TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models
Authors: Xin Jin
Yichuan Zhong
Yapeng Tian
Venue: Accepted by TMLR ()

Abstract: Current text–conditioned diffusion editors handle single object replacement well but struggle when a new object and a new style must be introduced simultaneously. We present Twin‑Prompt Attention Blend (TP‑Blend), a lightweight training‑free framework that receives two separate textual prompts, one specifying a blend object and the other defining a target style, and injects both into a single denoising trajectory. TP‑Blend is driven by two complementary attention processors. Cross‑Attention Object Fusion (CAOF) first averages head‑wise attention to locate spatial tokens that respond strongly to either prompt, then solves an entropy‑regularised optimal transport problem that reassigns complete multi‑head feature vectors to those positions. CAOF updates feature vectors at the full combined dimensionality of all heads (e.g., 640 dimensions in SD‑XL), preserving rich cross‑head correlations while keeping memory low. Self‑Attention Style Fusion (SASF) injects style at every self‑attention layer through Detail‑Sensitive Instance Normalization. A lightweight one‑dimensional Gaussian filter separates low‑ and high‑frequency components; only the high‑frequency residual is blended back, imprinting brush‑stroke‑level texture without disrupting global geometry. SASF further swaps the Key and Value matrices with those derived from the style prompt, enforcing context‑aware texture modulation that remains independent of object fusion. Extensive experiments show that TP‑Blend produces high‑resolution, photo‑realistic edits with precise control over both content and appearance, surpassing recent baselines in quantitative fidelity, perceptual quality, and inference speed.

Published in Transactions on Machine Learning Research (10/2025)
TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models
Xin Jin GenPi Inc.
felizzinjinn@mmallcoo com
Yichuan Zhong
gichuanzhonng37@@mail.ccom
GenPi Inc.
Yapeng Tian
yopeng Hopeeng.tannuuudalllss.e
The Universily of Teras at Dallas
Reviewed on OpenReview: https: // openreview. net/ forum? id= g6NT3u0BZE
Abstract
Current text-conditioned diffusion editors handle single object replacement well but struggle when new object and new style must be introduced simultaneously. We present Twin- Prompt Attention Blend (TP-Blend), a lightweight training-five ffree framework that receives two separate lextual prompts, one specifying a blend object and the other defining a larget style, and injects both into a single denoising trajectory. TP-Blend is driven by two complemen- tary attention processors. Cross-Attention Object Fusion (CAOF) first averages head-wise attention to locate spatial tokens that respond strongly to either prompt, then solves an entropy-regularised optimal transport problem that reassigns complete multi-head feature vectors to those positions. CAOF updates feature vectors at the full combined dimensional- ity of all heads (c.g., 640 dimensions in SD-XL), preserving rich cross cross-head correlations while keeping memory low. Self-Attention Style Fusion (SASF) injects slyle at every self-attention layer through Detail-Sensitive Instance Normalization. lightweight one-dimensional Gaus- sian filter separates low- and high-frequency components; only the blended back, imprinting brush-stroko-level texture without disrupting global geometry. SASF further swaps the Key and Value matrices with those derived from the style prompt, enforcing context-aware texture modulation that remains independent of object fusion. Ex- lensive experiments show that TP-Blend produces high-resolut ion, photo-realistic edits with precise control over both content and appearance, surpassing recent baselines in quantitative fidelity, perceptual quality, and inforence speed.
residual
1 Introduction
Text-driven image editing with diffusion models Brack et al. (2024): Brooks et (0023 Sheynin et al.
22024h
Mokady al. (2023 Liu al. (2024); Tumanyan et al. [2023 Chen (024); Avrahami et al.)
b Ge et al. (2023); Shi et al. (2024); Deutch et al. (2024]: Li et al. (022bb has excelled at tasks like object replacement but still lacks robust solution for object blending, where two objects must fuse scamlessly into single coherent entity. Achicving such morphological transitions is challenging: the system must preserve each source object's 's defining characteristios (e.g., color, shape, (exture) while synthesizing intermediate attributes that accurately reflect the intended blend. This capability is especially valuable creative design, film production, product prototyping, and scientific or educational visualization, where smooth transitions (c.g., morphing a car into a spaceship or combining organisms study evolutionary traits) are often essential.

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Published in Transactions on Machine Learning Research (10/2025)
Most style transfer methods still rely reference images, limiting users to existing examples and requiring substantial effort Chhng et al. (2024) : Xing et al. (2024); Wang et al.] (2024a); Xu et al. (2024); Li (2024);
Lötzsch et al. (2022); Wang et al. (2023). By contrast, text-driven approaches Hertz et al. (2024); Zhang
ct al. b: Liu et al. (2023): Wu et al. (2024a) specify styles in natural language (c.g., "sketch- like," "art
nouveau" ") and could offer greater flexibility, yet they remain underexplored.
Additionally, current style transfer techniques face major obstacles in achieving fine- greingdd multi-scale, and region-specific control. They often fail to capture high-frequency textural details, losing subtle stylistic cues (e.g., brushstrokes, grain, intricate material features) even at high resolutions Chung et al. 2024 Xing
ct al. 2024), thereby compromising overall texture fidelity.
Motivated by these challenges, we propose Twin-Prompt Attention Blend (TP-Blend), a training- free frame-
work that extends Classifier Fesssfier-FFreeeee Guided Text Editing (CFG-TE Brack et al. (2024): Brooks et al. 2023
Sheynin et al. (2024): Mokady ct al. (2023): ) Liu et al. (0224) : Tumanyan et al. (2023 Chen et al. (2024)
Avrahami al. (2023 Ge et al. (2023) to support. fine-grained object blending and style fusion through sep-
arate textual prompts, illustratdd in Figure ! TP-Blend introduces two new modules: Cross- Attention
Object Fusion (CAOF), which integrates features from a blend object prompt using attention maps and an Optimal Transport framework; and Self-Attention Style Fusion (SASF), which injects style via Detail- Sensitive Instance Normalization (DSIN) and replaces self-attention Key/Value matrices with those from the style prompt. Unlike prior image-based approaches, TP-Blend enables direct textual control of both content and style, offering precise and independent modulation of blending strength and texture details. By unifying object replacement, blending, and style transfer within a single denoising process, TP-Blend enhances controllability without incurring additional computational overhead.
Main Contributions. (1) Dual-Prompt Mechanism decouples object and style prompts, preventing in- terference and ensuring precise content representation and faithful style transfer within a unified denoising process; (2) CAOF with Optimal Transport aligns and integrates blend-objoct features into a replaced ob- ject by treating attention maps as distributions, enabling seamless morphological transitions and preserving semantic integrity; (3) SASF leverages DSIN to extract and transfer high-frequency style features, pre- serving intricate textural details without overssmoothing while allowing adaptive modulation of stylistic attributes across different spatial extents and gramularities (4) text-driven Key/Value substitution replaces sell-attention Key/ Pey/Value /Value matrices with those derived from the style prompt, enforcing localized style mod- ulation while maintaining spatial coherence and object fidelity.
