InstantCharacter - A customizable image generation plugin from Tencent's Hunyuan open source platform.

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 to achieve high-fidelity, text-controlled, and character-consistent image generation. InstantCharacter allows users to provide a character image and a simple text description, enabling the character to appear in different scenes in any pose. InstantCharacter has broad application prospects in fields such as comic book and film creation, setting a new benchmark for character-driven image generation.

The main functions of InstantCharacter

• Maintaining role consistencyMaintain consistency in the character's appearance, style, and identity across different scenarios and poses.
• High-fidelity image generationGenerates high-quality, high-resolution images with rich detail and realism.
• Flexible text editing capabilitiesUsers control the character's actions, scenes, and style based on simple text descriptions.
• Open Domain Role CustomizationSupports a variety of character appearances, poses, and styles.
• Quick generationNo need for complex fine-tuning for each character; quickly generate images that meet the requirements.

InstantCharacter's technical principles

• Diffusion Transformer (DiT) ArchitectureBased on the modern diffusion Transformer as its foundational model, DiT offers superior generative capabilities and flexibility compared to the traditional U-Net architecture. DiT's Transformer-based structure allows it to better handle complex image features and long-range dependencies.
• Scalable AdapterA Transformer-based extensible adapter module is introduced to facilitate the interaction between character features and the latent space of DiT. The adapter consists of multiple stacked Transformer encoders, progressively refining character features to ensure seamless integration with the base model. Pre-trained visual encoders such as SigLIP and DINOv2 are used to extract detailed character features, avoiding feature loss.
• Large-scale character datasetsWe constructed a large-scale character dataset containing tens of millions of samples. The dataset was divided into paired (multi-view characters) and unpaired (text-image combinations) subsets. Paired data was used to optimize character consistency, while unpaired data was used to optimize text controllability.
• Three-stage training strategy:
  • Phase 1Pre-train using unpaired low-resolution data to maintain role consistency.
  • Phase Two: Use paired low-resolution data for training to enhance text controllability.
  • Phase ThreeJoint training with high-resolution data improves 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
• Experience the demo online:https://huggingface.co/spaces/InstantX/InstantCharacter

Application scenarios of InstantCharacter

• Comic strip and comic book creationQuickly generate character actions and expressions in different scenes, maintain character consistency, and reduce the amount of manual drawing work.
• Film and animation productionGenerate character concept art and animated scenes, quickly iterate character designs, and adapt to different plot requirements.
• Game DesignGenerates various poses and scenes for game characters, supports multiple styles, and quickly generates images that match the game's style.
• Advertising and MarketingQuickly generate character images that match the theme based on the advertising copy, enhancing the appeal and creativity of the advertisement.
• Social media and content creationUsers can generate personalized character images based on text descriptions, increasing the fun and interactivity of the content.