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.
