LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers
Authors: Yusuf Dalva
Hidir Yesiltepe
Pinar Yanardag
Venue: NeurIPS 2025 spotlight ()
Keywords: lora
image editing
t2i
flux
Primary area: applications
TL;DR: We introduce LoRAShop, the first framework for multi-concept image generation and editing with LoRA models.

Abstract: We introduce LoRAShop, the first framework for multi-concept image generation and 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: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers
Yusuf Dalva
Hidir Yesiltepe {ydalva, hidir, pinary)@vt.. Virginia Tech
Pinar Yanardag
lorashop. porashop.githu... io
Edited
A1 Generation with Mulli//Mulipic Conceepts
b) Editing with Singla Multiple Cencepts
Figure I: LoRAShop. We present LoRAShop, a training-free framework enabling the simultaneous use of multiple LoRA adapters for generation and editing. By identifying the coarse boundaries of personalized concepts as subject priors, we allow the of multiple LoRA adapters by eliminating the "cross-talk" between different adapters.
Abstract
We introduce LoRAShop, the first framework for multi-concept image editing with LoRA models. LoRAShop builds on key observation about the feature interaction patterns inside Flux-style diffusion transformers: concept-spechie 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 styles into the original scene while preserving global context, lighting, and fine details. Our experiments demonstrate that LoR LoRAShop delivers better identity preservation compared to baselines. By eliminating retraining and external constraints, LoRAShop turns personalized diffusion models into practical "photoshop-we Mhkotoshop-with-Lode -oth-LoRAA tool and opens new avenues for compositional visual storytelling and rapid creative iteration.
transformer
Introduction
The rapid progress in Text-to-image (T21) generative models 29 33 28 has opened new creative avenues such as content generation [8 B 21 and editing [5] 39
48 4. but users often desire customized outputs with specific topics styles not present in the original training data [51] Personalization techniques that fine-tune pre-trained generative model on a small set of user-provided images have emerged meet this need. Notably, methods like DreamBooth (30) and Low-Rank Adaptation (LoRA) [12] allow T2I models to be customized, capturing user-specific concepts (e.g. partieular pet, unique face, distinct style) and regenerating them in
49 22
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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new contexts with high fidelity. While multi-concept generation is challenging problem: Given multiple fine-tuned concept models (e.g. several LoRAs trained on different subjects), how can we compose them to synthesize coherent image containing all the custom concepts? Achieving such compositions is challenging because independently trained LoRAs can interfere with each other when combined, leading to identity distortions or one concept dominating the other - a phenomenon sometimes called "LoRA crosstalk"
personalization is a relatively simple task,
[10 [25 20 36). Simply merging applying multiple LoRAs naively often causes one concept to vanish or entangle attributes with the other [0]. Recent research indeed highlights that multi-concept generation remains nontrivial: combining personalized models typically degrades individual concept quality unless special measures are taken [6 25 [0]. However, these methods still require training a new combined model fine- fine-tuning process (e.g.. imposing constraints during each LoRA's training or running a post hoc alignment optimization).
While existing techniques can achieve multi-concept generation i.e. producing a new image containing several personalized concepts - none of these methods addresses the task of multi- concept editing: modifying a given image to insert multiple new concepts. Multi-concept image editing presents a different set of challenges. Here, the goal is not to generate a scene from scratch, but to start from an input image and seamlessly blend in additional personalized elements (each defined by a LoRA model) into that image. A naive approach to this problem might be to apply iterative inpainting: for example, masking region in the image and prompting the diffusion model (with the LoRA loaded) to generate the new concept in that area. Unfortunately, off-the- sffehhe-shelf inpainting with personalized diffusion models often yields artifacts and inconsistencies. The injected object or character may not blend naturally with the lighting and context of the original image, or the model may unintentionally alter the surrounding content. Another approach could be face-swapping or identity transfer, where a person's face in the image is replaced with a personalized face (using a LoRA of that person). Although this can handle single face, it often does not preserve the full appearance of the person, such as body features, and can produce unrealistic results.
In this paper, we propose LoRAShop, a novel framework that enables multi-concept image editing with LoRA models, without requiring any additional training, special auxiliary inputs, or external segmentation. Given an input image and set of LoRA modules (each encoding a different concept), LoRAShop allows the user to insert each concept into the image at a desired location in a disentangled way. One of our key observations is disentangled mask extraction technique that leverages the internal representations of the rectified-flow model to localize the influence of each subject to be personalized. In essence, each LoRA is applied during the denoising process, method extracts a coarse mask that delineates the regions where that concept significantly contributes to the image. By combining these masks with the user's concept specifications, A is able blend multiple concepts directly into the diffusion latent in a controlled manner (see Fig. D. Our experiments show that LoRA subjects blend naturally into the original scene, and their identities/styles match the LoRA concepts with high fidelity. Our approach does not require training of any new model or ensemble; it directly utilizes existing LoRAs and the base rectified-flow floww model at inference time, making efficient and user-fric friendly. We believe that LoRAShop fills an important gap between personalized generation and image editing, opening the door to new creative workflows (such as "LoR Lsshsshpin with generative models) that were previously impractical.
2 Related Work
Personalized Image Generation. Personalized image generation aims to inject a user-defined concept, typically a face, style, or object, into a text-to-image model so it can be used in future generations. Early work relied on Textual Inversion (TI) [9]. which learns a single embedding that reproduces a user's concept. TI is lightweight, but struggles to learn concepts involve high level of detail, where it learns to reconstruct the target concept with diffusion loss. DreamBooth (DB) [30] improves fidelity by fine-tuning selected model weights and reserving a rare token for the new concept. though at a higher compute cost. Later methods seek better extends TI with richer token representation; Custom Diffusion [19] trains only cross-attention layers; and DB-LoRA R2 applies low-rank adaptation [12 to store each concept in small rank-limited update. Recent based systems such as StyleDrop 37 Dream (3I, Taming Encoder [15), IP-Adapter [46), MS-Diffusion
efffciiecy trade-offs: P+ [40
42 MIP-Adapter [3), Infinite You (16). OmniGen

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Stage 1: Self-Suuerriied Subject Prior Extraction
Stage Prior-Guided Blending Residual Features
Imagee
Image Tokens
Double Stmam Block
((W)
I(W AW)
Double Stream Black
putiocs-prio Extraction (Bea. 3.20
Blanding Residual Fasitaraa 9.8)
Singlo Stream Block
M
Singlo Stream Black
Image
Toxt
Figure 2: LoRAShop Framework. LoRAShop enables multi-subject generation and editing over a
two-stage training-fred pipeline. First, we extract the subject prior Me, which gives a coarse-level prior on where the concept of interest, c', is located. Following, introduce blending mechanism over the transformer block residuals, which both enables seamless blending of customized features and bounds the region-of-interest for the LoRA adapter utilized.
45 and UNO 4 predict adapter features directly from reference images, enabling near-instant personalization but often with some loss identity fidelity compared with full DreamBooth tuning.
Merging Multiple Concepts. Combining LoRAs for style and subject control remains as a chal- lenging tasks, as combined adapters usually optimize overlapping representations. In achieving such a combination of personalized concepts, current work still faces certain challenges. Simple weight averaging
32 is fast but quickly causes interference. Mix-of-Show [TO] trains special embedding-
decomposed LoRAs that avoid this clash, yet it needs the original data and cannot use community models, such as those available on platforms like civit.ai (2. ZipLoRA (34 merges one style and one content adapter but breaks down with more than one content LoRA. On the other hand, OMG 18 is based on an external segmenter to apply separate concepts, whose errors propagate to the result. Orthogonal Adaptation 25 keeps LoRAs in separate subspaces with additional constraints introduced, reducing cross-talk, but adds training overhead and likewise assumes data access. Our proposed approach differs from existing multi-concept generation methods since our main goal is 'editing' as opposed to generation. Moreover, our method does not require any input conditions such as keypoints segmentation masks.
3 Method
We propose LoRAShop. new training-free pipeline that enables the use of multiple LoRA adapters through a targeted feature blending scheme for multi-subject generation and editing. Our method, Multi-Subject Residual Blending (MSRB), consists of two fundamental stages: 1) the extraction of subject prior that effectively highlights the spatial regions where each subject is intended appear, and 2) the application of residual feature blending scheme within the diffusion transformer that selectively merges the outputs of different LoRA adapters. This allows to spatially combine features corresponding to distinct concepts, enabling coherent and disentangled multi- multi-subject generation and editing without any additional training.
3.1 Preliminaries
Multi-Modal Diffusion Transformers. Multi-modal diffusion transformers (MM-DiT) g extend the DiT architecture by processing text and image tokens two tightly coupled streams, enabling end- to-end text-to-ing to-ie generation. Rectified-flon models such as FLUX adopt this design and alternate between two transformer block types. We denote blocks that keep separate parameter sets for the text and image streams double-stream blocks, and those that apply shured transformation to both streams as single-stream blocks. During the denoising trajectory, the network first aligns textual and visual features within the double-stream blocks and subsequently refines the fused representation in

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Concepts
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Figure Editing Generated Real Images with LoRAShop. We provide qualitative editing results with different human concepts. LoRAShop can achieve both edits on real and generated images. Due to -ntinttrsecting subject prior extraction scheme our framework, LoRAShop can perform edits with multiple concepts in one denoising pass.
the single-stream blocks. All feature updates propagate through residual connections, an architectural property that our generation and editing protocol leverages directly.
Personalization via Low-Rank Adaptation. Low-Rank Adaptation (LoRA) 12 was originally
introduced as lightweight fine-tuning method for large language models. Instead of updating the full weight matrix Wo E Rd LoRA learns a low-rank increment, formulating the fine-tuned weights W Wo + AW- Wo + BA with B Rx A E T and intrinsic rank < min d. k)) Because only A and B are trained, the additional parameter count and memory footprint scale linearly with r, making LoRA especially attractive for large backbones. Following its success in NLP. LoRA has been adopted for text-to-image diffusion models and, more recently, for rectified-flow transformers such FLUX. We leverage single-subject LoRA adapters trained for rectified-llow transformers and introduce a training faining-free mechanism that allows multiple adapters, each corresponding to a different subject, be used simultaneously without any additional optimization.
3.2 Self-Supervised Subject Prior Extraction
Training several LoRA adapters so they can be applied simultaneously is costly and often infeasible for large-scale denoisers: Every additional adapter consumes optimization memory, and jointly fine- tuning many of them tends to introduce interference and distribution drift. To bypass this bottleneck, first predict, in inference time, where each personalized subject will emerge latent space and then confine every adapter's effect to the pixels assigned to that subject. The binary masks that delimit these regions are our subject priors. We extract each prior once
short eseudo-deniisee run that proceeds only until timestep 7, when latents are still close to noise, yet cross-attention already carries strong spatial cues IT 4. Rectified flow transformers such as FLUX provide well-localized cross-attention maps. In particular, the map from the last block that still keeps the text and image streams separate (the double- stream block) gives the sharpest separation. For prompt e and the token subset c' naming one subject we compute Me where Q; are the image queries, Ker the keys of d', and the key dimension.
M softmax (Q.K
Because raw attention may fragment, we iteratively blur M with a 3 3 Gaussian kernel and renormalize until the super-threshole area forms single connected component. Thresholding the т posterior quantile then produces the final binary mask, which we denote by Me, , for the subject é'

