LoRAShop: Training-Free Multi-Concept Image Generation and Editing with
  Rectified Flow Transformers
Authors: name: Yusuf Dalva, name: Hidir Yesiltepe, name: Pinar Yanardag

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.