title: Personalize Anything for Free with Diffusion Transformer
summary: 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.
paper_authors._id: 67d8de85f7809eea577c4805
paper_authors.name: Haoran Feng
paper_authors._id: 67d8de85f7809eea577c4806
paper_authors.name: Zehuan Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T08:08:06.131Z
paper_authors._id: 6375d136dee28348a9c63cbf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6375d136dee28348a9c63cbf/gK465HBrQWIOZ-qtHb-Vh.jpeg
paper_authors.fullname: zehuan-huang
paper_authors.type: user
paper_authors.user: huanngzh
paper_authors._id: 67d8de85f7809eea577c4807
paper_authors.name: Lin Li
paper_authors._id: 67d8de85f7809eea577c4808
paper_authors.name: Hairong Lv
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:22:23.294Z
paper_authors._id: 674ded8ee50d988a4b9e108b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/8oQITwlb7AB8LeIJjooYc.png
paper_authors.fullname: Hairong Lv
paper_authors.type: user
paper_authors.user: lvhairong
paper_authors._id: 67d8de85f7809eea577c4809
paper_authors.name: Lu Sheng
paper_title: Personalize Anything for Free with Diffusion Transformer
paper_summary: 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.
paper_ai_keywords: diffusion transformers (DiTs) denoising tokens zero-shot subject reconstruction timestep-adaptive token replacement early-stage injection late-stage regularization patch perturbation strategies layout-guided generation multi-subject personalization mask-controlled editing identity preservation versatility