RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Unifying diverse image generation tasks within a single framework remains a fundamental challenge in visual generation. While large language models (LLMs) achieve unification through task-agnostic data and generation, existing visual generation models fail to meet these principles. Current approaches either rely on per-task datasets and large-scale training or adapt pre-trained image models with task-specific modifications, limiting their generalizability. In this work, we explore video models as a foundation for unified image generation, leveraging their inherent ability to model temporal correlations. We introduce RealGeneral, a novel framework that reformulates image generation as a conditional frame prediction task, analogous to in-context learning in LLMs. To bridge the gap between video models and condition-image pairs, we propose (1) a Unified Conditional Embedding module for multi-modal alignment and (2) a Unified Stream DiT Block with decoupled adaptive LayerNorm and attention mask to mitigate cross-modal interference. RealGeneral demonstrates effectiveness in multiple important visual generation tasks, e.g., it achieves a 14.5% improvement in subject similarity for customized generation and a 10% enhancement in image quality for canny-to-image task. Project page:this https URL

@article{lin2025_2503.10406,
  title={ RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models },
  author={ Yijing Lin and Mengqi Huang and Shuhan Zhuang and Zhendong Mao },
  journal={arXiv preprint arXiv:2503.10406},
  year={ 2025 }
}