What is InstantCharacter
InstantCharacter is a customized image generation plugin open-sourced by Tencent Hunyuan. Based on the Diffusion Transformer (DiT) framework, it introduces a scalable adapter (containing multiple Transformer encoders) and a large-scale character dataset with tens of millions of samples, enabling high-fidelity, text-controllable, and character-consistent image generation. InstantCharacter supports users providing a character image and a simple text description, allowing the character to appear in different scenes in any pose. InstantCharacter has broad application prospects in fields such as comic strips and film creation, setting a new benchmark for character-driven image generation.
Main Features of InstantCharacter
- Character Consistency Preservation: Maintains the character's appearance, style, and identity consistency across different scenes and poses.
- High-Fidelity Image Generation: Generates high-quality, high-resolution images with rich details and realism.
- Flexible Text Editability: Users control the character's actions, scenes, and style based on simple text descriptions.
- Open-Domain Character Customization: Supports a variety of character appearances, poses, and styles.
- Fast Generation: Quickly generates images that meet requirements without complex fine-tuning for each character.
Technical Principles of InstantCharacter
- Diffusion Transformer (DiT) Architecture: Based on a modern Diffusion Transformer as the foundation model, DiT offers better generation capability and flexibility compared to traditional U-Net architectures. DiT is based on a Transformer structure, better handling complex image features and long-range dependencies.
- Scalable Adapter: Introduces a Transformer-based scalable adapter module used to parse character features and interact with the DiT latent space. The adapter consists of multiple stacked Transformer encoders that progressively refine character features, ensuring seamless integration with the foundation model. Pre-trained visual encoders such as SigLIP and DINOv2 are used to extract detailed character features, avoiding feature loss.
- Large-Scale Character Dataset: Constructs a large-scale character dataset containing tens of millions of samples, divided into paired (multi-view characters) and unpaired (text-image combinations) subsets. Paired data is used to optimize character consistency, while unpaired data is used to optimize text controllability.
- Three-Stage Training Strategy：
- First Stage: Pre-trains with unpaired low-resolution data to maintain character consistency.
- Second Stage: Trains with paired low-resolution data to enhance text controllability.
- Third Stage: Jointly trains with high-resolution data to improve image fidelity.
InstantCharacter Project Address
- Project Official Website：https://instantcharacter.github.io/
- GitHub Repository：https://github.com/Tencent/InstantCharacter
- arXiv Technical Paper：https://arxiv.org/pdf/2504.12395
- Online Experience Demo：https://huggingface.co/spaces/InstantX/InstantCharacter
Application Scenarios of InstantCharacter
- Comic Strip and Manga Creation: Quickly generates character actions and expressions in different scenes, maintains character consistency, and reduces manual drawing workload.
- Film and Animation Production: Generates character concept art and animation scenes, rapidly iterates character designs, and adapts to different plot requirements.
- Game Design: Generate multiple poses and scenes for game characters, support multiple styles, and quickly generate images that match the game's style.
- Advertising and Marketing: Quickly generate character images that match the theme based on advertising copy, enhancing advertising appeal and creativity.
- Social Media and Content Creation: Users generate personalized character images based on text descriptions, increasing content interest and interactivity.