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When multiple subjects are present, these masks can intersect, leading to undesirable "LoRA cross-talk". To obtain non-overlapping maps, stack the smoothed attention maps {Mu)am) determine for every spatial position (i,j) the subject u with the strongest response,
kt (i, = arg max, (i.i.),
Mmax (i,j) (i.)
(2)
and finally define one-hot priors as (i.j) 11 k (i.j)) The set Mu partitions the latent canvas without overlap and serves as the spatial guide for adapter mixing during generation and editing.
3.3 Prior-Guided Blending of Residual Features
The diffusion transformer proceeds as usual, but every block overwrite the residual feature lensors wherever a subject prior is active. At block the frozen backbone produces a collection of R residual tensors, Plase RxC with
..,.... R. corresponding the outputs of multi-modal attention, MLP, and any other sublayer that feeds skip connection. In parallel, the k-th LoRA adapter contributes its counterparts ((A) The binary priors Me E {0, 1 indicate which latent tokens belong to subject c'.
Err
For each token position we turn the priors into weights, so the weights sum to one on the subject tokens and to zero on background tokens.
(p(p) Σ (x)
and (p)
8 <<
N
Whether the block is double-stream (text and image kept separate) or single-stream, we treat the same way: only image tokens are blended; prompt tokens keep their backbone residuals. For every image token p and every residual index r we substitute
F (g)
Σ a (p( F (/
(4)
and feed Fr., back through the block's s skip connection. no subject claims token p C,, M.. = 0), we leave F (p) unchanged. Blending disabled during the first until timestep letting the backbone establish the overall layout of the scene before subject-specific features are inserted. Because we mix the residual outputs of every sublayer rather than changing any weights, all adapters remain independent, and each subject influences exactly the tokens selected by its prior across the entire depth of the transformer.
3.4 Editing with LoRAShop
LoRAShop intervenes only in the feature space of rectified flow transformer: it neither modifies the noise schedule nor alters any model weights. During the reverse diffusion process, we overwrite residual features solely at token positions indicated by the subject priors, leaving all other tokens unchanged. Because this operation is local and linear, the global denoising trajectory, and thus the overall scene layout, remains intact. The same mechanism integrates seamlessly with inversion. We adopt the RF-Solver pipeline of [41], which uses a second-ordes solver to recover the latent noise corresponding to target image. After reconstructing the latent, we utilize LoRAShop edit the inverted latent. As illustrated in Fig. and Fig. B] this enables eggion-controlled insertion of multiple personalized concepts into real images while faithfully preserving the properties of the input.
Experiments
We evaluate LoRAShop on both image generation and image editing tasks. For generation, we measure how well the method renders a single personalized subject and how reliably composes multiple personalized subjects in one scene. For editing, we evaluate identity transfer on real images, replacing a person's 's appearance with that encoded by a LoRA adapter. We provide the details of our experimental protocol along with the results in this section.
5

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Ref.
Dream Booth
IP-Adapter
InfiniteYou
UNO
OmniGen
a)
happy elf, standing beneath glowing frees forest where mooniggt streams through twisted branches.
OmniGen
UNC
DreamBoait
MS-Diffusion
MIP-Adapter
b)
a woman a chef's uniform, a woman a barista agron a warmly café interior
Original
Ours
ReFace
Original
Ours
ReFace
c)
Figure 4: Qualitative Comparisons. We provide qualitative comparisons three mainstream tasks: single-subject generation, multi-subject generation and face swapping. Over all of the benchmarked tasks, LoRAShop provides superior performance against competing approaches.
Experimental Setup We use FLUX.1-dev. as the rectified-flow transformer on which we build our approach. Our approach is based on utilizing pre-trained LoRA adapters for tasks such as singlc/multi-concept generation and editing. In all of our experiments, we use the LoRAs available diffusers 24 library. We provide complete list of LoRAs used in our experiments in the supplementary material, along with visual representations of these concepts for ease of understanding. Unless otherwise mentioned, we set the editing timestep 0.90, 7 0.94 and r 0.7, where we apply the proposed blending scheme (Sec. 3.3) onward timestep t during the reverse process. Our approach requires no training over the pre-trained adapters and can perform the aforementioned personalization task in inference time. We conduct our experiments using one NVIDIA L40S GPU. LoRAShop can generate images using two concepts approximately 50 seconds, as opposed the manual inference time of FLUX. 1-dev which requires 30 seconds per image. Furthermore, since LoRAShop can apply each concept sequentially, we introduce no memory constraints on how many concepts that can be applied to given image. See Fig. for 4 subject generation results.
Table 1: Quantitative Comparisons on Sungle-Subject Generation. We provide quantitative comparisons single-subject generation. Our method outperforms the competing FLUX-based approaches the overall performance, measured over identity similarity, prompt alignment and visual quality.
Method
ID 1
CLIP-T
HPS
Aesthetics 1
DreamBooth
0.755 + 0.089
0.429 + 0.055
0.305 = 0.030
6.311 0.505
IP-AdapterFFLUX
0.309 + 0.077
0.330 -:000:00 0.053
0.272 = 0.026
6.340 - 0,408
Infinite You
0.683 + 0.068
0.039 0.439
0.307 = 0.026
6.490 0.459
Omni- -Gen
0,657 + 0.066
0.434
0.311 = 0.030
6.514 = 0.448
UNO
0.486 + 0.137
0.415 0.051
0.289 = 0.030
6.303 0.527
Ours
0.740 + 0.066
0.439 + 0.047
0.321 + 0.028
6.499 0.529
4.1 Qualitative Results
We qualitatively assess the effectiveness of our method in single & multiple subjects well as generated real images. To assess the visual performance of our framework, we demonstrate its capabilities in experiments on human subjects. Although LoRAShop can also perform edits

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variety of types of subject, we perform our experiments on human subjects due to the high level of details such concepts involve, and their wide-usage in customization tasks. Since our method requires no fine-tuning for LoRA- Aaadaptess we can use any adapter trained for our base model. Furthermore, since our approach does not focus on specific type of residuals (e.g. attention layer outputs), but operates on the overall representation space, we can also use LoRAs with different ranks and different sets of fine-tuned parameters together.
Editing on Generated and Real Images. We provide editing results with LoRAShop on both male and female subjects, where these LoRAs are trained with different sets of combinations, which involve different sets of weights, ranks, and presence of a trigger word. Presented in Fig, 3 LoRAShop can both perform edits on real & generated images, without altering any subject-independent details. Note that, since LoRA adapters offer us a way to utilize the rich semantics in the weight space of the denoiser, our approach can also perform changes to the body of the edited subject (Fig. [ row 1), which exceeds the limits of the face swapping task and provide us an advanced way of editing images with customized concepts. Furthermore, as our subject prior extraction algorithm provides non-intersecting masks, our approach facilitates performing multiple edits with distinct LoRA adapters in a single denoising pass.
Qualitative Comparisons. We provide qualitative comparisons of our approach with competing methods on sugject, multi-subject and face swapping tasks. Since our proposed approach performs generation by editing, we enable blending of the residual features. While vanilla approaches such as DreamBooth [30] achieve subject- based generation results, since they fine-tune the weights of the original denoiser, they result in reduced prompt alignment and visual coherence. On the other end, encoder-bad based approaches such as IP-Adapter [46), Infinite [T6], UNO (43 and OmniGen (45) struggle to encode the identity features that are effectively captured by DreamBooth. In this regard, our approach offers the best of both worlds (Fig. (a)), where we personalize only the regions related to the identity, which both achieves superior prompt alignment and personalization performance.
For multi-subject generation, we provide comparisons with FLUX-based approaches such as UNO [43] OmniGen [45 DreamBooth (30] and SDXL (26] -based approaches MS- DSSDuffusion [42 and MIP-Adapter 133. We use federated averaging for DreamBooth, as baseline towards multi- subject personalization. As we demonstrate Fig. (b), our subject priors mitigate the confusion between similar concepts effectively, where the remaining approaches either attempt to merge the two identities into one, or fail to capture the identity accurately. In this regard, our approach outperforms the competing methods for multi-subject generation as a training- free solution, which can effectively reflect multiple concepts effectively, mitigating the "cross-talk" effect between the concepts. Additionally, we provide comparisons with methods combining multiple LoRA adapters in Fig. 6 where our method offers compositions with high quality, without any pose input. To benchmark our method terms of editing, we select the face swapping task, where we use identity LoRAs represent the identity to be inserted into the original image. As qualitatively benchmark in Fig. 4 (c). our approach extends the limitations of identity swapping, which was a task that is limited with swapping the faces until today. As LoRA adapters are capable of capturing physical features in addition to facial features, LoRoShop enables the transfer of physical features in addition to the face of the source identity, in addition to superior fidelity against methods based on inpainting such as ReFace I.
While our main results focus on human subjects due to the complexity of identity fidelity, LoRAShop not limited to face- or body-based personalization. Fig. (6 we further demonstrate multi-concept composition on non-human and object-centric LoRAs (e.g.. animal identities, accessories, stylized materials, and textured objects). These examples highlight that our residual blending operates on concept semantics rather than facial structure, and extend beyond identity transfer to general personalized content insertion. Quantitative evaluations for non hon-human concepts are provided in the supplementary material.
4.2 Quantitative Results
Extending our benchmark qualitative experiments, we benchmark the editing and generation performance of LoR on three mainstream tasks. Specifically, we benchmark the performance of LoRAShop for single & multi concept generation along with face swapping task. We provide the details of each constructed benchmark below.
7

