LoRAShop：基于整流流变换器的免训练多概念图像生成与编辑

LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers

May 29, 2025

作者: Yusuf Dalva, Hidir Yesiltepe, Pinar Yanardag

cs.AI

摘要

我们推出LoRAShop，这是首个利用LoRA模型进行多概念图像编辑的框架。LoRAShop基于对Flux风格扩散变换器内部特征交互模式的关键观察：在去噪过程的早期阶段，特定概念的变换器特征会激活空间上连贯的区域。我们利用这一观察，在先前的前向传递中为每个概念推导出解耦的潜在掩码，并仅在待个性化概念所限定的区域内融合相应的LoRA权重。由此产生的编辑结果，能够将多个主题或风格无缝融入原始场景，同时保持全局上下文、光照及精细细节的完整。实验表明，相较于基线方法，LoRAShop在身份保持方面表现更优。通过省去重新训练和外部约束，LoRAShop将个性化扩散模型转变为实用的“LoRA版Photoshop”工具，为组合式视觉叙事和快速创意迭代开辟了新路径。

English

We introduce LoRAShop, the first framework for multi-concept image editing with LoRA models. LoRAShop builds on a key observation about the feature interaction patterns inside Flux-style diffusion transformers: concept-specific transformer features activate spatially coherent regions early in the denoising process. We harness this observation to derive a disentangled latent mask for each concept in a prior forward pass and blend the corresponding LoRA weights only within regions bounding the concepts to be personalized. The resulting edits seamlessly integrate multiple subjects or styles into the original scene while preserving global context, lighting, and fine details. Our experiments demonstrate that LoRAShop delivers better identity preservation compared to baselines. By eliminating retraining and external constraints, LoRAShop turns personalized diffusion models into a practical `photoshop-with-LoRAs' tool and opens new avenues for compositional visual storytelling and rapid creative iteration.