Taming Outlier Tokens in Diffusion Transformers
📰 ArXiv cs.AI
Learn to identify and manage outlier tokens in Diffusion Transformers for improved image generation
Action Steps
- Identify outlier tokens in the encoder and denoiser of Representation Autoencoder (RAE)-Diffusion Transformer (DiT) pipelines
- Analyze the role of high-norm tokens in attracting disproportionate attention
- Apply techniques to tame outlier tokens and improve model performance
- Evaluate the impact of outlier token management on image generation quality
- Compare the results with and without outlier token management to assess the improvement
Who Needs to Know This
AI engineers and researchers working on image generation models can benefit from this knowledge to optimize their models' performance
Key Insight
💡 Outlier tokens in Diffusion Transformers can negatively impact image generation, but managing them can lead to improved performance
Share This
💡 Taming outlier tokens in Diffusion Transformers can improve image generation quality
Key Takeaways
Learn to identify and manage outlier tokens in Diffusion Transformers for improved image generation
Full Article
Title: Taming Outlier Tokens in Diffusion Transformers
Abstract:
arXiv:2605.05206v1 Announce Type: cross Abstract: We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens that attract disproportionate attention while carrying limited local information, but their role in generative models remains underexplored. We show that this phenomenon appears in both the encoder and denoiser of modern Representation Autoencoder (RAE)-DiT pipelines: pr
Abstract:
arXiv:2605.05206v1 Announce Type: cross Abstract: We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens that attract disproportionate attention while carrying limited local information, but their role in generative models remains underexplored. We show that this phenomenon appears in both the encoder and denoiser of modern Representation Autoencoder (RAE)-DiT pipelines: pr
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