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Table 2: Quantitative Comparisons on Multi-Subject Generation. We benchmark approach against FLUX and SDXL based methods. LoRAShop achieves superior identity preservation over multiple subjects, while maintaining the prompt alignment and visual quality of the base model.
Method
ID
CLIP-T 1
HPS
Aesthetics 1
SDXL based
OMG
0.305 = 0.14
0.217 = 0.09
0.212 + 0.05
6,017 0.35
MS-Diffusion
0.206 = 0.05
0.251 = 0.08
0.253 + 0.03
6.119 + 0.24
MIP-Adapter
0.209 + 0.06
0.243 = 0.07
0.236
0.03
6.111 0.30
DreamBooth
0,444 = 0.08
0.248 = 0.08
0.259 + 0.04
6.113 0.30
FLUX based
OmniGen
0.453 + 0.09
0.256 0.08
0.258 + 0.04
6.264 + 0.26
UNO
0,270 + 0.07
0.252 = 0.08
0.255 + 0.04
6.113 0.36
Ours
0.532 + 0.12
0.252 = 0.08
0.260 + 0.04
6.124 + 0.29
Table 3: User Study. We present user study results on identity preservation (Q1), and prompt alignment (Q2) for multi-subjeet generation task.
Method
User Study Q1 1
User Study Q2
SDXL based
OMG
2.591 = 0.25
3.332 0.55
MS-Diffusion
2.596 + 0.27
2.753 + 0.19
MIP-Adapter
2.889 + 0.26
3.123 0.50
DreamBooth
3.196 + 0.10
4.060 + 0.14
FLUX based
OmniGen
3.340 + 0.32
4.012 + 0.26
UNO
2.711 + 0.23
3.587 + 0.44
Ours
3.762 + 0.25
4.230 + 0.13
Single-Subject Generation. Following previous work, we populate set of varying identities and generation prompts to benchmark our generation results. Among publicly available LoRA adapters, select 15 identity LoRAs and generate total of 520 images where 15 generation prompts were applied to each identity separately. To adequately assess both the personalization, prompt alignment and visual coherence of the generated outputs, such as artistic creations, contexts defined by activities and superficial concepts (see Fig. (a)). We provide the complete list of prompts we use for our benchmark in the supplementary material. To assess both identity preservation, text alignment and visual coherence of the generated images, we utilize ArcFace embeddings 77), CLIP-T similarity 227. HPS score 44] and Aesthetics score" We present the quantitative results in Table |
construct our benchmark prompts with themes
As quantitative metrics also show, our approach leads to a sweet spot between identity preservation, prompt alignment, and visual coherence. as we utilize the generative priors in our residual blending scheme.
Multi-Subject Generation. In addition to our benchmark for single-subject generation, also benchmark our approach against multi multi-subject generation methods. Using the 15 subjects that we used in our benchmark for single-subject generation, we initially generate random pairs of identities with corresponding prompts to create a benchmark for the two-subject generation task. In our evaluations, we compare our method with both FLUX-based methods UNO 4K] OmniGen 45 and DreamBooth
(FedAvg) [30] and SDXL-based methods OMG [18]. MS-Diffusion 42 and MIP-Adapter [13]. As we present in the results in Table our approach achieves both superior prompt alignment, visual coherence, and identity preservation.
User Study. Supplementary study to perceptually evaluate the generation quality of our approach. We conducted our study on 50 participants over Prolific. 70 images involving multiple subjects. In our study, we evaluated the generation performance in which users are asked to rate the images in two aspects Likert scale (1: poor, 5: excellent): (Q1) alignment with the target identities and (Q2) alignment with the generation prompt. We provide the result of our study in Tab. As our results also demonstrate, LoRAShop outperforms the competing
benchmark multi-subject generation, also conducted user
crowdsourcing platform, where each participant is asked to assess

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Table 4: Quantitative Comparisons Face Swapping. We benchmark LoRAShop against REFace M. While performing on-par par in input preservation, LoRAShop introduces significant improvements in identity preservation.
Method
ID 1
DINO
CLIP-I f
LPIPS +
ReFace
0.330 + 0.091
0.982 = 0.012
0.940 0.038
0.031 = 0.033 0.050 = 0.019
Ours 0.709 + 0.101
0.970 = 0.019
0.926 = 0.037
approaches in both prompt alignment and identity preservation. Please see Appendix for additional details about the user study.
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Figure 5: Ablation Study. (a) Ablations hyperparameters time step 4, subject's prior extraction step 7, and the posterior threshold for binarization of the subject's prior masks т. (b) Ablation on transformer blocks, where Block 19 shows superior ability for separation between subjects.
Face Swapping. We also benchmark our approach in the face swapping task. We compare our method with an inpainting- based swapping approach ReFace [.] Although our approach does not involve any hard constraints for content preservation such as inpainting masks that restrict the regions to be edited, our method still achieves competitive performance in terms of input preservation, which we measure using DINO 23 CL.IP-I (27). and I.PIPS (50) metries. Furthermore, LoRAShop leads to significant improvements in identity preservation properties. Note that our approach extends the bounds of the face swapping task and can perform full identity transfer by editing the physical appearance, in comparison to inpainting-base swapping approaches.
4.3 Ablation Studies
Ablations on Transformer Blocks. To further justify the use of the last double-stream block for subject prior extraction, and to provide an investigation over the roles of different transformer blocks, we provide ablations over the masks extracted from different transformer blocks in Fig. [ As shown by the attention masks M¹ extracted for the subject c' (e.g. woman), we observe that through the double-stream blocks (blocks 0- 091,, FLUX constructs the semantic context and is able to perform the separation between different concepts at the end of these blocks. In the single-stream blocks, we observe that the model attempts to focus more on the visual details, which results in maps spread out over different entities. Building up on this observation, we build our subject prior extraction scheme on the attention maps produced by the last double-stream block (c.g. Block 19).
Ablations on Editing Parameters. Complementary to the block selection, LoRAShop includes three additional hyperparameters for editing, which are the editing time step t, the subject's prior extraction step 7 and the posterior threshold for binarization of the subject's prior masks We provide ablations on these hyperparameters in Fig. 5 Similarly to the trend observed in diffusion-bain editing methods, LoRAShop is able to preserve the adapter-ireleeann features of the input image better when the edit is performed later timesteps. Considering that the effect should be effective enough and preserve certain features of the input image, we achieve good balance for the timestep I. Regarding the subject priors extracted prior to the denoising steps, we recognize that the introduced parameters have significant impact on the quality of the mask. In general, we find y 0.94 and

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a)
b)
Figure 6: Multi-concept personalization across domains. (a) Non-human and object-centric composition: LoRAShop extends beyond identity personalization to animals, footwear, and objects, accurately preserving shape and texture semantics without retraining external guidance. (b) Human multi-subjecc composition: LoRAShop disentangles and blends multiple identity LoRAs (e.g. <Lebron>, <Pirr>, <Messi>, <Gosling>), maintaining subject fidelity and spatial coherence. Compared to baselines; LoRACLR, Mix-of-Show, Prompt+, Orthogonal Adaptation, UNO, Om- niGen, MS-Diffusion, and MIP-Adapter, LoRAShop preserves concept boundaries and contextual consistency while avoiding feature entanglement. Quantitative results for non-human concepts and ocelusion-heawe overlapping cases are provided in the supplementary material.
0.7 as suitable hyperparameters, which we utilize in all of our experiments for complete and accurate enough masks.
Discussion
Limitations and Broader Impact. Because the extracted masks inherit the latent biases of the underlying diffusion model (e.g., greater attention to faces, stereotypical gender features, or saturated colors) [17 47), they can sometimes mislocate or underrepresent certain regions, leading to less coherent or unbalanced edits, particularly for concepts underrepresented in the model's pretraining data. Our mask extraction leverages attention patterns unique to the Flux architecture; other diffusion backbones (e.g., SDXL-TTurbe Turbo) may require re-tuning of threshold parameters or yield less coher- ent masks. This limits immediate portability across all T2I models. Like other powerful editing tools, LoRAShop can be used create non-consensual content. We encourage deployment within responsible-Al guardrails, but broader ethical safeguards remain necessary. Nevertheless, LoRAShop demonstrates-for the first time traeettrinnn--free region-controlled multi-concept editing with LoRAs, unlocking new creative workflows and research directions in compositional image manipulation.
Conclusion. We presented LoRAShop. the first training-free framework that enables region- controlled multi-concept image editing with off-the-shelf LoRA modules. By uncovering, and exploiting, spatially coherent activation patterns inside Flux diffusion transformers, we devised disentangled latent-mask extraction procedure that lets each LoRA act only where it is intended, eliminating cossect interference. Without any extra optimization, segmentation, or auxiliary guidance, LoRAShop seamlessly blends multiple personalized subjects or styles into input image, preserving both global context and fine local detail. Beyond advancing the state of the in person- alized image editing, Aooo turns diffusion models into an intuitive "photoshop-withe with-LoRAss. opening new possibilities for collaborative storytelling, product visualization, and rapid creative iteration.
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Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
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We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [Yes]
Justification: We use LLMs to generate evaluation prompts. We provide the complete list of prompts generated as part of our supplementary material.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM for what should or should not be described.
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Table of Contents Supplementary Material
Details of User Study
22
B
Additional Comparisons
22
B.1
Comparisons with Multi-LoRA Composition Approaches
22
B.2
Comparisons on the Extracted Masks
23
B.3
Quantitative Comparisons on Non-ID Concepts
................................
24
B.4
Comparisons on Occluding Concepts
................................
24
B,5
Evaluations on Full Body Identity Transfer
................................
25
Supplementary Generation and Editing Examples
25
Detailed Masking and Blending Algorithm
25
E Experiment Details
27
List of LoRA Adapters
28
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A Details of User Study
We provide a sample question for the user study conducted in Fig. 7 To assess both the identity preservation and prompt alignment capabilities of our approach, direct two questions to the participants of our study. The users are also provided representative examples of the personalized subjects, where these images are outsourced from assets available for public use. Then, the users are asked to rate the provided image on Likert scale, where corresponds to an unsuccessful generation and corresponds to successful generation.
The image the left (labeled "Output") is generated with using the given reference images ("Ref "Ref 2"), using the prompt "a woman in chef's uniform, and woman in barista apron, in warmly lit café interior". Based on the generation prompt and the references, answer the following questions the provided likert scale (1-poar, 5-excellent lent). [C]
Ref 1
Ref 2
Output
23 AN
C
S
C
O
A
1-poor
ow average
3 avvvraae
4-good
ent
image right (Dutput) reflerts identities on the (fte Ref 2)7
How wall does the "Output" image prompt "a woman
woman in a barista apron, in warmly lit café interior"?
Figure 7: User Interface of our User Study.
B Additional Comparisons
B.1 Comparisons with Multi-LoRA Composition Approaches
We compare our method against multi-concept LoRA composition approaches, including Mix-of- Show (10), LoRACLR [36), Orthogonal Adaptation [25), and Prompt+ [40] in Fig, S Notably, the first three methods require pose condition for generating compositions, and the first two depend on specialized LoRA models such EdLoRA, which limits their applicability when using community LoRAs from platforms like Civit.ai. In contrast, our method operates without pose conditions, retraining, or model merging, enabling successful composition using arbitrary LoRA models out of box. Additionally, our method can compose LoRAs with different characteristics (e.g. different ranks and different sets of parameters), by operating on output space only. We also highlight that other methods do not support Flux, thus we visually compare with their Stable Diffusion-bal based generations.
22

