LoRAShop: Training-Free Multi-Concept Image Generation and Editing with
  Rectified Flow Transformers
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.
683925243a3289061eda69ee
Yusuf Dalva
claimed_verified
2025-05-30T06:53:26.107Z
65454d7c117ecae648892170
/avatars/83a7091a24bf86801176ca85234b417a.svg
Yusuf Dalva
user
ydalva
683925243a3289061eda69ef
Hidir Yesiltepe
claimed_verified
2025-06-02T07:47:00.364Z
63597e84d72fc0539e72b507
/avatars/f568302b70064220e3c824577e5bece4.svg
Hidir Yesiltepe
user
Hidir
683925243a3289061eda69f0
Pinar Yanardag
claimed_verified
2025-06-02T07:47:03.191Z
683b9c7d4568ffd33e7334e1
/avatars/046bd5a78a24f3bc4e1f6cb6076970e0.svg
Pinar Yanardag
user
Pinguar
LoRAShop: Training-Free Multi-Concept Image Generation and Editing with
  Rectified Flow Transformers
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.
LoRAShop, a framework for multi-concept image editing with LoRA models, leverages spatially coherent feature activation in Flux-style diffusion transformers to achieve seamless integration of multiple subjects or styles while preserving global context and identity.
Flux-style diffusion transformers concept-specific transformer features denoising process disentangled latent mask LoRA models identity preservation personalized diffusion models compositional visual storytelling rapid creative iteration