, aligns with that of the vanilla Transformer,
enabling the direct application of existing muP methodologies. Leveraging
this result, we systematically demonstrate that DiT-muP enjoys robust HP
transferability. Notably, DiT-XL-2-muP with transferred learning rate
achieves 2.9 times faster convergence than the original DiT-XL-2. Finally, we
validate the effectiveness of muP on text-to-image generation by scaling
PixArt-alpha from 0.04B to 0.61B and MMDiT from 0.18B to 18B. In both cases,
models under muP outperform their respective baselines while requiring small
tuning cost, only 5.5% of one training run for PixArt-alpha and 3% of
consumption by human experts for MMDiT-18B. These results establish muP as a
principled and efficient framework for scaling diffusion Transformers.
682e907a24b2bb08885b94dc
Chenyu Zheng
claimed_verified
2025-05-22T07:16:10.939Z
682e8e6d007cd8c2f2cd0afd
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/az0B5USOPlLR3vq872JeN.png
Chenyu Zheng
user
ChenyuZheng
682e907a24b2bb08885b94dd
Xinyu Zhang
682e907a24b2bb08885b94de
Rongzhen Wang
682e907a24b2bb08885b94df
Wei Huang
682e907a24b2bb08885b94e0
Zhi Tian
682e907a24b2bb08885b94e1
Weilin Huang
682e907a24b2bb08885b94e2
Jun Zhu
682e907a24b2bb08885b94e3
Chongxuan Li
Scaling Diffusion Transformers Efficiently via μP
Diffusion Transformers have emerged as the foundation for vision generative
models, but their scalability is limited by the high cost of hyperparameter
(HP) tuning at large scales. Recently, Maximal Update Parametrization (muP)
was proposed for vanilla Transformers, which enables stable HP transfer from
small to large language models, and dramatically reduces tuning costs. However,
it remains unclear whether muP of vanilla Transformers extends to diffusion
Transformers, which differ architecturally and objectively. In this work, we
generalize standard muP to diffusion Transformers and validate its
effectiveness through large-scale experiments. First, we rigorously prove that
muP of mainstream diffusion Transformers, including DiT, U-ViT,
PixArt-alpha, and MMDiT, aligns with that of the vanilla Transformer,
enabling the direct application of existing muP methodologies. Leveraging
this result, we systematically demonstrate that DiT-muP enjoys robust HP
transferability. Notably, DiT-XL-2-muP with transferred learning rate
achieves 2.9 times faster convergence than the original DiT-XL-2. Finally, we
validate the effectiveness of muP on text-to-image generation by scaling
PixArt-alpha from 0.04B to 0.61B and MMDiT from 0.18B to 18B. In both cases,
models under muP outperform their respective baselines while requiring small
tuning cost, only 5.5% of one training run for PixArt-alpha and 3% of
consumption by human experts for MMDiT-18B. These results establish muP as a
principled and efficient framework for scaling diffusion Transformers.
Maximal Update Parametrization (μP) is extended to diffusion Transformers, demonstrating efficient hyperparameter transferability and reduced tuning costs across various models and tasks.
Diffusion Transformers Maximal Update Parametrization μP hyperparameter tuning DiT U-ViT PixArt-α MMDiT text-to-image generation transfer learning convergence training run