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We also note that LoRACLR (6), Orthogonal Adaptation [25] and Prompt+ 40 do not have
publicly available implementations, which prevents us from conducting a quantitative comparison.
Ours
LoraCLR
Orthogonal Adaptation
Mix-of-Show
Prompt+
BAKERS
<Lebron> and <Taylor>, inside a futuristic spaceship, sci-fi realism
Figure 8: Comparison with state-of-the -ffth-aa---- multi composition methods, on two subject generation task.
B.2 Comparisons on the Extracted Masks
We additionally compare our subject-prior extraction strategy against ConceptAttention[]] masks under the same multi-subject prompt set used in the main paper. ConceptAttention does not reliably separate instances of similar concepts (e.g., two women), which leads to entangled subject regions during composition. In contrast, our subject priors are derived per-LoRA and remain instance-aware. We report the quantitative comparison Table
Table 5: Quantitative comparison with ConceptAttention [11] multi-subject scenarios. We perform our comparisons using the same benchmark in the main paper. ConceptAttention fails to retain distinct identities when multiple similar concepts present.
Method
ID 1
CLIP-T f
HPS
DINO 1
ConceptAttention
0.362
0.250
6.189
0.261
Ours
0.532
0.252
6.124
0.261
23

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Ours
OmniGen
MS MS- -iffiffusion
Figure Generations with occlusions. When two concepts overlap spatially (e.g., a human identity and object in the same region), competing approaches partially overwrite distort one of the concepts. LoRAShop preserves both concepts in the final output.
B.3 Quantitative Comparisons on Non-ID Concepts
We additionally evaluate LoRAShop on non-human concepts using the multi-subject bench- mark setup adopted in UNO. Specifically, we train 18 LoRAs using the concepts included in the DreamBooth dataset, form 10 concept pairs, and generate compositions using the standard prompt templates introduced by [30]. We report the quantitative comparisons in Table 6 As also reported in the quantitative evaluations, our method scores either best or second-
all benchmarks.
Table 6: Multi-subject comparisons with non-human concepts. We additionally provide quantita- tive comparisons on multi-subject composition task with non-human concepts. For our evaluations, train 18 LoRA adapters from the DreamBooth dataset and form 10 concept pairs.
Method
CLIP-T 1
HPS
Aesthetics t
DINO 1
MS-Diffusion
0,483
0.291
5,609
0.446
MIP-Adapter
0,414
0.244
5,561
0.383
OmniGen
0.468
0.273
5.960
0.364
UNO
0.493
0.301
5.960
0.383
Ours
0.487
0.307
5.923
0.473
B.4 Comparisons Occluding Concepts
We also evaluate cases where two concepts occupy overlapping or partially occluded regions. In these scenarios, baselines often merge partially overwrite one of the concepts, while our method preserves both concepts the final composition. we provide qualitative examples Fig. Specifically, we use the LoRA adapter corresponding to concept <Pir> and <Teapor> (from DreamBooth (30) dataset. Additionally, we provide quantitative comparisons for this generation in Table We use the prompt "a teapot with face embossment on it, on dinner table."

---

Table 7: Quantitative Comparisons on Occlusion Cases. To quantitatively evaluate the performance of our method on occlusion cases, we perform compositions with the concepts <Pitr> and <Teapor>. In our comparisons we perform 50 generations per method with the prompt teapot with face embossment on it, on a dinner table". To measure the concept consistency, we report ID score for the
human identity, and DINO score for the teapot.
Method
ID t
DINO f
CLIP-T f
HPS f
Aesthetics 1
MS-Diffusion
0.308
0.645
0.528
0.298
5.534
OmniGen
0.286
0.625
0.520
0.286
5.735
Ours
0.651
0.645
0.581
0.326
5.818
Table Full-body identity preservation user study. We report pairwise user preferences for full- body identity preservation, where participants evaluate the identity transfer capabilities of competing methods under the same prompt set, where I corresponds to poor and corresponds to excellent. LoRAShop is consistently preferred when the personalization extends beyond the face region to full body transfer.
OmniGen
Infinite
Ours
User Preference
2.608
2.848
3.228
B.5 Evaluations on Full Body Identity Transfer
In addition face-centric identity transfer, we also conducted user study on 40 personalized samples to evaluate full-body identity preservation. We follow the same pairwise preference protocol our main user study: participants matches the target identity for given prompt, while specifying that the task is full-body identity preservation. We provide the results of our user study in Table where we compare our method with OmniGen 45 and Infinite You [16].
shown two images side-by-side and asked which one better
The results indicate that LoR AShop is preferred over the competing approaches in the majority of comparisons, demonstrating that the extracted subject priors enable identity transfer at a whole-body level, not only at the facial region. These findings align with our qualitative results and confirm that the method generalizes to fine-grained, full-body personalization rather than relying solely on facial matching signals such as ArcFace.
C Supplementary Generation and Editing Examples
Supplementary to the editing and generation examples provided in the main paper, we provide supplementary results from LoRAShop in this section. Specifically,
provide examples of four subject generation in Fig. 10 three subject generation in Fig. IT two subject generation [2] and combination of human and non-human adapters in Fig. 13 As we demonstrate qualitatively, approach can both handle multiple instances of the same type of entities (e.g. woman) and different type of entities (e.g. man, sunglasses, clothing).
D Detailed Masking and Blending Algorithm
To further clarify the details of our method, we provide detailed descriptions subject prior extraction and residual blending scheme introduced in the main paper. For the subject prior extraction, we provide the details of blob construction algorithm in [ In addition, to further clarify the blending process, we describe the blending process for given residual (from a transformer block) in Alg. 2 Note that this blending operation applicable for all residual features outputted inside the transformer blocks.
25

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knight, on elf, sorcerer and superhercine standing in sunny forest
Women in red. blue, black and green suils, playing poker in casina
Women in floral dress, french coal. a business suit and red dress, walking downtoow
Figure 10: Multi-subject composition results four human subjects. As our approach does not rely on any other external conditioning like pose conditioning, LoRAShop can utilize the generative capabilities of FLUX, and thus generate outputs with high fidelity and superior prompt alignment. In the provided examples, we utilize the concepts <Margot>, <Gal>, <Kiernan> and <Beer>
portrait photo of three men attending frien''s wedding, in sunny garden
a gala setting, three men wearing suits in itaion style, in a luxurious dinner
Three men in gym, wearing sport clothes
Figure 11: Multi-subjeett composition results for three subjects. We provide generation results for the subjects <Pitt>, <Elon> and <DiCaprio>. We provide generation results on three different generation prompts, with different compositions of the subjects.
26

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a woman black business suit, next to a woman in a beige blazer, modern office setting
two men sitting in a park, wearing casual clothes
a man and woman, having a business dinner in a fancy restaurant, posing for a photo
Figure 12: Multi-subject composition results for two subjects. We provide generation results for the concepts <Armas>, <Sabrina>, <Pitt>, <DiCaprio>, As we demonstrate in the examples, LoRAShop can perform compositions between the same type (e.g. woman-woman) and different type (e.g. man-woman) of identity concepts.
E Experiment Details
In this section, we provide supplementary details on our quantitative evaluations and provide the specifics the metrics utilized and the prompt sets used. We provide the prompts that use for the evaluation of the single-subject and multi-subject generation tasks in Table 12 and Table [3] where we generate the prompt set with [4). In the following, we provide the details for each the metrics that we use in our evaluations.
ID: We use the InsightFacce ce codebase for the ID similarity metric. Specifically, ArcFace 77 embeddings provided in their implementation, using the buffalo_1 variant.
CLIP: To assess text-to-ing text-to-image similarity for single/multi subject generation tasks, and image-to-image similarity for face swapping benchmark, utilize CLIP 27 feature extractor. In all of our experiments, we use the big-G variant of the mode
HPS: As secondary metric to quantify text-to-image alignment, we utilize the Human Preference Score (HPS), which is fine-tuned with user preferences. In experiments, use the HPSv2 variant.
httpp///gtthhbbccmm/eeppssssssss hctpa///huugiiggaaee..////aooo/p https://ggthuu.cmm///ss/////////
144-aainn2B.... -ww.nn----------- 39B-b1601
27

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a woman posing in front of car
a man/woman and a cat, playing together in a sunny backyard
man/woman with sunglasses. wearing a jacket. Standing in the downtown at night
Figure 13: Multi-subject composition results generated by method different types of objects. As can be seen the examples LoRAShop can perform combinations between different types of concepts.
To assess the quality of the generated images, we utilize the aesthetics score for single and multi subject generation tasks. We the second version of the predictor in all of our experiments
Aesthetics:
DINO: As secondary metric assess the imput preservation for the face swapping task.
we use DINO for our benchmark. We use the checkpoints from http::tt// htttps:/huugiinfaatt co/faceeook//inoo2-baee
LPIPS: Following the common practice from image editing tasks, utilize L.IPS (50)
score with VGG 35 backbone.
For all of the competing approaches, we use the default hyperparameter setups and their corresponding official implementations.
F List of LoRA Adapters
We provide complete list LoRA adapters used in this section. Specifically, we provide the list of the LoRA adapters for woman subjects in Tab. g man subjects in Tab. and non-human subjects in Tab. [T] For each of the adapters, we provide representative images for each, to help readers identify the subjects. Note that, provide this list legend, where the adpater icons in match with the ones used the main paper. As exceptions, we train the LoRA adapters for <Gosling> and <Lebron> using Dreambooth [30]
http:///wataiiacccco//mmm////ltt sac- sac-ssac----------- aval 114- linearMSE
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Original
+
*aatson>>
+Gaa>
+<Kierna>>
+Beer>
Figure 14: Face Swapping results with LoRAShop. As we demonstrate in the provided examples, our editing approach offers a seamless blending between the input subject and the target identity, while preserving the input characteristics.
29

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Original
+<aatso>>
+Gaa>
+<Kierna>>
+Beee>>
Figure 15: Face Swapping results with LoRAShop.
Algorithm HOMOGENEOUSBLOB
Require: Soft mask M E [o, 1] Bx x1, image size (H, W), Gaussian size k, variance threshold
I, maximum passes P. mode flatten, distance parameter À
1: M + reshape(M (B, 1, H, W))
2: G < Gernelan
3: M < renorm(M)
b 0-1 scaling
4:
for p to P do
5: M <- renorm(com-2d G))
6: if every batch sample has connected component above then
7: break
8: end
9:
end for
10:
> Homogenise the blob Use the global peak as single-pixel marker Flood-fill outward until original mask intensity reached b Rescale result to (0, 1]; yields flat, uniform blob
11: P +- [[M=
M <- rerphhorooouuucP... M)
13: M <- renorm(M (M)
M <- reshape(M, (B, H H=W,1))
15: return M
30