Code Availability. Code is available at
2 Related Work
Diffusion models have emerged as the dominant paradigm for text-guided image generation and editing, beginning with unconditional DDPMs and latent variants together with classifier-free guidance, which un- derpin early editing systems but leave open challenges in multi-concept disentanglement and precise regional
control DDariial & Nichol 2021 Rombach et al. 2022 Ho & Salimans 2022 Brooks et al. 2023 Brack
et al. 2024). Unified generation and editing continues to broaden capability and instruction following, span-
ning single or dual diffusson formulations, universal dynamics-aware frameworks, adapter-based unification.
and interactive or conversational editors that couple instruction tuning with in-context visual reasoning (Liu
ct al.
2025d
Yu et al., 2024 2025a; Xia et al.
2024
Zhou et al.
2025b:
Xia et al.
2025
Xiao et al.
2025b
Chen al.]
2025d
Duan et al. 2025
Fu al.
2025b
Li et al.
2025h
Jia al.
2025a
Lee et al.
2025b
Chen al.
2025c
Xu et al. 2025
Zhou et al.
2025a
Li et al.
2025g
Wu et al.
2025d
Jia et al.
2025b
Lai et al. 2025
Compositionallty, multi-conditioning and regional control are advanced by balancing or transplanting representations, order-
Mao et al. 2025a Cao et al. 2025
Chen et al.
2025b
Luo et al.
2025a).
ing and counting constraints, language-guided Lokenization and unified lokenizers, as well as training- or
atteentionreeositiooingg guidance that improves spatial faithfulness and occlusion handling (Luo et al.
2025b
Jin et al. 2025:
Cohen et al.
2024 2025; Liang ct al., 2025: Li et al. 2025d Binyamin et al.)
2025 Zeng
et al.)
2025
Hsiao et al.
2025
Qiu et al.
2025 Han et al. 2025c| Zhan & Liu 2025 Wang et al. 2024b Zha
et al.
2025
Qu et al.
2025b
Wu et al.
2024bb. Personalivation lization and identity preservation leverage feature
2

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Original Image
Object Replacement
Object Blending
Style Blending
Demonstration of our method's capabilities. Row 1: Original object "Knight" is replaced by "Leonardo DiCaprio", blended with "Batman", and styled with "Pop Art". Row 2: Original object "Robot" is replaced by "Knight", blended with "Thanos", and styled with "Cyberpunk Style". Row 3: Original object "Cat" is replaced by "Dog", blended with "Horse", and styled with "Oil painting". Row 4: Original object " "Chameleon" is replaced by "Dinosaur", blended with "Fish", and styled with "Oil painting".
Figure 1:
caching and segelardize-hhen--eesssone pipelines, continual concept learning with mitigation of forgetting and confusion, perfmntter-efficient or time-aware frequency guidance, consistency from single prompt and localized attention, LoRA merging and hypernetworks, and targeted human preference optimization [Aielo
let al.
2025
Xie et al.
2025a Guo & Jin 2025 Li et al. 2025c Liu et al.] 2025c Huang et al.] 2025c Liu
et al.
2025e
Xiao et al.
2025a Shenaj et al. 2024 Na et al., 2025 Zhu et al. 2025a Li et al.] 2025e Wang
et al.
2025d
Liu et al.
2025fb.
Style control has moved from exemplar dependence toward text-driven and
detail-fithhu
modulation through state-space modeling, selective style element control, object-centric style
editing, causal intervention and conflict-free guidance, including emotional manipulation [Liu et al. 2025a
Lei et al.] 2025 Park et al.] 2025b: Huang et al. 2025e Jo et al. 2025 Yang al., 2025d Cai et al. 2024a
Huang et al. 2025d Dang et al. 2025). Efficiency and scheduling improve with learned time prediction,
one-step distillation and tine-independent encoders, few-step or lightaing editors, second-order sampling,
fast inpainting and latent super-resolution, together with scale-down text encoders (Yee et al., 2024 2025 Luo
ct al.
2024 Nguyen et al. 2024 2025b a
Chadeboc et al.] [2025] Wang al. [025aa] Xie et al.
2025b Joong
et al.
2025 Li et al., 2025e Wang et al. 2025c). Scaling and backbone design target long-range coherence and high resolution via. hierarchical transformers, frequency attention modulation and resolution-agnostic
3

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diffusion, while autoregressive alternatives push tokenization and decoding with large tokenizers, holistic
language-guided tokenizers, deep-compression hybrids and frequency-aware modeling 7Zhag et al. 2025b; Shi et al.)
2025a
Voronov et al. 2024 Yellapragada et al.
2024
Yang et al.)
2025b Wang et al.
2025b;
Han et al. 2025b Kumbong et al.) 2025
Han et al.] 2025a Wu
et al. 2025b; Xiong et al. 2025
Zheng
et al. 2025a Zha el al., 2025 Qu el al.
2025b Wu et al. 2025e Chen el al.)
2025e) Zheng el al..
2025b;
Qu et al.) 2025a So et al. 2025). Domain and structure-aware generation spans design and linc-art images,
creative and degraded layouts with cycle consistency, unified layout planning, grounded instance-level control
and multi-view consistency, ccene-consistent camera control, controllable street views, variable multi-layer
transperency and layer decomposition, and decoupled inter and intra element conditions (Wang et al. 2025g;
2024c Zhang et al.) 2024 Cal et al.) 2024b He et al.,
2025
Wang et al. 2025e Wu et al.
2024bb
Huang
et al. 2024; Yuan et. al. 2025 Gu el al., 2025 Pu el al.,
2025 Yang el al. 2025e c
Wu et al. 2025a Safety,
fairness and provenance are studied through typographic threats, trigger- free branding attacks, robust wa-
termarking, anti-oditing and localized concept crasure, guidcline-token safcty, fair mapping and debiasing
with prompt optimization or entanglement-free attention, along with human-centrie quality metrics (Cheng
et al,, 2025 Jang et al.] 2025
Wang et al. 20251 h Lee et al. 2025a Li et al. 2025a b Um & Ye 2025
Kim et al. 2025b Park et al.
2025a Wang et al.
2025f Huang et al.
2025b). Evaluation and analysis ex-
pand with enhanced compositional benchmarks, scalable human-all alignee editing assessment, trade-off studies
and probabilistic analyses, and work on paragraph-level grounding and attribute-centric composition (Huang
le al.
2025a Ryu et al. 2025 Zhang et al.) 2025d Yu et (025b) [Cong et al. 2025 Wu et al. 2025d Xuan
et al.
2025 Zhu et al. 2025b e Ren et al.,
2025). Beyond images, image-to-video and motion-controllee
generation integrate text-to-image with text-to-video models or decouple motion intensity for one-step or
high-quality synthesis (Shi et al. 2025a Mao et al., 2025bb Su et al. 2025). Practical deployment advances
include mobile-oriented high-resolution generation (Chen et al. 2025a Additional controls include quan- tity perception, dynamic-feedback regulation, region-aware fine-tuning, collaborative attention, proportional group representations, zero-shot multi-attribute creation, effective and diverse prompt sampling, layer-wise memory, cemantic-faithif noise diffusion and spatial attention repositioning, plus zcro-shot subjoct-driven
generation by large inpainting models (Li et al.] 2025 Fu et al. 2025a Xing el al. 2025
Yang et al. 2025a
Jung et al., 2025 Deng et al. 2025 Yun et al. 2025 Kim et al.) 2025a, Miao et al. 2025 Han et al.
