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

• URL: http://arxiv.org/abs/2503.10406v1
• Date: Thu, 13 Mar 2025 14:31:52 GMT
• Title: RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models
• Authors: Yijing Lin, Mengqi Huang, Shuhan Zhuang, Zhendong Mao,
• Abstract summary: RealGeneral is a novel framework that reformulates image generation as a conditional frame prediction task.<n>It mitigates a 14.5% improvement in subject similarity for customized generation and a 10% enhancement in image quality for canny-to-image task.
• Score: 22.042487298092883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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: https://lyne1.github.io/RealGeneral/

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