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Algorithm 2 BESEDIGL
Require:
base E RBxC
D residual from frozen backbone > residuals from LoRA adapters
( RXC for k N
Me E 10. for k 1 N
token-wise subject priors D. indices of image tokens avoids divide-by-zero b blended residual tensor
I IC (1,....
E small constant
Ensure: R RXC
1: for each token index p 1,
S do
2:
if p g Z then
b prompt token: no blending
3:
R(p) < R buse (p)
4: continue
5: end if
6: sumMask < >M Mp(p)
7: if sumMask = 0 then
background token
8: R(p) - R R.aee (p)
9: else
token claimed by a subject
10: for k to N do
11:
ag - (sumMask + E)
normalise prior to a weight
end for
13:
R(p) < E an (p)
blend adapter residuals according to weights
14:
end if
15:
end for
return R
ready for the block's skip connection
31

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Adapter Icon
Adapter Tag
URL of the Adapter
<Armas>
Armas _rmas
httess//hanggggggeeecccooopou/us eilish
<Billie>
https://hwgghhaaaceeccco/uuuuurp watson
<Watson>
<Gal>
http:://wwgggaaaa.ccccc///////ap shipka
<Kiernan>
http:://aagginnaeee.ccc//uuuauup robbie
<Margot>
<Margot>
http::/wwwg.aggeeecccc/////////e beer
<Beer>
http:://hhuginnnaeccecc////ahhhh aarr-n--carppateee--------e
<Sabrina>
httee://hhugiingaaccc.o/////////
<Taylor>
Tayloršwift-Pluxlorr
Table 9: Image-and-text comparison table.
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Adapter Icon
Adapter Tag
URL of the Adapter
<Dicaprio>
Ieonardo-dicaprio
<Pitt>
<Lee>
http:://wwgggaaaaeeeecc//peppeer lee
htttaa//hnnggiiiieeeeco///eeeeer lora. elonmusk
<Elon>
<Messi>
Table 10: Image-and-text comparison table.
33

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Adapter Icon
Adapter Tag
URL of the Adapter
http:://huggingfacc.cco/tttttt lumiva-glasses
<umiva>>
otte:///hagciagaaoo.o.o////////3 bibridd_bomber.jaccceee in_pappa
<Jacket>
attp:///hagiiigfaeeeecco/ttttttr moccler-dress-1000vv
<Dress>
<Tower>
attp:://hugianffae...//hhhbbh flux-lora-eric--e
<Cat>
httpss//hugiaggacccco///sss21313
<Royce>
flu-LLrra-carrrolls-roooo
Table 11: Image-and -nx-text comparison table.
34

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ID
Prompt
P1
woman/man rendered in stylized manner is centered in the image, standing in front of a backdrop expressive brushstrokes and vibrant color blocks.
P2
woman/man illustrated in pencil is centered in the frame, with fine shading and linework defining her/his face, placed against a softly sketched background. woman/man illustrated with smooth digital brushwork is centered in the image, with soft ambient lighting and a clean gradient background behind her/him.
P3
P4
woman/man rendered in art deco style is centered in the scene, framed by angular gold patterns and symmetrical borders in an ornate composition.
P5
woman/man is centered in the image, rendered in cyberpunk painting style with neon reflections casting pink and blue highlights across her/his face, glowing circuitry traced along her/his cheekbones, and blurred futuristic cityscape of holograms and rain-soaked signs behind her/him.
P6
a woman/man with happy expression, sitting near a tall window with natural light falling across her/his face, while shadows from nearby plants frame the soft background. a beautiful woman/man is centered in cozy room filled with bookshelves and warm lighting, her/his face lit by glowing screen as she/he laughs during a video call. a woman/man with a nervous expression on a misty morning trail, the background gently blurs into distant trees and dew-covered grass. a woman/man with happy expression relaxed expression, surrounded by ingredients and subtle reflections from the counter. a woman/man centered a café table, sketching in a notebook with soft light falling on her/his face, as the background softly fades into rustic textures and furniture.
P7
P8
P9
warmly lit kitchen, preparing a meal with a
P10
P11
a woman/man knight with a fierce expression, wearing intricately detailed medieval armor, standing battleficld at sunset as orange light reflects off her/his head and the silhouettes of fallen weapons surround her/him.
P12
woman/man sorcerer is centered in the image, casting glowing spell with both hands, her/his face illuminated by swirling magical energy, while runes float in the air and a faint pulses around her/him in the twilight mist.
P13 a futuristic cyborg woman/man is centered in the image, with a metallic faceplate, cybernetic implants across her/his jaw and temple, and glowing blue circuitry along her/his neck, standing in front of a neon-lit skyline under a starless night sky.
P14
woman/man dragon rider is centered in the image, her/his face framed by windswept hair and a dark leather hood, with the neck of black-scaled dragon behind her/him and storm clouds swirling in the sky, her/his expression fierce and focused wind lifts her/his cloak around her/his shoulders.
P15
a happy woman/man elf portrait photo setting, with long silver hair and pointed ears, cloaked in forest forest-green green robes, standing beneath ancient glowing trees in an enchanted forest where magical particles float in the air and moonlight streams through twisted branches.
Table 12: Prompt list for single-subject generation.
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ID
Prompt
P1
a close-up profile photo of woman in a red suit with slicked-back hair and defined brows, next to a woman in a green suit with soft curls and warm smile, both standing side by side under office hallway lighting, posing to the camera.
P2
a headshot-style image of woman in white lab coat with glasses and sharp jawline, beside a woman navy scrubs with tied-back hair and a round face, both facing forward in hospital corridor.
P3
a portrait-style image a woman in a floral dress with curly blonde hair and bright eyes, and woman in denim jacket with straight black hair and neutral expression, both seated on a park bench, looking at the camera.
P4
profile photo of woman in black business suit with confident expression, next woman a beige blazer with a composed look, both looking directly at the camera in a modern office setting.
P5
a woman in a crisp chef's uniform with her hair neatly tied back and a confident expres- sion, and woman in barista apron with short bangs and friendly smile, both posed for professional headshots in a warmly lit café interior.
P6
a head-and sead--and-hool anouddouss photo of woman in athletic wear with her hair tied up and a serious look, next to a woman in a hoodie with loose strands and a light smile, both standing on track field at sunrise. a portrait-style image of a woman in a yellow raincoat with damp bangs and a composed face, and a woman under black umbrella coat with a cheerful smile, both captured walking side by side on a rainy city street.
P7
P8
a softly lit bridal portrait of a bride in a white wedding dress with glowing makeup, alongside a bridesmaid in navy gown with a calm expression, both facing forward in a bridal room setting.
P9
a posed gala portrait of a woman in red evening gown with defined features and dramatic makeup, beside a woman in a silver sequin dress with soft curls and a neutral expression, both under spotlight lighting. a construction site ID photo of a woman in a safety vest and hard hat with firm gaze, next woman holding blueprints with glasses and composed face, both framed in the foreground.
P10
P11
portrait of woman holding camera in casual wear with focused look, and a woman with a soft gaze in a long white dress, both photographed at golden hour in a field. greenhouse portrait of woman in green apron with tied-back hair and relaxed expression, next to a woman in a plaid shirt with a gentle smile, both facing the camera with greenery in the background.
P12
P13
a coastal roadside profile photo of a woman in a black motorcycle jacket with bold lipstick, and a woman both posing beside a scooter.
sundress holding an ice cream cone with cheerful expression,
P14
a woman with calm expression sits café table, her face softly lit and clearly framed in the image; and a woman beside her with gentle smile turns slightly toward her, both captured from the shoulders up in a warm, relaxed atmosphere with the background softly out of focus.
P15
a college campus profile photo of graduate woman in black gown and cap with a proud smile, and woman in floral dress holding a bouquet with joyful expression, posing for a graduation photo.
Table 13: Multi-subject prompts used in evaluation.
36