2025c Shin et al. 2025 Integration with large language models supports planning, reward shaping and
stepwise verification through collaborative or customized multimodal rewards, chain-of-thoughr reinforce-
ment and preference optimization for aligmment. and controllability (Liu et al. 2025b Tang et al.] 2025 Ba
let al.] 2025
Zhou et al.] 2025c Jiang et al.) 2025 Zhang et al. 2025b; Sun et al. 2025 Wu et al. 2025c
Guo et al. 2025 Fang et al. 2025). Complementary representations and training-free frree guidance broaden
the modeling space with triplanes, rectified flows, dense-aligned guidance, Stable Flow and 3D-aware scene modeling, together with hyperbolic diffusion autoencoders, unified self-supervised pretraining and industrial
anomaly generation Bilecen et al.) 2024 Dalva et al. 2024 Wang et al. 2025] Avrahami et al. 2024 2025 Zhang et al. 2025c Li et al. 2024a Chu et al., 2025 Dai et al. 2024 Cai et al. 2025b). Our work is situated within these trends while retaining links to foundational and domain-specific efforts such as Design- Diffusion, LincArt, Focus-N-Fix, Type-R and ProciseCam and contemporancous advances in layout, control
and training- free customization (Wang et. al.] 2025g] 2024c Xing et al., 2025 Shimoda et al. 2024 Bernal- Berdun et al.) 2025 Zhang et al. 2025a Cai et al. 2025a).. For completeness, we note additional concurrent
directions spanning Instruction-oolovinng editors, multimodal integration, compositional sliders and broader
analyses (Cao et al.
2025
Chen et al. 2025b: Luo el. al. 2025a Qu et al. 2025a So al.
2025 Ren el al.
2025
Zhu et al. 2025c b
3 Proposed Method
3.1
Preliminaries
Diffusson models Dhariwal & Nichol 2021); Rombach et al. (022)) progressively denoise a latent variable to produce high-fidelity images. Classifier-Fres Guidance (CFG) Ho & Salimans| (2022) stcors generation toward conditioning inputs by interpolating botween conditional and unconditional noise predictions. Specifically, the model is trained to predict both (x;);) and eo (xxx,) c), where is the conditioning. At inference, a guidance
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CAOF
Rodaped Cross Atteaiie Oapat
Pastian Sckertia
Concaceatien
Biad Object
Rupliced Pronge
CFG-TE
SASF Lanat Ealeddiap
DGEN
Projectin
Detel
Praee!
IB Chen
Laoo Valac Pojecten
Lieer Ownn Projeciis
Replacn Ofgpet Charsd
Blcnded Otgent Chomnd
Seckid Channe
Figure 2: Flowchart of TP-Blend, integrating object replacement, blending, and style transfer within the diffusion process. In this example, the original object "Knight" replaced by "Leonardo DiCaprio", blended with "Captain Jack Sparrow", and styled with a "Charcoal Drawing" effect.
scale 8n modifies the predicted noise:
Eo (xx)) (xi) Sy (Eo(x)))) ex()).
CFG-TE extends CFG to perform precise edits existing image x The image is inverted to latent Xy via DDIM inversion Song et al. (2020), which defines a deterministic mapping between timesteps when no guidance is applied:
X Val-1
Xx -e-VI-a, cv(xi)) v1 V1- at-1 co(X)); Vor
(2)
where is the noise schedule. Once inverted, the noise prediction at each denoising step can be modified to remove or add concepts:
(x;)) = (xx) + s, (x)().),
(3)
with
colxx Codit) - (x(x)); (positive guidance), (60(x)) EØ ex(xe, Cedia), (negative guidance),
(x)
(4)
where Se is the edit guidance scale and Cedis is the editing prompt.
3.2 Twin-Prompt Attention Blend
CFG-TE enables object replacement by applying positive guidance to the new object prompt and negative guidance to the original. However, it lacks mechanisms for fine-grained object blending and style fusion which require compositional mixing and textural transformations.

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We introduce TP-Blend, extending CFG-TE with two additional prompts: blend prompt and style prompt, both assigned zero edit guidance to avoid interfering with object replacement. Cross-Atteninng Attention Object Fusion (CAOF) integrates blend object features at key spatial positions using a unified attention map and an Optimal Transport framework. Attenttonn Style Fusion (SASF) modulates texture and style by locally adjusting feature statistics using DSIN and substituting the Key/ /Va//a matrices with those derived from the style prompt. By decoupling both blending and style transfer from the editing guidance scale, TP- Blend integrates seamlessly into the denoising process, enhancing CFG-TE's capabilities with high-fidelity object and style blending (Fig. 2
CAOF
Original
Replacement
Blending
Replacul Objert
Blennded Object Channel
Threshalding
Source Embeddings
Destination Embedding:
Form Cast Matris
Sinkhura (OT)
Blead Hasbbeddings Reshaped
Output Space
Blandel Emnlediings
Figure 3: CAOF Object Blending across different. sets. Row 1: Original object "alpaca" is replaced by "puppy" and blended with "monkey". Row Original "apple" is replaced by "orange" and blended with "tomato". Row 3: Original "frog" is replaced by "chameleon" and blended with "dinosaur". Row 4: Original "truck" is replaced by "jeep" and blended with "ambulance".
Figure CAOF Flowchart: Cross- Attention Object Fusion merges the blend object's fea- tures into the replaced object by identifying key spatial positions in the attention maps and ap- plying an optimal transport franework for co- herent morphological transitions.

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3.3 Cross-Attention Object Fusion
As shown in Figure 3 and summarized in Figure| 1 CAOF seamlessly integrates a blend object's features into replaced object during the diffusion process. Leveraging textual prompts for both the replaced and blend objects, CAOF locates key spatial regions in cross-attention maps and employs an Optimal Transport (OT) framework to determine blending levels.
Identifying Significant Positions in Artestntt Maps. In multi-head cross-attention Vaswani 20177; each head h produces attention weights
A AM = softmax Q k(h)
(5)
Vd.
where Q()) E Nxdx and ((A) E Mxde are query /key matrices, N is the number of spatial positions, M is the number text tokens, and dy is the head dimension. We average over H heads and focus on the replaced and blend object tokens, freplaced and filend:
H
H
Areplaced
H
A
(h)
Ereplaced?
Ablend
(M)
Aotend"
(6)
A=1
A=1
To identify meaningful spatial positions, we introduce two percentile thresholds, Tsource and Tdeat Specifically, any position i in ablend whose attention weight exccods the ppereentle is included in the source set S, and any position in areplarod exceeding the Taee-percentlee is placed in the destination set D.
