Token Perturbation Guidance for Diffusion Models
Classifier-free guidance (CFG) has become an essential component of modern
diffusion models to enhance both generation quality and alignment with input
conditions. However, CFG requires specific training procedures and is limited
to conditional generation. To address these limitations, we propose Token
Perturbation Guidance (TPG), a novel method that applies perturbation matrices
directly to intermediate token representations within the diffusion network.
TPG employs a norm-preserving shuffling operation to provide effective and
stable guidance signals that improve generation quality without architectural
changes. As a result, TPG is training-free and agnostic to input conditions,
making it readily applicable to both conditional and unconditional generation.
We further analyze the guidance term provided by TPG and show that its effect
on sampling more closely resembles CFG compared to existing training-free
guidance techniques. Extensive experiments on SDXL and Stable Diffusion 2.1
show that TPG achieves nearly a 2times improvement in FID for unconditional
generation over the SDXL baseline, while closely matching CFG in prompt
alignment. These results establish TPG as a general, condition-agnostic
guidance method that brings CFG-like benefits to a broader class of diffusion
models. The code is available at
https://github.com/TaatiTeam/Token-Perturbation-Guidance
684bc8db3b733ba33368718c
Javad Rajabi
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2025-06-27T08:58:00.859Z
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Javad Rajabi
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Nova2001
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Soroush Mehraban
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2025-06-18T12:17:14.139Z
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Soroush Mehraban
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SoroushMehraban
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Seyedmorteza Sadat
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2025-06-13T07:38:32.584Z
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Seyedmorteza Sadat
user
msadat97
684bc8db3b733ba33368718f
Babak Taati
Token Perturbation Guidance for Diffusion Models
Classifier-free guidance (CFG) has become an essential component of modern
diffusion models to enhance both generation quality and alignment with input
conditions. However, CFG requires specific training procedures and is limited
to conditional generation. To address these limitations, we propose Token
Perturbation Guidance (TPG), a novel method that applies perturbation matrices
directly to intermediate token representations within the diffusion network.
TPG employs a norm-preserving shuffling operation to provide effective and
stable guidance signals that improve generation quality without architectural
changes. As a result, TPG is training-free and agnostic to input conditions,
making it readily applicable to both conditional and unconditional generation.
We further analyze the guidance term provided by TPG and show that its effect
on sampling more closely resembles CFG compared to existing training-free
guidance techniques. Extensive experiments on SDXL and Stable Diffusion 2.1
show that TPG achieves nearly a 2times improvement in FID for unconditional
generation over the SDXL baseline, while closely matching CFG in prompt
alignment. These results establish TPG as a general, condition-agnostic
guidance method that brings CFG-like benefits to a broader class of diffusion
models. The code is available at
https://github.com/TaatiTeam/Token-Perturbation-Guidance
Token Perturbation Guidance (TPG) enhances diffusion models with condition-agnostic, training-free guidance, similar to classifier-free guidance (CFG), without requiring architectural changes.
classifier-free guidance (CFG) Token Perturbation Guidance (TPG) perturbation matrices intermediate token representations norm-preserving shuffling FID prompt alignment SDXL Stable Diffusion 2.1