Reviews:
[{"review_id": "ip8KudsZ4R", "reviewer": "Reviewer_o5bb", "summary": "LoRAshop devises a method for applying the combined effect of multiple LORA customziations on the same synthesized image. It operates by narrowing the effect of a specific LORA to a masked region of the generated image.  LORAshop defines a method for automatically deriving a mask from a \"subject prior\" by running the generation process up to an intermediate timestamp, then ensuring that the mask corresponding to each LORA does not overlap; then subsequently restricting the action of each LORA to a corresponding mask.  The paper conducts evaluations including human evaluations compared to several baseline methods that are capable of combining multiple custom subjects in an image.", "questions": "[My original questions are below.  I have revised the rating in light of the author's response.]\n\n* What is the performance of the method (especially compared to non-explicit-masking to baselines) when the multiple customized subjects have overlapping effects? For example, when one subject says is \"as shown as a decoration on a vase\" and another subject is \"the person looks like Brad Pitt\"; does the method succeed in drawing Brad Pitt on a vase?\n* Some previous mask-oriented methods have previously focused on good performance in the presence of occlusions. Has this paper tested performance on these cases?  How does it do, and how does it compare to the previous occlusion-friendly methods?\n* The paper mentions some failure cases. What do these look like?  Why aren't examples shown and discussed in detail?", "limitations": "Although a few words are given about the technical limitations of the paper, the scope of the paper is very limited when compared to baseline methods that, for example, previously combined multiple subjects using open-text instruction descriptions instead of masks (like Omnigen) or that attempted to combine subjects that could overlap and affect the same pixels of the target image (like MS-Diffusion).  This limitation should be explored in more detail, with failure cases comopared to previous mthods explicitly measured and shown. The method seems like it might be superior for a narrow and important use-case, but this narrow scope should be clarified and put in context with previous work that may be superior for other uses.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: the paper's method is simple, understandable. The paper selects a reasonable set of baseline methods to compare to, and benchmarks suggest that it performs well against baselines.\n\nWeaknesses: the paper does not measure performance on difficult multi-subject tasks such as where subjects overlap in the same region of the target image. In particular, by imposing the assumption that different subjects should correspond to non-overlapping masks, the paper implicitly raises the question of whether it suffers from the \"bitter lesson\" in which its performance win might be too narrowly tailored and will suffer compared to previous methods such as OmniGen and MS-Diffusion that apply learning methods to more flexibly combine mutiple subjects. This potential drawback should be discussed in detail, and measured. The paper does not appear to measure any tasks in which the subjects might overlap. It also does not show any failure cases in detail. Since many of the baseline methods attempt to address these more-difficult cases, the paper should compare its performance on this basis\n\nThe paper would be significantly strenthened if it discussed its limited scope in more detail, showing both wins and losses and discussing tradeoffs. Instead, by not investigating difficult cases, and exploring potential disadvantages, it effectively overclaims by implicitly claiming unvarnished superiority over other methods that have previously attempted more difficult forms of the multi-subject customzation task.", "was_revised": true, "final_justification": "The authors have answered my main concerns clearly and I have revised my rating to \"accept\".", "rebuttal": "We thank reviewer o5bb for the valuable feedback and finding our method simple, understandable and well evaluated. Please find our responses to the raised concerns below.\n\n**Q1: The paper does not measure performance on difficult multi-subject tasks such as where subjects overlap in the same region of the target image. (...) The paper does not appear to measure any tasks in which the subjects might overlap.**\n\nWe thank the reviewer for this insightful comment. We would like to clarify that our paper and appendix contain multiple examples demonstrating overlapping subjects within the same spatial region (please see details below). We would also like to address any potential misunderstanding: while our method indeed emphasizes non-overlapping masks for clarity in multi-subject compositions, it does not inherently impose a limitation against handling overlapping concepts. Specifically, our method effectively handles overlapping subject regions by performing an attention-driven residual blending during the denoising process, allowing multiple LoRAs to jointly influence the same spatial region without explicit retraining or constraints.\n\nWe provide several practical demonstrations of successful overlapping cases in our experiments. For instance, in Fig. 1(a) of the main paper, we combine identity, clothing, and accessory LoRAs` <Lumiva> (eyeglasses), <DiCaprio> (identity), and <Jacket> (clothing)`. Similarly, our Appendix Fig. 4 illustrates further combinations, such as `<Lumiva>, <Messi>, <Jacket>` and `<Lumiva>, <Armas>, <Jacket>`, showcasing robust handling of overlapping LoRAs.\n\nFurthermore, Appendix Figs. 3 and 4 depict closely interacting subjects, featuring scenarios where subjects (each represented by individual LoRAs) hold each other around the waist (Fig 3), place hands on each other's shoulders (Fig 3), hold a LoRA cat on their lap (Fig 4), or stand directly in front of a LoRA car (Fig 4). These examples underscore our method’s capability to effectively handle complex spatial overlaps.\n\nNote that the details about these LoRAs are available in Tables 1, 2, and 3 of our supplementary material. We will elaborate further on our method’s capability to handle overlapping subjects in the revised manuscript.\n\n**Q2: What is the performance of the method (especially compared to non-explicit-masking to baselines) when the multiple customized subjects have overlapping effects? For example, when one subject says is \"as shown as a decoration on a vase\" and another subject is \"the person looks like Brad Pitt\"; does the method succeed in drawing Brad Pitt on a vase?**\n\nThank you for this suggestion. As requested, we performed an experiment on this complex case, involving a teapot LoRA (trained from Dreambooth dataset) and Brad Pitt LoRA.  Our evaluation consists of 50 generated images based on the prompt “a teapot with a face embossment on it, placed on a dinner table,” where both the teapot and the face are represented by corresponding LoRAs. \n\nTo thoroughly assess LoRAShop's performance relative to the requested baseline methods (OmniGen and MS-Diffusion), we evaluated the generated images using multiple metrics: ID and DINO scores for concept similarity, HPS and CLIP-T for text-image alignment, and the Aesthetics score for visual coherence. The results are summarized in the table below. \n\n| Method       |   ID  |  DINO | CLIP‑T |  HPS  | Aesthetics |\n|--------------|-------|-------|--------|-------|------------|\n| MS‑Diffusion | 0.308 | **0.645** | 0.528 | 0.298 | 5.534 |\n| OmniGen      | 0.286 | 0.625 | 0.520 | 0.286 | 5.735 |\n| Ours         | **0.651** | **0.645** | **0.581** | **0.326** | **5.818** |\n\nAs demonstrated by our experiments, LoRAShop excels at handling these complex scenarios, providing superior identity preservation and accurately representing the target object (teapot).  We also note that unlike approaches like OmniGen and MS-Diffusion, which necessitate full model retraining, LoRAShop works directly at inference time without explicit retraining or additional constraints.\n\nDue to NeurIPS guidelines, we are not permitted to share qualitative results at this review stage; however, visual examples will be provided in the camera-ready version. \n\n**Q3: Some previous mask-oriented methods have previously focused on good performance in the presence of occlusions. Has this paper tested performance on these cases? How does it do, and how does it compare to the previous occlusion-friendly methods?**\n\nWe thank the reviewer for raising this important point. As a comparison with occlusion-friendly methods, we provided quantitative evaluations against \"Kong et.al., OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models (2024)\". These comparisons, including multi-subject evaluation metrics, are detailed in Table 4 of the main paper, and user study results are presented in Table 1 (main paper). Our method consistently outperforms existing occlusion-friendly methods in both sets of evaluations. Additional qualitative results will be included in the camera-ready version.\n\nMoreover, we provide qualitative comparisons against concept-merging approaches such as Mix-of-Show, Orthogonal Adaptation, Prompt+, and LoRACLR in the supplementary material (Figs. 7, 8, and 9). These qualitative assessments demonstrate that our method produces diverse and occlusion-tolerant compositions without relying on external pose guidance, effectively leveraging the high-quality generative capabilities of the base model, unlike the comparative methods that necessitate external pose inputs to distinctly separate entities.\n\n\n**Q4: The paper mentions some failure cases. What do these look like? Why aren't examples shown and discussed in detail?**\n\n\nThank you for highlighting this important point. In our initial manuscript, we briefly mentioned technical limitations however we acknowledge the importance of clearly illustrating and discussing failure cases. \n\nSince our method relies on object locations extracted from the attention representations learned by FLUX, the performance of our method is limited by the robustness of the model in multi-subject scenarios. As also mentioned in the official model checkpoints of the Flux.1-dev model, the developers state that “The model may fail to generate output that matches the prompts.” (please see Huggingface’s Flux.1-dev webpage for more details).\n\nTherefore, while our method effectively handles scenarios with up to four subjects (as illustrated in Appendix Fig. 9), accuracy declines when attempting to generate a higher number of subjects (e.g., six or seven). This decrease in accuracy occurs due to FLUX's inherent blending of attentions when numerous subjects are simultaneously generated.\n\nWe note that this limitation is a broader ongoing challenge in the recent multi-concept personalization literature. Recent approaches such as LoRACLR, Orthogonal Adaptation, and Mix-of-Show address this by explicitly employing additional conditioning information (e.g., pose or keypoint inputs via ControlNet). In contrast, our approach uniquely handles multi-subject composition without any explicit mask or external conditions.\n\nWhile we are not allowed to share visual examples due to NeurIPS’s rebuttal guidelines,  we will provide comprehensive qualitative examples and a detailed discussion of these challenging cases in the camera-ready version. \n\n \n\n\n**Q5: The paper would be significantly strengthened if it discussed its limited scope in more detail, showing both wins and losses and discussing tradeoffs. Instead, by not investigating difficult cases, and exploring potential disadvantages, it effectively overclaims by implicitly claiming unvarnished superiority over other methods that have previously attempted more difficult forms of the multi-subject customization task.**\n\nThanks for the opportunity to clarify the novelty and scope of our work. To the best of our knowledge, our paper introduces the first multi-LoRA 'editing' framework in the literature, distinguishing it from previous methods limited to composing entirely new images. Unlike alternatives such as face-swapping or inpainting, which primarily handle changes restricted to the face region, our approach effectively captures and reflects the full identity associated with LoRAs, including both facial and body-level features. Furthermore, LoRAShop is the first framework capable of performing multi-concept tasks specifically with Flux models.\n\nMoreover, our method addresses complex multi-subject customization tasks in a fully training-free manner. Unlike existing approaches like OmniGen and MS-Diffusion, which necessitate full model retraining, LoRAShop uniquely facilitates flexible multi-concept editing and composition directly at inference time without explicit training or additional constraints.\n\nIn addition, unlike recent multi-LoRA composition methods such as LoRACLR, Orthogonal Adaptation and Mix-of-Show, our method does not require additional conditioning information (e.g., pose or keypoint inputs via ControlNet) and handles multi-subject composition without any explicit mask or external conditions.\n\nOur comprehensive evaluations demonstrate LoRAShop’s robust capability to handle challenging scenarios, including overlapping subjects, intricate subject-object interactions, and complex spatial relationships. In our main paper, we provide an extensive number of visual examples showcasing these capabilities (45 examples in total), supplemented by additional examples in our Appendix (75 examples in total).\n\nWe recognize the importance of transparently outlining both strengths and limitations. Therefore, in our revised manuscript, we will provide a detailed discussion on trade-offs, explicitly highlighting specific challenging scenarios and comparative strengths to ensure a balanced and thorough assessment of our contributions relative to existing methods."}, {"review_id": "byThgwPxFE", "reviewer": "Reviewer_tkqk", "summary": "This paper proposes LoRAShop, a novel training-free framework for multi-concept image generation and editing using pre-trained LoRA. The overall framework first extract disentangled spatial priors for each concept. Then, these priors are then used to guide residual feature blending. Quantitative and qualitative results demonstrate the effectiveness of LoRAShop on various tasks.", "questions": "Questions: \n\n1. Could the processing in lines 133–135 potentially lead to additional error?\n2. How would the method perform on overlapping concepts (e.g. a couple embracing each other)\n3. Could the framework benefit from the learned hyperparameters, such as $\\tau$.", "limitations": "None", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Sufficient Experiments.\n2. Generalization. The proposed framework can support various tasks. \n3. Novelty. Propose a training-free framework without tuning the LoRA adapters.\n4. Practical Impact. The training free nature makes the framework efficient, supports both generated and real-image editing.\n\nWeaknesses:\n\n1. Visual Clarity. Figure 2 can be designed more accurate to illustrate the overall framework.\n2. Limitation of Distangled Masks. The extracted mask may inherit biases from the 3x3 Gassian kernel.", "was_revised": true, "final_justification": "My concerns are addressed. I am happy to vote for acceptance.", "rebuttal": "We thank reviewer tkqk for the valuable feedback and for finding our method *well-evaluated, generalizable, novel,* and *practical*.  Please find our detailed responses to the raised concerns below.\n\n**Q1: Figure 2 can be designed more accurately to illustrate the overall framework.**\n\nWe thank the reviewer for the feedback regarding the framework figure. While we are unable to include an updated version in this rebuttal due to formatting constraints, we will revise the figure in the camera-ready version to improve clarity. Additionally, the prior extraction and blending procedures are detailed in Algorithms 1 and 2 in the supplementary material for further reference.\n\n**Q2: The extracted mask may inherit biases from the 3 × 3 Gaussian kernel.**\n\nWhile we acknowledge that Gaussian kernels may introduce biases, we select them because they emphasize regions with stronger attention values and progressively suppress low-confidence areas over successive iterations. In the camera-ready version, we will illustrate how the mask structure evolves across iterations. Notably, due to this effect of the Gaussian kernels, the subsequent arg-max operation can correctly separate subjects by prioritizing regions with stronger attention responses relative to others.\n\n**Q3: Could the processing in lines 133–135 potentially lead to additional error?**\n\nThank you for raising this implementation detail. In the processing described in lines 133–135, we apply connected-component analysis to improve efficiency by capping the number of Gaussian filtering steps. Concretely, the loop terminates after at most ten iterations when a single connected component has not yet emerged. We examined the impact of the masking-threshold parameter in the ablation studies shown in Figure 4 of the main paper; as illustrated, choosing a threshold in the 50% – 70% range yields the highest-quality subject priors. We appreciate the opportunity to explain this design choice and will highlight these findings more clearly in the revised manuscript.\n\n**Q4: How would the method perform on overlapping concepts (e.g., a couple embracing each other)?**\n\nThank you for highlighting this important use case. Our submission already demonstrates LoRAShop’s ability to handle overlapping subjects in both qualitative and quantitative settings. Figure 1(a) of the main paper blends three LoRAs: <Lumiva> (eyeglasses), <DiCaprio> (identity), and <Jacket> (clothing), within the same spatial region, while Appendix Fig. 4 shows additional overlaps such as <Lumiva>, <Messi>, <Jacket> and <Lumiva>, <Armas>, <Jacket>. Appendix Figs. 3 & 4 further depict closely interacting subjects: two identities holding each other around the waist or placing hands on each other’s shoulders, a subject with a LoRA cat on their lap, and another standing directly in front of a LoRA car.\n\nBeyond these visual examples, we conducted a dedicated experiment to quantify performance on overlapping concepts. We generated 50 images with the prompt “a teapot with a face embossment on it, on a dinner table,” which combines a DreamBooth-trained LoRA for the teapot with a pretrained <Pitt> identity LoRA (see Supplementary Table 2 for adapter details). The results, summarized in the accompanying table, show that LoRAShop outperforms MS-Diffusion and OmniGen in preserving both the teapot and face concepts (ID and DINO metrics) while achieving higher text–image alignment (CLIP-T and HPS) and visual coherence (Aesthetics). Full adapter specifications are provided in Supplementary Tables 1–3, and we will elaborate on these capabilities in the revised manuscript.