title: InstantCharacter: Personalize Any Characters with a Scalable Diffusion
    Transformer Framework
summary: Current learning-based subject customization approaches, predominantly
  relying on U-Net architectures, suffer from limited generalization ability and
  compromised image quality. Meanwhile, optimization-based methods require
  subject-specific fine-tuning, which inevitably degrades textual
  controllability. To address these challenges, we propose InstantCharacter, a
  scalable framework for character customization built upon a foundation
  diffusion transformer. InstantCharacter demonstrates three fundamental
  advantages: first, it achieves open-domain personalization across diverse
  character appearances, poses, and styles while maintaining high-fidelity
  results. Second, the framework introduces a scalable adapter with stacked
  transformer encoders, which effectively processes open-domain character
  features and seamlessly interacts with the latent space of modern diffusion
  transformers. Third, to effectively train the framework, we construct a
  large-scale character dataset containing 10-million-level samples. The dataset
  is systematically organized into paired (multi-view character) and unpaired
  (text-image combinations) subsets. This dual-data structure enables
  simultaneous optimization of identity consistency and textual editability
  through distinct learning pathways. Qualitative experiments demonstrate the
  advanced capabilities of InstantCharacter in generating high-fidelity,
  text-controllable, and character-consistent images, setting a new benchmark for
  character-driven image generation. Our source code is available at
  https://github.com/Tencent/InstantCharacter.
paper_authors._id: 6801f92ac9953c32ecda5c12
paper_authors.name: Jiale Tao
paper_authors._id: 6801f92ac9953c32ecda5c13
paper_authors.name: Yanbing Zhang
paper_authors._id: 6801f92ac9953c32ecda5c14
paper_authors.name: Qixun Wang
paper_authors._id: 6801f92ac9953c32ecda5c15
paper_authors.name: Yiji Cheng
paper_authors._id: 6801f92ac9953c32ecda5c16
paper_authors.name: Haofan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:38:16.438Z
paper_authors._id: 637745113a63a2983ffbde13
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 6801f92ac9953c32ecda5c17
paper_authors.name: Xu Bai
paper_authors._id: 6801f92ac9953c32ecda5c18
paper_authors.name: Zhengguang Zhou
paper_authors._id: 6801f92ac9953c32ecda5c19
paper_authors.name: Ruihuang Li
paper_authors._id: 6801f92ac9953c32ecda5c1a
paper_authors.name: Linqing Wang
paper_authors._id: 6801f92ac9953c32ecda5c1b
paper_authors.name: Chunyu Wang
paper_authors._id: 6801f92ac9953c32ecda5c1c
paper_authors.name: Qin Lin
paper_authors._id: 6801f92ac9953c32ecda5c1d
paper_authors.name: Qinglin Lu
paper_title: InstantCharacter: Personalize Any Characters with a Scalable Diffusion
    Transformer Framework
paper_summary: Current learning-based subject customization approaches, predominantly
  relying on U-Net architectures, suffer from limited generalization ability and
  compromised image quality. Meanwhile, optimization-based methods require
  subject-specific fine-tuning, which inevitably degrades textual
  controllability. To address these challenges, we propose InstantCharacter, a
  scalable framework for character customization built upon a foundation
  diffusion transformer. InstantCharacter demonstrates three fundamental
  advantages: first, it achieves open-domain personalization across diverse
  character appearances, poses, and styles while maintaining high-fidelity
  results. Second, the framework introduces a scalable adapter with stacked
  transformer encoders, which effectively processes open-domain character
  features and seamlessly interacts with the latent space of modern diffusion
  transformers. Third, to effectively train the framework, we construct a
  large-scale character dataset containing 10-million-level samples. The dataset
  is systematically organized into paired (multi-view character) and unpaired
  (text-image combinations) subsets. This dual-data structure enables
  simultaneous optimization of identity consistency and textual editability
  through distinct learning pathways. Qualitative experiments demonstrate the
  advanced capabilities of InstantCharacter in generating high-fidelity,
  text-controllable, and character-consistent images, setting a new benchmark for
  character-driven image generation. Our source code is available at
  https://github.com/Tencent/InstantCharacter.
paper_ai_summary: InstantCharacter is a diffusion transformer framework enabling high-fidelity, text-controllable character customization across diverse styles without requiring fine-tuning.
paper_ai_keywords: U-Net architectures diffusion transformer adapter transformer encoders latent space large-scale character dataset paired data unpaired data identity consistency textual editability character-driven image generation