title: LoRAShop: Training-Free Multi-Concept Image Generation and Editing with
    Rectified Flow Transformers
summary: 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.
paper_authors._id: 683925243a3289061eda69ee
paper_authors.name: Yusuf Dalva
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:26.107Z
paper_authors._id: 65454d7c117ecae648892170
paper_authors.avatarUrl: /avatars/83a7091a24bf86801176ca85234b417a.svg
paper_authors.fullname: Yusuf Dalva
paper_authors.type: user
paper_authors.user: ydalva
paper_authors._id: 683925243a3289061eda69ef
paper_authors.name: Hidir Yesiltepe
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:47:00.364Z
paper_authors._id: 63597e84d72fc0539e72b507
paper_authors.avatarUrl: /avatars/f568302b70064220e3c824577e5bece4.svg
paper_authors.fullname: Hidir Yesiltepe
paper_authors.type: user
paper_authors.user: Hidir
paper_authors._id: 683925243a3289061eda69f0
paper_authors.name: Pinar Yanardag
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:47:03.191Z
paper_authors._id: 683b9c7d4568ffd33e7334e1
paper_authors.avatarUrl: /avatars/046bd5a78a24f3bc4e1f6cb6076970e0.svg
paper_authors.fullname: Pinar Yanardag
paper_authors.type: user
paper_authors.user: Pinguar
paper_title: LoRAShop: Training-Free Multi-Concept Image Generation and Editing with
    Rectified Flow Transformers
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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