\n\n| Method |  ID  | DINO | CLIP-T |  HPS  | Aesthetics |\n|--------|:----:|:----:|:------:|:-----:|:----------:|\n| MS-Diffusion | 0.308 | **0.645** | 0.528 | 0.298 | 5.534 |\n| OmniGen      | 0.286 | 0.625 | 0.520 | 0.286 | 5.735 |\n| **Ours** | **0.651** | **0.645** | **0.581** | **0.326** | **5.818** |\n\nAs shown by the quantitative results, our method outperforms competing approaches in preserving both the teapot and face concepts (evaluated by ID and DINO metrics), while also generating images with higher text–image alignment (evaluated by CLIP-T and HPS scores) and improved visual quality (evaluated by the Aesthetics score).\n\n[1] Ruiz et. al., \"DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation\", CVPR 2023\n\n**Q5: Could the framework benefit from learned hyper-parameters, such as T?**\n\nThank you for this insightful suggestion. While our current implementation utilizes fixed hyperparameters (such as the timestep T) for simplicity and ease of inference, exploring learned or adaptive hyperparameters could indeed further enhance the performance and robustness of our framework. We agree that dynamically adjusting hyperparameters like T based on the complexity or characteristics of the input scenario could yield improved results. We will explore this promising direction as future work and discuss its potential benefits clearly in the revised manuscript."}, {"review_id": "cokAkrxXaJ", "reviewer": "Reviewer_7KJw", "summary": "This paper introduces LoRAShop, a training-free framework for multi-concept image generation and editing using LoRA adapters in diffusion models. The insight is transformer features in Flux-style diffusion models activate spatially coherent regions early in denoising, enabling the extraction of disentangled subject priors. LoRAShop performs a forward pass to extract coarse masks delimiting concept regions, then selectively blends LoRA adapter features only within these regions during reverse diffusion. This approach eliminates \"LoRA cross-talk\" without retraining, allowing simultaneous use of multiple adapters. The method supports both generating new multi-concept images and editing existing images by inserting personalized concepts. Extensive experiments demonstrate superior identity preservation over baselines in both generation and editing tasks, with applications ranging from artistic creation to identity transfer beyond simple face swapping.", "questions": "1. Generalization Beyond FLUX:\nQ: Can LoRAShop’s mask extraction work with convolutional U-Nets (e.g., Stable Diffusion)? If not, what architectural properties are essential?\n2. Full-Body Identity Evaluation:\nQ: ArcFace only evaluates faces, but Fig. 3 shows body edits. How is full-body identity preserved quantitatively?\n3. Computational Overhead:\nQ: How much memory does simultaneous multi-adapter blending incur compared to sequential application?\n4. Minor issues:\n* Typos. 'Expermential Setup We use ... ' -> 'Expermential Setup. We use ... '. L176, P5.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n1. This work addresses a critical practical limitation—the inability to combine LoRAs without interference—that has hindered real-world deployment. By solving multi-adapter composition in a training-free manner, it transforms LoRAs into practical \"photoshop-like\" tools, enabling new creative workflows. The insight about spatial activation patterns in diffusion transformers and the subsequent mask extraction/blending mechanism represents a conceptual leap beyond existing LoRA composition methods. The approach cleverly bypasses the need for joint training or constraints.\n2. The method is technically sound and thoroughly validated. Comprehensive experiments cover single/multi-concept generation, real/image editing, and face swapping. Quantitative metrics (ArcFace, CLIP-T, HPS) and user studies demonstrate clear improvements over strong baselines (DreamBooth, IP-Adapter, etc.). Ablation studies justify key design choices (e.g., using the last double-stream block for masks).\n\n# Weaknesses\n1. The method relies heavily on Flux’s double-stream/single-stream transformer blocks. While Sec 4.3 ablates block choices, it’s unclear how transferable the approach is to other popular diffusion architectures (e.g., Stable Diffusion’s U-Net). Limited experiments on non-human concepts (e.g., objects/styles) also raise questions about generalizability.\n2. Evaluation Limitations: Identity metrics (ArcFace) focus on facial features, but LoRAShop claims full-body editing (Fig. 3). No metric evaluates body-level identity preservation. The editing pipeline uses RF inversion (Sec 3.4), but inversion errors could propagate; this isn’t quantified.", "was_revised": true, "final_justification": "With most concerns addressed, I vote for acceptance.", "rebuttal": "We thank reviewer 7KJw for the valuable feedback and finding our method effective and well validated. Please find our responses to the addressed concerns below.\n\n**Q1: Limited experiments on non-human concepts (e.g., objects/styles) also raise questions about generalizability.**\n\nThank you for drawing attention to the importance of broader experiments for generalizability. We respectfully note that both the main paper and the supplementary material already include non-human examples, for instance, Fig. 1(b) (second row, first example for concept `<Tower>`) in the main paper and Fig. 4 in the supplementary material (for concepts `<Royce>, <Cat>, <Lumiva>, <Jacket>`).\n\nSince identity preservation requires retaining fine-grained details, it poses a particularly challenging generalization task; therefore, we chose this setting for our core multi-subject evaluations. We also point out that related works such as LoRACLR and Orthogonal Adaptation report results on a similar number of identity LoRAs (12 identities). Nonetheless, to demonstrate that our method generalizes beyond human subjects, we have added further quantitative results. We trained 18 non-human concept LoRAs from the DreamBooth dataset, formed 10 concept pairs, and evaluate text–image alignment (CLIP-T, HPS), visual quality (Aesthetics), and concept preservation (DINO). We compared LoRAShop with UNO, OmniGen, MS-Diffusion, and MIP-Adapter, using the same multi-subject evaluation setup as in the main paper. The results are presented in the table below.\n\n| Method        | CLIP-T |  HPS  | Aesthetics |  DINO |\n|---------------|:------:|:-----:|:----------:|:-----:|\n| MS-Diffusion  | 0.483 | 0.291 | 5.609 | *0.446* |\n| MIP-Adapter   | 0.414 | 0.244 | 5.561 | 0.383 |\n| OmniGen       | 0.468 | 0.273 | **5.960** | 0.364 |\n| UNO           | **0.493** | *0.301* | **5.960** | 0.383 |\n| **Ours**      | *0.487* | **0.307** | *5.923* | **0.473** |\n\nAs demonstrated by our quantitative evaluations, LoRAShop consistently achieves the best or second-best scores across all criterion, establishing its effectiveness for multi-subject composition. We will include qualitative results for these compositions in the camera-ready version.\n\n**Q2: Generalization Beyond FLUX: Can LoRAShop’s mask extraction work with convolutional U-Nets (e.g., Stable Diffusion)? If not, what architectural properties are essential?**\n\nWe rely on the separability property of attention masks in the final double-stream block, as highlighted in our paper. This property of FLUX enables us to successfully blend multiple concepts within the same image, even when their spatial regions overlap (see Fig. 1(a) in the main paper and Fig. 4, third row, in the appendix). However, as long as such masks can be extracted, our method is also applicable to U-Net-based diffusion models, where masked blending of latents has been explored in prior work such as [1].\n\n[1] Avrahami et. al. \"Blended Latent Diffusion,\" SIGGRAPH 2023\n\n**Q3: Identity metrics (ArcFace) focus on facial features, but LoRAShop claims full-body editing (Fig. 3). No metric evaluates body-level identity preservation.**\n\nAs pointed out by the reviewer, standard metrics like ArcFace primarily evaluate facial similarity and thus are insufficient for assessing full-body identity preservation quantitatively. To address this limitation, we conducted a user study specifically designed to evaluate full-body identity preservation. In our study, participants were presented with full-body reference images of the subjects represented by the LoRAs (e.g., Billie Eilish in a standing pose), alongside images generated by our method and baseline methods. Participants were then asked to rate how accurately each generated image preserved the full-body identity compared to the reference image, using a scale from 1 to 5 (1 = poor, 5 = excellent). We compare the performance of LoRAShop with OmniGen and InfiniteYou, selected as the strongest baselines according to Table 3 of the main paper. Each participant was shown 40 personalized generations and asked to answer the following question: How well does the \"Output\" image reflect the full-body physical appearance of the image on the left?\n\nThe quantitative results of this user study are summarized in the table below. As shown, our method consistently outperforms baseline approaches in preserving full-body identity. We will include additional qualitative examples and a more detailed analysis in the camera-ready version to further support these findings.\n\n| Method | Pref. Score |\n|--------|:-----------:|\n| OmniGen      | 2.608 |\n| InfiniteYou  | 2.848 |\n| **Ours**     | **3.228** |\n\n**Q4: How much memory does simultaneous multi-adapter blending incur compared to sequential application?**\n\nImplementation-wise, our method applies each LoRA adapter sequentially and does not introduce any parallelism between adapters. Thanks to this sequential design, during inference we iteratively enable and disable LoRA adapters, resulting in GPU memory usage comparable to FLUX without requiring any customization. Specifically, our method uses 36 GB of GPU memory, while FLUX alone requires 32 GB. All experiments are conducted on a single NVIDIA L40 GPU.\n\n**Q5: The editing pipeline uses RF inversion (Sec 3.4), but inversion errors could propagate; this isn’t quantified.**\n\nWhile we acknowledge that existing inversion methods may introduce errors that propagate across generations, we show in Fig. 3 of the main paper that our method preserves the inverted features without introducing additional divergence from the input. Instead of using RF-Inversion to invert the image latent, we adopt **RF-Edit** [2], as specified in Sec. 3.4, a more robust inversion pipeline that introduces value sharing in the single-stream transformer blocks to enhance content preservation during inversion. Importantly, our approach is compatible with any inversion method, as demonstrated with RF-Edit, since our editing and generation strategy is formulated as a masked residual summation over existing transformer features, which preserves the original model behavior.\n\nTo further evaluate the effectiveness of our method on real images, we present quantitative comparisons on the face swapping task in Table 2 of the main paper. Despite potential inversion-related errors, our method matches inpainting-based approaches in content preservation, despite the latter being explicitly restricted from altering unrelated pixels, and surpasses them in target face identity similarity.\n\n[2] Wang et. al. \"Taming Rectified Flow for Inversion and Editing\", ICML 2025"}, {"review_id": "p5t7Wappcr", "reviewer": "Reviewer_ddJF", "summary": "To enhance the consistency of multiple IDs in image-to-image generation and mitigate the coupling effect, the author proposes leveraging the inherent semantic advantages of DiT—specifically, using cross-attention to first obtain masks for each ID to bind the corresponding LoRA. This is a simple yet effective approach, and its extension to other tasks demonstrates the robustness of the method.", "questions": "1. Why must it be the LoRA structure? Have other options been tried? \n2. It feels like the test data is insufficient—only 15 identity LoRAs. We would prefer to see more diverse results. \n3. The authors conducted ablation experiments on the mask extraction effect across different layers. Has something like ConceptAttention been attempted? Their method appears to be more robust.", "limitations": "My primary concerns are twofold:\n1) The paper predominantly focuses on facial features, leaving the effectiveness on other categories unclear;\n2) Additional mask acquisition approaches should be explored, similar to ConceptAttention.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths: The system can conveniently leverage the abundant LoRA components within the community, demonstrating strong extensibility.\n\nWeaknesses: In the quantitative results, the authors only employed identity LoRA, and the qualitative experiments primarily focused on facial images. The effectiveness on other categories remains unclear.", "was_revised": true, "final_justification": "The author explains the necessity of using the LoRA structure and provides additional experiments for more test scenes and different mask strategies. He has addressed my concerns, so I have adjusted my score to 5.", "rebuttal": "We thank reviewer ddJF for the valuable comments and finding our method as strongly extensible and beneficial for the community. Please find our responses to the concerns raised below.\n\n**Q1: Why must it be the LoRA structure? Have other options been tried?**\n\nWe chose to use LoRA adapters for two key reasons. First, LoRAs have become the de-facto standard for single-concept personalization, resulting in a large public pool of pretrained adapters that LoRAShop can readily leverage, thus enhancing the method’s practical utility and community impact. Second, prior studies such as Concept Sliders [1], HyperDreamBooth [2], and Weights2Weights [3] have shown that diffusion models organize semantic information effectively in weight space. By operating in this space through LoRA adapters, our method is able to capture full-body semantic features. While we did not experiment with alternative adapter formats, these motivations, along with LoRA’s lightweight computational requirements, make it a natural and broadly compatible choice for multi-concept composition.\n\n[1] Gandikota et. al. \"Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models\", ECCV 2024\n\n[2] Ruiz et. al. \"HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models\", CVPR 2024\n\n[3] Dravid et. al. \"Interpreting the Weight Space of Customized Diffusion Models\", NeurIPS 2024\n\n**Q2: It feels like the test data is insufficient, only 15 identity LoRAs. The paper predominantly focuses on facial features, leaving the effectiveness on other categories unclear.**\n\nThank you for raising this point. We highlight the composition examples with non-human subjects, as it can be seen in Fig. 1(b) (second row, first image for concept `<Tower>`) in the main paper and Fig. 4 in the supplementary material (for concepts `<Royce>, <Cat>, <Lumiva>, <Jacket>`). We focus on face-identity preservation because it involves fine-grained details across the concepts; related studies such as LoRACLR and Orthogonal Adaptation likewise evaluate on a comparable scale (12 identity LoRAs). To further demonstrate generalization beyond human subjects, we trained 18 non-human concept LoRAs from the DreamBooth [4] dataset, formed 10 concept pairs, and generated multi-subject compositions using prompt templates from [5]. We then measured text–image alignment (CLIP-T, HPS), visual quality (Aesthetics), and concept preservation (DINO), comparing LoRAShop with UNO, OmniGen, MS-Diffusion, and MIP-Adapter under the same multi-subject evaluation protocol used in the main paper. The quantitative results are provided in the table below.\n\n| Method        | CLIP-T |  HPS  | Aesthetics |  DINO |\n|---------------|:------:|:-----:|:----------:|:-----:|\n| MS-Diffusion  | 0.483 | 0.291 | 5.609 | *0.446* |\n| MIP-Adapter   | 0.414 | 0.244 | 5.561 | 0.383 |\n| OmniGen       | 0.468 | 0.273 | **5.960** | 0.364 |\n| UNO           | **0.493** | *0.301* | **5.960** | 0.383 |\n| **Ours**      | *0.487* | **0.307** | *5.923* | **0.473** |\n\nAs our quantitative results demonstrate, LoRAShop is the only method that consistently ranks first or second across all evaluated multi-subject composition benchmarks. To further illustrate this capability, we will include qualitative examples in the camera-ready version, which could not be provided here due to the formatting constraints of the rebuttal.\n\n[4] Ruiz et. al., \"DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation\", CVPR 2023\n\n[5] Wu et. al. \"Less-to-More Generalization: Unlocking More Controllability by In-Context Generation\", ICCV 2025\n\n**Q3: The authors conducted ablation experiments on the mask extraction effect across different layers. Has something like ConceptAttention been attempted? Their method appears to be more robust.**\n\nAs an alternative masking strategy, we include quantitative comparisons with ConceptAttention in the table below. Specifically, for the examples used in our Table 4 evaluation, we first extract subject masks using ConceptAttention (based on FLUX-generated outputs), and then perform residual blending with those masks. However, as acknowledged in the original ConceptAttention paper [6], the method has difficulty separating highly similar concepts, such as two different identities, which is precisely the setting of our evaluation. We quote the following limitation from the ConceptAttention paper:\n\n*\"The primary limitation of **ConceptAttention** is that it struggles to differentiate between very similar textual concepts. For example, for a photo with a sky with the sun in it, the model does not necessarily know where the boundary of the sun resides, instead capturing a halo around the sun.\"*\n\nIndeed, in our experiments, we observe that ConceptAttention fails to produce clearly separated masks for different identities, which leads to LoRA cross-talk and diminished identity preservation. The corresponding quantitative results are shown below:\n\n| Masking Strategy      |   ID   | CLIP-T |  HPS  | Aesthetics |\n|-----------------------|:------:|:------:|:-----:|:----------:|\n| w/ ConceptAttention   | 0.362 | 0.250 | **6.189** | 0.261 |\n| **Ours**              | **0.532** | **0.252** | 6.124 | 0.261 |\n\nAs shown by the quantitative results, LoRAShop, using the subject priors introduced in our study, successfully separates closely related concepts (such as two distinct identities), leading to superior identity preservation while performing comparably in text–image similarity and visual quality.\n\n[6] Helbling et. al. \"ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features\", ICML 2025"}]

