Plug-and-Play Multi-Concept Adaptive Blending for High-Fidelity Text-to-Image Synthesis

• URL: http://arxiv.org/abs/2511.17615v1
• Date: Tue, 18 Nov 2025 12:25:47 GMT
• Title: Plug-and-Play Multi-Concept Adaptive Blending for High-Fidelity Text-to-Image Synthesis
• Authors: Young-Beom Woo,
• Abstract summary: We introduce plug-and-play multi-concept blending for high-fidelity text-to-image (T2I) generation.<n>Our method leverages guided appearance attention to faithfully reflect the intended appearance of each personalized concept.<n>We also present a mask-guided noise mixing strategy that preserves the integrity of non-personalized regions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrating multiple personalized concepts into a single image has recently become a significant area of focus within Text-to-Image (T2I) generation. However, existing methods often underperform on complex multi-object scenes due to unintended alterations in both personalized and non-personalized regions. This not only fails to preserve the intended prompt structure but also disrupts interactions among regions, leading to semantic inconsistencies. To address this limitation, we introduce plug-and-play multi-concept adaptive blending for high-fidelity text-to-image synthesis (PnP-MIX), an innovative, tuning-free approach designed to seamlessly embed multiple personalized concepts into a single generated image. Our method leverages guided appearance attention to faithfully reflect the intended appearance of each personalized concept. To further enhance compositional fidelity, we present a mask-guided noise mixing strategy that preserves the integrity of non-personalized regions such as the background or unrelated objects while enabling the precise integration of personalized objects. Finally, to mitigate concept leakage, i.e., the inadvertent leakage of personalized concept features into other regions, we propose background dilution++, a novel strategy that effectively reduces such leakage and promotes accurate localization of features within personalized regions. Extensive experimental results demonstrate that PnP-MIX consistently surpasses existing methodologies in both single- and multi-concept personalization scenarios, underscoring its robustness and superior performance without additional model tuning.

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