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arxiv 2504.11478 v2 pith:JFPQQTMR submitted 2025-04-12 cs.CV cs.AI

Flux Already Knows -- Activating Subject-Driven Image Generation without Training

Hao Kang , Stathi Fotiadis , Liming Jiang , Qing Yan , Yumin Jia , Zichuan Liu , Min Jin Chong , Xin Lu This is my paper
classification cs.CV cs.AI
keywords imagegenerationsubject-drivenfluxinsertionmodelresultssubject
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We propose a simple yet effective zero-shot framework for subject-driven image generation using a vanilla Flux model. By framing the task as grid-based image completion and simply replicating the subject image(s) in a mosaic layout, we activate strong identity-preserving capabilities without any additional data, training, or inference-time fine-tuning. This "free lunch" approach is further strengthened by a novel cascade attention design and meta prompting technique, boosting fidelity and versatility. Experimental results show that our method outperforms baselines across multiple key metrics in benchmarks and human preference studies, with trade-offs in certain aspects. Additionally, it supports diverse edits, including logo insertion, virtual try-on, and subject replacement or insertion. These results demonstrate that a pre-trained foundational text-to-image model can enable high-quality, resource-efficient subject-driven generation, opening new possibilities for lightweight customization in downstream applications.

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Cited by 4 Pith papers

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