Author rebuttal:

Meta-review: On this work, the authors introduce LoRAShop, a training-free method that can be used to generate and edit multi-concept images. To do so,  the authors first observe that they can design a disentangled mask extraction technique that can be used to localize the influence of each subject that needs to be personalized, using LoRA technique in diffusion models. This way the authors show how their method can be used to blend multiple concepts in a controlled manner in the diffusion latent. The authors show a bunch of good results both qualitatively and qualitatively to back their claims.

Originally, the reviewers scored the paper 3x Borderline Accept and 1x Borderline Reject. Two of the reviewers asked the authors to further discuss the performance on difficult multi-subject tasks such as where subjects overlap in the same region of the target image. The authors properly address this in the rebuttal (showing experimental results), and had partially addressed this in the original paper. Reviewer 7KJw asks for more extensive results in generalizability, identity metrics and memory performance which was properly addressed. In general, the other weaknesses found were relatively small and properly addressed, except using the method with other diffusion models instead of Flux which the authors could not properly address. Nevertheless, that is a minor issue.

During the authors-reviewers stage, all three positive reviewers increased the score from Borderline Accept to Accept, explicitly mentioning that their concerns have been addressed and thus are happy to raise the score. Reviewer o5bb did not respond to the authors despite the authors and the AC trying multiple times to engage with them. However, after the exchange period, they also increased the score from Borderline Accept to Accept, saying that the authors have addressed their main concerns. Considering that the reviewer was the most senior reviewer, and considering the perfect consensus between the reviewers, there is not any remaining big issue for the authors to address that would block their publication.

To summarize, the AC and the reviewers like the following aspects of the paper:

- The problem the authors address is interesting and well-executed.

- There is sufficient novelty on the paper.

- The paper is well-written.

- The experimental results, be them qualitative and quantitative are very good.

I would urge the authors to use parts of the rebuttal to improve the paper for the camera-ready version. Congratulations to the authors for a very good paper!
Decision: Accept (spotlight)