InstantCharacter: Personalize Any Characters with a Scalable Diffusion
  Transformer Framework
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.
6801f92ac9953c32ecda5c12
Jiale Tao
6801f92ac9953c32ecda5c13
Yanbing Zhang
6801f92ac9953c32ecda5c14
Qixun Wang
claimed_verified
2025-07-24T12:00:07.967Z
643665d33193f279361cc292
https://cdn-avatars.huggingface.co/v1/production/uploads/643665d33193f279361cc292/k85RbhgQHp_gr2lLLOcgK.png
wangqixun
user
wangqixun
6801f92ac9953c32ecda5c15
Yiji Cheng
6801f92ac9953c32ecda5c16
Haofan Wang
claimed_verified
2025-04-18T09:38:16.438Z
637745113a63a2983ffbde13
https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
user
wanghaofan
6801f92ac9953c32ecda5c17
Xu Bai
6801f92ac9953c32ecda5c18
Zhengguang Zhou
6801f92ac9953c32ecda5c19
Ruihuang Li
6801f92ac9953c32ecda5c1a
Linqing Wang
6801f92ac9953c32ecda5c1b
Chunyu Wang
6801f92ac9953c32ecda5c1c
Qin Lin
6801f92ac9953c32ecda5c1d
Qinglin Lu
InstantCharacter: Personalize Any Characters with a Scalable Diffusion
  Transformer Framework
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.
InstantCharacter is a diffusion transformer framework enabling high-fidelity, text-controllable character customization across diverse styles without requiring fine-tuning.
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