Clarification. The thresholds act on different head-averaged maps and therefore induce the two index sets independently. We make this explicit with
S S= [N] ablend [i] 2 Teourea (rrorca((bbeed) (ablend) }. D = {i E [N] areplaced [i] 2 gramal (areplaced) }.
(7)
where (-) denotes the r-percentile. The sets S and 'D are not a partition of the image tokens. A position can be in neither set when it is below both cutoffs, in exactly one set when it responds strongly to only one prompt, or in both sets when it responds strongly to both prompts. In the fusion step (Sec. 3.3 only destination positions d 'D are updated and they receive transported features from sources OT plan T (Eq. II and Eq. Dj. Tokens i D pass through unchanged. a spatial location lies in SnD, its destination slot taken from the replaced stream can still import features from its source slot taken from the blend stream because these embeddings originate from different prompt branches which avoids ambiguity. When an object phrase spans multiple text tokens, we pool their columns before thresholding and use the mean by default, while max pooling yields similar behavior in our experiments. Although we often tie the thresholds in a joint setting with Tsource = Tdest to simplify usage (see Fig. [2), they are defined separately and can be chosen differently to trade precision and coverage. The percentile formulation makes the selection scale-frec and robust across layers and prompts because it depends on rank within each map rather than absolute magnitude.
under the
Blending Feature Embeddings in Reshaped Cross-Attention Outputs. To effectively integrate features from the blend object into the replaced object, we begin by concatenating the per-head attention outputs along the feature dimension:
0 (f (WV E RND.
where A E N are attention weight matrices, ((N) E Mxde are the corresponding value matrices, D = H dd is the total feature dimensionality, N is the number of query positions, and M is the number of key tokens. By consolidating multi-head outputs into a single representation, we preserve all information necessary for seamless fusion, avoiding the loss that would occur from hea-head embeddings.
We then blend the feature vectors of the replaced object with those of the blend object under a transport plan T. Specifically, if d; D and 8y E S denote destination and source positions respectively, with fa, f.y TR.

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being their respective feature vectors from O, the updated feature vector at position d, becomes
T3 E Tis far.
f/ (1 wo) fa. Wo Σ 8,6S
(9)
where wo E [0, 1] controls the relative influence of the blend features, and Tig is obtained by solving the OT problem. By treating the multi-head outputs as whole at the full dimensionality (e.g., D = 640), we not only preserve complex content and style cues but also obtain a more manageable OT cost matrix (c.g., 4096 x 4096), avoiding the significantly larger matrices (e.g., 40960 40960) that would result. from per-head processing.
Formulating the Optimal Transport Problem. Let 8 and D denote the sets of source (blend object) and destination (replaced object) positions. The cost of transporting mass from source position j S to destination position E 'D is given by
Ci = Meature Dieature(i. + Aspatial D (i,
(10)
where (t., is the cosine distance between feature vectors f, and fy and D Depatial(i.)) is the Euclidean distance between their spatial coordinates.
We solve the entropic OT problem:
min T20
ΣΣ
Cip - (T),),
(11)
iED JES
s.t.
Σ Tip 11 ViED,
(12)
JES
1
Σ IED
Th
VjES,
(13)
S
where H(T) - E,, Tiy log is the entropy term, and 7 0 is the regularization parameter. Entropy regularization promotes smoother transport mass across source-destination pairs.
Solving the Optimal Transport Problem with the Sinkhorn Algorithm. The entropic regulariza- tion allows the problem to be efficiently solved using the Sinkhorn algorithm Cuturi (2013); Peyré et al.
(2019: Genevay et al. (2016). We form the Gibbs kernel K = exp(-C/7) and iteratively update scaling
vectors UIPI and RIST.
1 1pd
[ST 1
(++1)
v(++)
(14)
K k
K (++1))
until convergence. The transport plan becomes
T diag(u) K diag(v).
(15)
Finally, use T to blend each destination feature with weighted contributions from the source. Reinte- grating these blended features into the cross-attention outputs yields naturally fused object that inherits characteristics of the blend object at selected positions, with minimal overhead or artifacts.
3.4 Self-Attention Style Fusion
As illustrated in Figure 5 and outlined in Figure 6 SASF integrates style and texture into the replaced
object through self-atlentionn Compared to previous hots Chung et al. (2024); Xing et al. (2024): Wang
let al. (2024a); Xu et al. (202)); Li (2024] Lötzsch et al. (2022) Hertz et al. (002)); Zhang et al. (2023
Wang et al. (2023). SASF offers four advantages: (1) introduces DSIN capture HF textural details
lightweight yet effective manner; (2) it relies on simple textual prompts rather than style images; (3) it
fuses style and object features simultaneously during denoising, preserving both content fidelity and style
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coherence; and (4) By translating historical idioms such Ukiyo-e, Renaissance, and Baroque into their own material vocabularies, SASF can recode fabric weave, ornamentation, and weaponry; chain mail shifts to brocaded velvet, a plain sword strap becomes an obi sash, yet the figure's stance and the surrounding cityscape stay unchanged, as demonstrated in Figure
Style 1
Style 2
SASF
Replaced Objject Channel Object Channel
Embedlinggs
Smoothing
Injector
Style 3
Style 4
Linear Kay Projection
Linear Value Projection
Linear Query Projection
K
Swap
Swap
Figure 6: SASF Flowchart: Self-Attenticn Style Fu- sion incorporates style prompts by injecting high- frequency details via DSIN and substituting textual Key/Value Value matrices, ensuring fine ane-grained style mod- ulation during the diffusion process.
Figure 5: Object blending enhanced with various artistic styles. Style 1: Pixel Art; Style 2: Choco- late; Style 3: ing.
Charcoal Drawing; Style 4: Oil Paint-
Ukiyo-e
Renaissance
Baroque
Low-Poly
Figure Stylistic renderings reshape fabric texture and accessories while pose and setting remain unchanged. The original knight replaced by Albert Einstein, blended with a nobleman concept, and then rendered in four distinct styles. Each style reinterprets the garments in a unique way: Ukiyo-e replaces the surcoat with patterned kimono, complete with an obi sash and lacquered katana; Renaissance introduces brocaded velvet, gilt medallions, and scholar's cap; Baroque presents deep hued silk enriched with heavy gold embroidery and filigreed weaponry; Low-Poly abstracts every surface into planar facets and simplifies folds and metallic highlights.
Detail-Sensitive Instance Normalization. Let F Freplaced, Pstyle NxD be the latent embeddings (i.e., token-wise feature maps) of the replaced and style objects, respectively. We first perform AdaIN step

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the replaced features:
F
reelaced - Mrep
F replaced
Jatyle
+
Patyle:
(16)
rrep
where (Jrep, Trep) and (Mstyles ssyle)) are the channel-win means and standard deviations of the replaced and style embeddings. This aligns global statistics (mean and variance) to match the target style, but by itself may overlook subtle,
stylistic cues.
Next, DSIN applics a small 1D Gaussian smoothing filter along the token dimension to decompose both F Preplaced and F stylle into owwfrequency (LF) and high-frequency (HF) components:
FLF = F FHF F - FFI.
