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

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arxiv 2503.10406 v2 pith:XSJNXPYW submitted 2025-03-13 cs.CV cs.AI

Yijing Lin , Mengqi Huang , Shuhan Zhuang , Zhendong Mao This is my paper
classification cs.CVcs.AI
keywords generationmodelsimagerealgeneralvisualgithubunifiedvideo
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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_web/; GitHub Link: https://github.com/Lyne1/RealGeneral

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling

    cs.CV 2026-07 conditional novelty 6.0 of 10

    An MLLM-conditioned next-scale VAR decoder handles 15+ unified visual generation tasks with competitive quality and substantially lower latency than diffusion baselines.

  2. iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation

    cs.CV 2025-11 conditional novelty 6.0 of 10

    iMontage repurposes a pretrained video diffusion model to generate coherent yet highly dynamic image sets from arbitrary numbers of input images.

  3. USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

    cs.CV 2025-08 conditional novelty 6.0 of 10

    USO trains one DiT model for subject-driven, style-driven, and joint generation by disentangling content and style from triplet data and adding a style-reward objective, claiming SOTA on USO-Bench.

  4. LongAnimation: Long Animation Generation with Dynamic Global-Local Memory

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LongAnimation uses a dynamic global-local memory, built from a long-video-understanding model's KV cache, to colorize animation sequences of about 500 frames with stable color consistency.

  5. From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Frozen CogVideoX1.5, adapted with LoRA on 3 to 30 input-output videos, performs segmentation, pose estimation, and abstract reasoning (ARC-AGI 16.75%) with modest but real generalization.

  6. Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A video diffusion model, HunyuanVideo-I2V, is adapted with mixup transitions, frame-skip position embeddings, and attention masking to outperform image-only models on several controllable image generation benchmarks.

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