Personalize Anything for Free with Diffusion Transformer
Personalized image generation aims to produce images of user-specified
concepts while enabling flexible editing. Recent training-free approaches,
while exhibit higher computational efficiency than training-based methods,
struggle with identity preservation, applicability, and compatibility with
diffusion transformers (DiTs). In this paper, we uncover the untapped potential
of DiT, where simply replacing denoising tokens with those of a reference
subject achieves zero-shot subject reconstruction. This simple yet effective
feature injection technique unlocks diverse scenarios, from personalization to
image editing. Building upon this observation, we propose Personalize
Anything, a training-free framework that achieves personalized image
generation in DiT through: 1) timestep-adaptive token replacement that enforces
subject consistency via early-stage injection and enhances flexibility through
late-stage regularization, and 2) patch perturbation strategies to boost
structural diversity. Our method seamlessly supports layout-guided generation,
multi-subject personalization, and mask-controlled editing. Evaluations
demonstrate state-of-the-art performance in identity preservation and
versatility. Our work establishes new insights into DiTs while delivering a
practical paradigm for efficient personalization.
67d8de85f7809eea577c4805
Haoran Feng
claimed_verified
2025-06-25T09:19:38.789Z
65240d0ca801972b6eb12ed8
https://cdn-avatars.huggingface.co/v1/production/uploads/65240d0ca801972b6eb12ed8/hl2RAssBperb5JlgOIDvw.jpeg
Haoran Feng
user
fenghora
67d8de85f7809eea577c4806
Zehuan Huang
claimed_verified
2025-03-18T08:08:06.131Z
6375d136dee28348a9c63cbf
https://cdn-avatars.huggingface.co/v1/production/uploads/6375d136dee28348a9c63cbf/gK465HBrQWIOZ-qtHb-Vh.jpeg
zehuan-huang
user
huanngzh
67d8de85f7809eea577c4807
Lin Li
claimed_verified
2025-03-19T09:46:21.369Z
677c938c7d036e49073e2989
/avatars/6f308ddbbd8c8eba8ecffbc28f4c941b.svg
Li Lin
user
Nelipot
67d8de85f7809eea577c4808
Hairong Lv
admin_assigned
2025-03-18T09:22:23.294Z
674ded8ee50d988a4b9e108b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/8oQITwlb7AB8LeIJjooYc.png
Hairong Lv
user
lvhairong
67d8de85f7809eea577c4809
Lu Sheng
claimed_verified
2025-03-26T20:49:27.933Z
65b722dbe02a17f0f8d1cc6b
/avatars/65f20601ef9b8ebfdddadd737f9153d6.svg
Lu Sheng
user
lsheng2024
Personalize Anything for Free with Diffusion Transformer
Personalized image generation aims to produce images of user-specified
concepts while enabling flexible editing. Recent training-free approaches,
while exhibit higher computational efficiency than training-based methods,
struggle with identity preservation, applicability, and compatibility with
diffusion transformers (DiTs). In this paper, we uncover the untapped potential
of DiT, where simply replacing denoising tokens with those of a reference
subject achieves zero-shot subject reconstruction. This simple yet effective
feature injection technique unlocks diverse scenarios, from personalization to
image editing. Building upon this observation, we propose Personalize
Anything, a training-free framework that achieves personalized image
generation in DiT through: 1) timestep-adaptive token replacement that enforces
subject consistency via early-stage injection and enhances flexibility through
late-stage regularization, and 2) patch perturbation strategies to boost
structural diversity. Our method seamlessly supports layout-guided generation,
multi-subject personalization, and mask-controlled editing. Evaluations
demonstrate state-of-the-art performance in identity preservation and
versatility. Our work establishes new insights into DiTs while delivering a
practical paradigm for efficient personalization.
Personalize Anything is a training-free framework for personalized image generation using diffusion transformers, enabling subject consistency and flexibility through timestep-adaptive token replacement and patch perturbation.
denoising tokens diffusion transformers DiTs feature injection timestep-adaptive token replacement patch perturbation layout-guided generation multi-subject personalization mask-controlled editing identity preservation versatility