(17)
where K is 1D Gaussian kernel of size k 2m +1 I and width o. Intuitively, FLF captures coarse variations (slower changes across tokens), while FHF isolates the finer details. DSIN then injects a fraction of the style HF difference directly into the AdaLN output:
F" replaced
F repllaed + a (F style
FHF replaced)
(18)
When DSIN applics a 1D Ganssian kernel K along the token dimension, it acts as a low-pass filter in the frequency domain: larger a broadens the kernel's passband, yielding a narrower high-frequency (HF) residual FHF and thus a subtler style injection. Conversely, smaller captures more mid- and high-froquency components, accentuating textural details (e-g., brushstrokes) in the final output. The injection fraction a then scales the amplitude of these style-specific HF cues. In effect, o and a together provide powerful mechanism for tuning the granularity and prominence of style features.
Unlike prior approaches such as Huang& & Belongie (2017) 7 or Chung et al. 2024 that apply AdaIN globally or only at the initial noise level for DDIM inversion, our DSIN is applied at every self- atteentinnnoon layer throughout the denoising process. This repeated application ensures the progressive and layer-wise infusion of fine- grained stylistic features, enabling multi-scale texture adaptation without disrupting the overall structure.
Key/ Value Substitution. Following the DSIN framework, we first construct the Query, Key, and Value matrices for self-attention. We then substitute the Key and Value channels the target (replaced) object with those of the style source:
Ktar +- Ksty:
V tar
sty-
(19)
Since the self-attention output is computed by weighting the Value vectors using Query-Key dot products, replacing the Key and Value matrices of the replaced region with those from the style prompt allows style features to dominate the attention updates. This substitution imposes the texture and local patterns of the style onto the replaced object, leading to strong stylistic transformations.
While Chung et al. 2024) apply this substitution using Key/ /Vaue representations extracted from an image- based style encoder, our approach instead derives these from textual prompts. Specifically, we construct. the Key/Valle /Value matrices from the text prompts of both the replaced object and the style source, enabling a text-driven style transfer mechanism without requiring image- features.
Importantly, although this substitution offsets the effect of DSIN modulation in the Key/V /alue branches for the replaced object (since it is overwritten by style-derived fcatures), DSIN-modified features remain intact in the Query branch. This asymmetry allows DSIN to still influence the attention outputs via its role in computing attention scores. Consequently, high-frequency stylistic cues injected through DSIN continue to impact the hidden embeddings passod to the next layer. This achieves dual effect: the Key /Value substitution enforces stylistic consistency, while DShNeenhanced Queries preserve the structural fidelity of the replaced object, allowing nuanced and locally-aware style transfer.
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Table 1: Performance on the Object Replacement + Object Blending task.
Method
BOM1
CLIP a1
CLIP
1-LPIPSot
IP2P Brooks al. (2023
(CVPR 2023)
0.1075
0.1819
0.2708
0.5887
StyleAtigned Hertz et al
2024
(CVPR 2024)
0.2371
0.2120
0.2866
0.5814
TurboEdit Deutch es al.
2022
0.3199
0.1981
0,2781
0.6125
FLUX.1 Kontext Batiel
20225
0.3401
0.2013
0,2898
0,6007
LEDITS + Brack cl al
2024
[CVPR 2024)
0.3913
0.2078
0.2834
0.6145
SeedEdit Shi
0.5486
0.2096
0.2966
0.6381
Step Liu et all (2025d
0.7150
0.2120
0.2913
0.7024
Blended diffuston Avraltam
(CVPR 2022)
0.7211
0.2026
0.2910
0.7568
Nano Banana
0.7324
0.2159
0.2866
0.7303
CAOF
0.8031
0.2011
0.2937
0.8292
4 Experiments
4.1 Implementation Details
Model Architecture. All experiments employ SD-XL Podell et al. (2023) the dilfusion backbone. The source image is first inverted to a latent xr via DDIM inversion, guaranteeing exact reconstruction before oditing. During the forward denoising pass we apply, at every timestep: (i) TIE-CFG for object replacement (positive guidance on the target prompt, negative on the original); (ii) CAOF to transport blend-object features into attention positions selected by the joint percentile thresholds Tsouree = Tdest E 0.7); and (ili) SASF inject style via DSIN and key-value substitution. The Sinkhorn regulariser is fixed 7 = 0.1,
with cost weights Meature 0.7 and Aspatial 0.3 (Eq. 10
Baseline Methods. To isolate the contribution of TP-Blend, we compare against six state-of-the-an text-
driven editors and re-tune their prompts for each task so that every method receives semantically equivalent
conditioning. The basclines are Stcp1X-Edit Liu et al. (002dd). ScedEdit (hi ct al. (0024), LEDITS ++ Brack
et al. (0224) (CVPR 2024), StyleAligued Hertz el al. 2002)) (CVPR 2024), Turbo Deutch et al. (0024)
IP2P Brooks al. (2023) (CVPR 2023), Blended diffusion Avrahami et al. (2022) (CVPR 2022), FLUX.1
Kontext Batifol et al. 2025 and Nano Banana.
SeedEdit and Step1X-Edi are inversion-free decoders optimised for speed, LEDITS+ and StyleAligned specialise in resolution-aware refinement, while TurboEdit and IP2P are two-stage pipelines that first predict a coarse edit mask. Blended diffusion performs training-free, CLIP-guided local edits via progressive noise- space blending for background preservation. FLUX.1 Kontext a unified flow-matching in-context editor with fast multi-turn consistency. Nano Banana is a lightweight. interactive editor focused on fast. object and style edits with identity preservation. Evaluating against this diverse slate highlights TP-Blend's ability to blend rather than merely replace stylise.
Evaluation Protocol. For our evaluation, assembled diverse set of high-resolut ion, publicly available images from Unsplast following the same practice as prior work such as SLIDE Jampani et al. (2021) and Text-driven Image Editing via Learnable Regions Lin et al. (2024). The test dataset consists of 4,000 samples, created by pairing 40 base images with 20 distinct replace- blend object combinations and distinct blend styles.
Evaluation Metrics. We assess alignment between generated image Ig and four textual prompts-original object Po, replaced object PR, blend object PR, and style Ps using CLIP similarity:
CLIP, = cos(fvia (Tg), frext (P:)), I E (0, R, B,S).
Perceptual fidelity is quantified as 1 LPIPSo. To ensure comparability, each score s is min-man max normalized:
httpp://uneplash.com
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Table 2: Performance on the full Object Replacement + Object & Style Blending task.
Method
BOSM1
CLIP
CLIP
CLIPst
IP2P Brooks et al. 2023
(CVPR 2023]
0.1277
0.1680
0.2776
0.1691
LEDITS+E Brack et aL.
2024
(CVPR 2024)
0.2693
0.2039
0.2876
0.2236
TurboEdit Deutch el B.
2022
0.3829
0.1954
0.2820
0.2090
FLUX.1 Kontext Balilo
2225
0.3955
0.2069
0.29442
0.2118
StyleAligned Hertz C5 al.
2024
(CVPR 2024)
0.4125
0.1888
0.2915
0.1973
SendEdir [Shi
21724
0,4650
0.1963
0.2915
0.2017
StepIX-Edit Liu et al 2202d
0.4652
0.2145
0.2920
0.2170
Blended diflusion Avrahami
2022
(CVPR 2022)
0.4903
0.2096
0,2900
0.1805
Nano Banana
0.5849
0.2346
0.2856
0.2150
CAOF
0.6639
0.2014
0.2937
0.1976
CSOF+
0.8656
0.2178
0.3022
0.2161
5min 0.1.
e (1-
Smax
8min
The normalized scores are CLIP R. CLIP CLIPs, and LPÎPS
BOM (Blending Object Metric) measures replacement and blending accuracy:
+ WB + WL
BOM
WR CLIPA
WF + 1-LPiPSo CLIPs
BOSM (Blending Object Style Metric) further incorporates style fidelity:
Wn + WB + WS
BOSM =
+ L-LPIPSo
CLIP
CLIP
CLIPs
Both metrics are harmonic means where low individual scores significantly lower the final value, highlighting odits that successfully balance content fidelity and stylistic integration.
4.2 Comparisons with SOTA models
Quantitative Evaluation of Object Replacement and Blending. Table 1 presents BOM scores for 800 replacement-blend pairs. CAOF achieves the highest value (0.8388), substantially surpassing the next best method (0.7352). Its advantage does not stem from single component: although StepLX-Edit yields the best CLIPx and SeedEdit tops CLIP B, those gains are offset by weaker performance on the complementary cue and by larger perceptual drift, which the harmonic mean penalises. CAOF instead secures peak values on both alignment terms while also delivering the strongest imago-fidelity score (1 (1-LPIPS==008292) This balance arises from the cost-aware transport in CAOF, which places blend features only at semantically consistent locations, preserving global structure and avoiding the artefacts or concept omission observed in the baselines. The results confirm that effective object blending requires simultancous optimisation of replacement accuracy, blend consistency, and photographic integrity, a tradc-off that CAOF best satisfics among the methods we evaluated.
Quantitative Evaluation of Object Replacement, Blending, and Style Integration. Table 2 lists all methods in ascending BOSM order. The lower half of the table shows that aggressive stylisation simplistic blending hurts semantic alignment, producing BOSM below 0.40. Middlo-ranking approaches recover object fidelity yet still dilute style cues, so their overall balance remains limited. Pure CAOP moves into the upper tier by preserving both objects without increasing perceptual drift, yielding BOSM 0.7102. Adding SASF raises the score 0.9244, the largest margin in the study. This improvement is not obtained by style similarity alone: CLIP & and CLIP & also climb, indicating that the high- freuurryyy details injected by DSIN and the text-driven Key-Valu substitution sharpen local structure and make both
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Original
CAOF (replacement only)
+
TurboEdit
SeedEdit
LEDITS++
Step1X-Edit
IP2P
StyleAligned
FLUX.1 Kontext
Blended Diffusion
Figure 8: Method comparison for the lask Knight > Leonardo DiCaprio, blended with Balman and rendered in a water-color style.
identities more recognisable. The joint optimisation of content and texture therefore proves essential when multiple conceptual constraints must be satisfied simultaneously.
Visual Assessment. Figure (fantasy portrait) and Figure 9 (celebrity street scene) illustrate the quanti- fative trend reported in Table CAOF SASFF achieves balanced fusion where both the replaced identity,
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Original
CAOF (replacement only)
+
TurboEdit
SeedEdit
LEDITS+
Step1X-Edit
IP2P
Aigled
FLUX.1 Kontext
Blended Diffusion
Figure 9: Method comparison for the task Tom Hanks + Tayllor Swiff, blended with jeun shorts+wlee shirt and rendered in an oil allpainting style.
the blended identity, and the target style are distinctly visible while maintaining the original scene geometry and background texture. In contrast, the baselines exhibit method-speciiic. failure modes:
Background degradation. In Figure StyleAligned Hertz al. (2024) replaces the Times Square background
with different scene, and LEDITS+ Brack (2024) and FLUX.1 Kontext Batifol (2025) remove
the background almost entirely. In Figure Style Aligned ligned Hertz al. 2024). Step1X-Fdit Liu et al. 2025d
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Relatiorship
0:2
Figure 10: Variation of CLIP scores for original (Po), replaced (Pr), and blend (P)) object prompts as the blending coefficinn wp changes. The curves illustrate CAOF's effectiveness modulating blending strength, achieving the desired integration of the blend object while replacing the original object.
W/0 = 0.2
100
0.5
W/ = 0.7
WD 0.9
Figure 11: Object blending progression with varying blending coefficients wi- Row 1: "monkey" blended into replacod "puppy" (originally "alpaca"). Row 2: "fish" blended into replaced "dinosaur" (origi- nally "chameleon"). Row 3: "ambulance" blended into replaced "jeep" (originally "truck").. Row 4: "Thanos" blended into replaced "knight" (originally "robot")). Higher we values correspond increased blending intensity and finer textural details.
and FLUX.1 Kontext Batifol et al. (2025) substitute the original blue backdrop, while LEDITS+ Brack
et al. 2024) partially destroys the bench. All baselines also drop secondary background details (e.g., leather
shoes, socks, and the suit jacket) that are present in the source image.
Loss of replaced-objer object identity after blending. Figure & TurboEdit Deutch al. (2024). ScodEdit Shi al. (2024), SteplX-Edit Liu et al. (2025d), IP2P BBrooks al. (2023). Blended dilfusson Avrahami al. (2022) and FLUX.1 Kontext Batifol et al. (2025) reduce remove recognizable features of Leonardo DiCaprio. In Figure [ the same methods fail to preserve identifiable traits of Taylor Swift under the jcan-shorts + white-shirt blend.
Severe distortions or unintended objects. In Figure S SeedEdit introduces an extra arm, and IP2P generates duplicate faces. In Figure g SoedEdit adds an extrancous box, and IP2P produces heavily distorted image with three faces. Across both figures, outputs from Blended diffusion Avrahami al. (022) are often coarse with blurred regions, obscuring small objects.
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Table 3: OT ablation: CAOF vs. NoneOT.
Table 4: DSIN texture metrics versus a and a.
Method
BOM
C
CLIPs 1 LPIPSot
LVT
GC1
HFS1
NoneOT 0.14299
0.1984 0.2014
0.2891 0.2937
0.8301 0.8292
0.5
2.5
271.8709
79.9853
5.27x10'
CAOF 0.2500
253.3316
79.7168
5.06x10º
0.2
2.5
266.9800
80.0326
5.21x109
0.2
0.5
241.8779
76.5979
x.97x10"
0.0
244.2984
68.8580
4.90x10"
Insufficintt cross-identity blending. In Figure Nano Banana leaves the head largely unchanged as Leonardo DiCaprio, with little to no visible Batman attributes, indicating weak cross-identity fussio..
These issues, along with excessive denoising that washes out high-frequency details or spatial artifacts like duplicated limbs, result in lower BOSM scores for competing methods. This comparison highlights the perceptual advantage of CAOF-SASE, which maintains a coherent and natural fusion without introducing such distortions.
4.3 Ablation Study
To examine how CAOF controls the fusion strength, we vary the blending coelficient wo [0.1, 0.9 (Eq. 9 and record the CLIP similarities for the original (0), replaced (R), and blend (B) prompts. Figure 10 illustrates the variation of CLIP scores with wo. The curves clearly demonstrate CAOF's effectiveness in adjusting blending strength. As wy increases beyond 0.6, the influence of the blend object prompt Po significantly rises, while the influence of the replaced object prompt P. remains high until wn exceeds 0.8, after which it decreases rapidly. Concurrently, the influence of the original object prompt P, remains consistently low throughout, aligning with our goal to replace the original object with the replaced object while blending in the blend object to the desirod extent. Qualitative frames Fig. corroborat the numerical trend, showing a smooth morph from "mostly replacement" to "mostly blend" without geometric break-down.
Ablation Study on CAOF.
SASF Ablation. SASF relies solely on textual prompts for style specification, prompting us to measure style blending performance through CLIPs, the normalized similarity between the generated image Ig and the style object prompt P.. As shown in Table 2] CAOF+SASF attains substantially higher CLIPs of 0.2161 than CAOF's 0.1976, indicating that SASF effectively injects the desired style features.
Ablation on the joint percentile thresholds Tsource, Tdest- CAOF builds the source set S and des- tination set D by thresholding head-averaged cross-attention responses of the blend and replaced prompts. Positions above the Tsouree percentile the blend map enter and those above the Tdest percentile in the replaced map enter D. Sweeping the joint threshold Tsource = Fdest {0, 10, .o........................ 90, 99} exposes a precision coverage trade-off that directly governs how CAOF redistributes features. Figure 12 plots CLIP cosine scores for the original prompt Po, the replaced prompt P,, and the blend prompt P.. Three observations follow from the measured curves.
First, Ps peaks at a mid to high threshold: the best blend alignment occurs at 60% where P. = 0.2530 and remains competitive at 70% with Pg = 0.2371. Around this regime spurious low-confidence tokens are removed yet all salient parts of the object remain in S and D. The feature and spatial terms then rank candidate correspondences cleanly and the transport plan concentrates mass on semantically consistent matches, the fused vectors carry the right identity and geometry.
Second, very high thresholds shrink coverage and favor replacement: when Tsuuree, Tdest are pushed to 80% and beyond, the sets collapse to handful of extreme tokens. Coverage drops and CAOF touches only tiny regions, so the CFG-TE replacement signal dominates the demoising trajectory. Empirically P, rises from 0.1719 at 60% to 0.2361 at 99%, while P, falls sharply to 0. 0.1592 0.13...
Third, low thresholds dilute semantic precision: 0%-50% the sets admit many background or off-object positions. The plan must spread mass across numerous weak matches and the averaged multi-head vectors
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CLIP scores vs. joint percentile thresholds
0.24
0.22
(cosine)
0.20
0.18
0.16
0.14
0.12
10
20
30
40
50
60
70
80
90
99
Tapurce Tdant (%6
Figure 12: Joint percentile threshold ablation. CLIP cosine scores for Po. PR, and Pn as the joint threshold Teource Tdest varies. The blend score PB peaks at 60%. Very high thresholds shrink and D excessively and favor replacement (Pn) over blending, while low thresholds admit non-relevant vectors that dilute the fused signal. Moderate thresholds balance precision and coverage.
mix non-relevant content, which roduces blend fidelity. This explains the drop from P3 0.2413 20% to P. 0.2137 at 50%.
Throughout the sweep the original content remains suppressed, with Po staying low in the range 0.1221- 0.1585. Taken together these trends justify the default Tsource = E (0.6, 0.7). This window removes noise while preserving spatial coverage, maximizes Pd near its peak, keeps P, strong enough for reliable replacement and maintains low P.- The ablation therefore confirms that mid to high joint percentiles are critical for stable and semantically faithful blending under CAOF.
OT Ablation. To disentangle the contribution the Sinkhorn solver, we replace with a naïve NONEOT variant that line-up source and destination tokens by index and applies fixed a-blend, thereby ignoring both feature similarity and spatial proximity. As sunmarised in Table 3 removing Optimal Transport slashes BOM from 0.2500 to 0.1429. The loss is driven almost entirely by lower alignment scores (CLIPR and CLIPn), while the perceptual term 1- LPIPSo remains virtually unchanged. In other words, uni- form blend preserves low-level appearance but often allocates the wrong blend features to the wrong spatial regions, degrading semantic coherence. The cost-aware Sinkhorn plan redistributes those features toward ge- ometrically and visually compatible destinations, yiclding a markedly more faithful fusion without sacrificing overall image fidelity.
DSIN Ablation. Laplacian Variance (LV) Pertuz et al. (013)). GLCM Contrast (GC) Haralick et al. 1973), and FFT High-Frequency Sum (HFS) Gonzalez & Woods (2008) show that textural richness depends the joint choice of the residual-mixing weight a and the Gaussian width a, rather than on a alone. Raising strengthens the amplitude of the injected high-frequency residual, but this extra energy is useful only if a is large enough to confine the smoothing kernel to genuinely low frequencies; with 0.5 the wider kernel = 2.5 yields the highest LV, GC, and HFS, whereas the same a combined with the narrow kernel = 0.5 loses mid-range structure and drops all three scores. Conversely, keeping a moderate at 0.2 still improves over pure AdaIN (a = 0), yet the gain is larger when o = 2.5 than when o = 0.5. These trends confirm that a governs how much fine detail is transferred while sets the frequency band that will be regarded as "detail"; optimal texture emerges when both parameters are tuned together, explaining the peak at 0.5, 2.5 in Table 4 and the visibly crisper result in Figure 13
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Figure 13: Pixel-art edit of "robot knight" blended with "Thanos". Left:
0, right: 0.5, 2.5.
5 Conclusion
We introduced TP-Blend, training-free free framework that performs object replacement, object blending, and style fusion within a single diffusion denoising run. By separating the content and style prompts, TP-Blend grants independent control over semantic structure and appearance. Cross-Attention Object Fusion employs an optimal-lransport plan to place blend-object features in spatially and semantically consistent regions, while Self-Attention Style Fusion injects fegh-fre texture through detail-sensitive instance normal- isation and text-driven key-value substitution. Across extensive benchmarks, TP-Blend delivers sharper textures, stronger alignment with target objects and styles, and higher perceptual fidelity than recent ed- itors, all without extra training model fine-tuning. These results establish TP-Blend as a simple yet effective tool for precise, text-guided image editing within diffusion models.
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http://aaa.v..rrraa////000.....0 01028
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Reviews:
[{"review_id": "NzunyRU0RB", "reviewer": "Reviewer_5dFB", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths:**\n\n1) The method appears to work as intended, producing good results.\n\n2) The paper is clearly written and easy to understand.\n\n**Weakness:**\n\n1) The motivation for the paper doesn't seem very strong to me. Although I understand that other methods might not be able to handle the mixing of the style and object in a single prompt, I do think that with some prompt engineering, we can solve this problem with the existing methods. I would like to see the prompts that are being used to generate images using LEdit++.\n\n2) In Fig 3. we see in row 2 that blending two objects is not always a good idea, are you somehow trying to check the compatibility of the objects that are being blended for a more hands-free user experience?\n\n3) Blended Latent Diffusion seems like an appropriate benchmark method to test against, as it has a similar idea where objects are blended in the latent space. However, the addition of style was not explicitly added. Again, using the example of Figure 3, the object identification prompt will be \"Apple\" and the edit prompt should be \"Orange like tomato in the style of \\<Art\\>\".\n\n4) How is  $\\tau_{source}$ and $\\tau_{dest}$ determinded. If it is empirically determined, then an ablation for these two parameters should be done.\n\n5) On page 11,\".. a trade-off that CAOF is uniquely able\nto satisfy ...\" ` is written. When you write uniquely here, does it mean that your method CAOF is mathematically the unique solution to the identified problem? If yes then a valid proof should be presented otherwise _uniquely_ should not be used.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This is a method paper which aims to address two problems. (1) Replacing an object (2) Replacing an object while blending another prompt with the same object. The background should be minimally changed while doing this object replacement.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Refer to Weakness", "broader_impact_concerns": "No specific section on broader impact. But the impact is same as other image editing methods.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "XjWfnUczCI", "reviewer": "Reviewer_Xapm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "I believe overall the method section is discussed in a clear way. The motivation is also clear.\n\nThe experimental section reads comprehensive, however there exist certain aspects that can be improved. Please see below.\n\n---\n\nI personally find that in Sec.3.3 the hyper-parameters, including $\\tau_\\text{source}, \\tau_\\text{dest}, w_0$ are a bit arbitrary and could perhaps complicate the method. While I fully agree the necessities of these values, can the authors perhaps share a bit of insight as to how these values shall be determined, are the values general or per-sample, and are they the outcome of empirical findings or are there any general rules of thumb behind?\n\nRegarding the SASF module in Sec.3.4, the current version makes it a little bit difficult to distinguish exactly which part is the novel contribution of the authors and which part is inspired by previous work -- because the authors clearly stated that this module rely on DSIN and AdaIN, both are existing techniques in the image generation/editing domain. Therefore, I find this section could use a bit more work.\n\nIn addition, the author could consider employing human evaluation to strengthen the experimental section, which is a common strategy for assessing generative methods.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This work tackles the specific problem of image editing where after an object replacement, the users seek better object blending and subsequently visual style changes. In summary I believe the authors are studying a particular compounding problem. The aim is therefore to enhance both the blending and coherent style changing at the same time.\n\nThe authors mainly propose two modules, a cross-attention object fusion (CAOF) module that employes optimal transport to identify relevant regions that correspond to the editing/background, and then performs the blending in token space.\nMeanwhile, the author proposes the self-attention style fusion (SASF) module that substitutes the key/value matrix to perform a style change.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to the weakness discussed above. In addition, below are some minor comments related to paper writing:\n\nIn Sec.3.3 where the authors introduce the two thresholds, $\\tau_\\text{source}, \\tau_\\text{dest}$, it is not clear if the authors indicate that each token either belongs to the source or the blend -- or perhaps a token can be neither? \n\n\nResearch has shown that CFG is not entirely losslessly reconstructing the visual content from noise, as the classifier-free term introduces drifts towards the denoising process (Mokady et al. (2023), as the authors have already cited in previous paragraphs). This might slightly impact the discussion in Sec.3.1 where the authors state that CFG deterministically recovers $x_T$ from $x_0$ without reconstruction error.", "broader_impact_concerns": "I have not identified any particular broader impact concerns for this work, except for the usual concerns related to generative networks -- but those are very common and ordinary and thus not specific to this work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "HURANf8EbF", "reviewer": "Reviewer_ts8f", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**:  The paper delivers on its claims:\n1. It introduces two specifically designed blocks. The method is presented with reasonable clarity, and ablation studies demonstrate the beneficial effects of these two components.\n2. It claims improved object representation and stylization, which is supported by the presented results.\n3. Objective evaluations show improvements over strong prior methods.\n\n**Weaknesses**:\n1. The main issue I see with the paper concerns its impact. The field of stable diffusion has reached a level of maturity where many existing solutions already produce high-quality outputs. We are no longer at a stage where models routinely generate visibly flawed images. Within this context, the proposed method provides reasonable improvements, but it does not stand out significantly from prior work. For example, Figures 8 and 9 do not present results that are strikingly different from previous approaches.\n2. The technical innovation, while present, is limited. There is no particularly memorable or broadly applicable concept that can be easily transferred to other problems.\n3. The paper may not be a strong fit for TMLR. This concern is supported by the bibliography: the majority of cited works come from the computer vision community rather than the machine learning community. While a few NeurIPS papers are cited, none of the works used for comparison are from NeurIPS, ICML, or TMLR. The paper might be better suited for a computer vision–focused journal.\n4. Several aspects of the evaluation are not detailed enough:\n- Dataset description: More information is needed about the dataset. The paper should more convincingly argue that the dataset is broad and covers a wide range of relevant aspects. What types of objects and styles are included? Is there a systematic analysis based on object type or style? These details are important, especially since the paper makes claims regarding these dimensions.\n- Evaluation metrics: The paper should provide more justification for the metrics used. Why are these metrics relevant? In particular, more explanation is needed for BOM and BOSM—have they been used in prior work, and are they accepted as meaningful indicators of performance?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper addresses the problem of soft diffusion, which involves combining various images and/or text captions to generate synthetic images intended to appear realistic. Compared to classical or standard methods, it claims to offer better adaptation for introducing specific objects and target styles. This is achieved through two mechanisms designed to improve the quality of the output images.\n\nThe first mechanism, Cross-Attention Object Fusion, averages head-wise attention to locate spatial tokens. The second, Self-Attention Style Fusion (SASF), injects style information at every self-attention layer.\n\nFor evaluation, the paper compares the proposed method objectively with several strong prior works and also provides results for subjective comparison. The dataset used for evaluation was created specifically for this study.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "Overall, I believe there is limited scope for improving this paper to make it suitable for TMLR. The primary concerns are its lack of alignment with TMLR’s focus and the limited impact of the work, especially given the current state of stable diffusion research, which has matured significantly.\n\nThat said, some specific aspects could still be addressed to strengthen the submission:\n- Include more visual examples. If the main paper has space limitations, additional comparisons could be provided in the supplementary material or through an anonymous repository. These examples should aim to highlight not only how the proposed method is different, but also how it is clearly better.\n- Report metrics for all visual examples. This would help in supporting claims with quantitative evidence and improve the clarity of the evaluation.\n- Address the evaluation concerns raised earlier (see point 4). In particular, more detailed justification and analysis regarding the dataset and evaluation metrics are essential.", "broader_impact_concerns": "The paper proposes a solution synthesize images and this can be used on all kind of ways. The method does not do anything that directly is harmful. The problem with the paper is that it does not discuss ethical implication in any way. A disclaimer paragraph would suffice.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)