Cross-ControlNet: Training-Free Fusion of Multiple Conditions for Text-to-Image Generation
Authors: Xiang Liu
Junjun Jiang
Wei Han
Kui Jiang
Xianming Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Training free
Multi-Condition
Controllable Image Synthesis
Primary area: generative models
TL;DR: Cross-ControlNet is a training-free framework that fuses multiple spatial conditions for text-to-image generation via three novel modules: PixFusion, ChannelFusion, and KV-Injection.

Abstract: Text-to-image diffusion models achieve impressive performance, but reconciling multiple spatial conditions usually requires costly retraining or labor intensive weight tuning.
We introduce Cross-ControlNet, a training-free framework for text-to-image generation with multiple conditions.
It exploits two observations: intermediate features from different ControlNet branches are spatially aligned, and their condition strength can be measured by spatial and channel level variance.
Cross-ControlNet contains three modules: PixFusion, which fuses features pixelwise under the guidance of standard deviation maps smoothed by a Gaussian to suppress early-stage noise; ChannelFusion, which applies per channel hybrid fusion via a consistency ratio gate, reducing threshold degradation in high dimensions; and KV-Injection, which injects foreground- and background-specific key/value pairs under text-derived attention masks to disentangle conflicting cues and enforce each condition faithfully.
Extensive experiments demonstrate that Cross-ControlNet consistently improves controllable generation under both conflicting and complementary conditions, and further generalizes to the DiT-based FLUX model without additional training.

Published as a conference paper at ICLR 2026
CROSS-CONTROLNETT TRAINING-FREE FUSION OF MULTIPLE CONDITIONS FOR TEXT-TO-IMACE GENER- ATION
Xiang Liu, Junjun Jiang', Wei Han, Kui Jiang, Xianming Liu Harbin Institute of Technology
ABSTRACT
Text-to-image diffusion models achieve impressive performance, but reconciling multiple spatial conditions usually requires costly retraining or labor intensive weight tuning. We introduce Cross-ControlNe text-to-image generation with multiple conditions. It exploits two observations: intermediate features from different ControlNet branches are spatially aligned, and their condition strength can be measured by spatial and channel level vari- ance.
training-frre framework for
contains three modules: PixFusion, which fuses features pixelwise under the guidance of standard deviation maps smoothed by a Gaus- sian suppress early-stage noise; ChannelFusions which applies per channel hybrid fusion via consistency ratio gate, reducing threshold degradation in high dimensions; and K I-jjnccti,on, which injects foreground- and background-specift key/value pairs under text-deriver attention masks to disentangle confticting cues and enforce each condition faithfully. Extensive experiments demonstrate that Cross-Controll consistently improves controllable generation under both con- flicting and complementary conditions, and further generalizes to the DiT-based FLUX model without additional training.
INTRODUCTION
In recent years, text-to-image (T2I) diffusion models (Podell et al., 2023; Balaji et al., 2022; Saharia et al., 2022; Chen et al., 2024b; Xue et al., 2023) have demonstrated unprecedented proficiency in image generation. These models can produce a wide variety of high-quality images from concise text prompts, effectively bridging the gap between human language and visual content. This achievement has not only captured the imagination of researchers and practitioners, but has also found broad applications in digital art, graphic design, and illustration generation (Wang et al., 2025a; Chen et al., 2023; Wang et al., 2025c; Chen et al., 2024a). However, the inherent ambiguity and abstractness of language make it extremely challenging for T2I models achieve fine- ane-grained control over the semantic layout of images (Li et al., 2023b). To address this issuc, ControlNet (Zhang et al., 2023) (detailed in Appendix A.1 and T21-Adapter (Mou al., 2024) incorporate spatial conditions (e.g.. edges) into pretrained T2I models by introducing additional control networks, making controllable generation under single modality no longer out of reach. Both approaches have achieved high- fidelity image synthesis while maintaining strong coherence with the structural conditions. Yet, when extended to multiple conditions, the inherent imbalance among different modalities means that each generation requires careful manual tuning of weighting coefficients- significant burden (Nair et al., 2024). Consequently, synthesizing images that satisfy multiple conditions remains challenging (Wang et al., 2024).
straightforward strategy is to train on large-scale paired data spanning diverse conditional modalities in order to learn how to balance multiple controls (Sun et al., 2024; Qin et al., 2023; Zhao et al., 2023a). Although effective, such approaches incur huge training costs and, when new modalities are introduced, almost always require partial or full retraining, which limits their flexibility. Moreover, when processing multiple control conditions, these methods tend to neglect weaker control signals. Recognizing that powerful pretrained controllable models (c.g., ControlNet) are already publicly available, some recent works (Nair et al., 2024; Wang et al., 2025b) such as MaxFusion avoid retraining and instead design fusion strategies to combine multiple ControlNet branches. However,

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the fusion strategy of MaxFusion is easily disrupted by the noise inherent in the denoising process, especially during the early sampling steps when latent variables exhibit high uncertainty (Karras et al., 2022; Cao et al., 2023). In addition, because it does not explicitly model the relationships between spatial conditions, it often struggles to generate harmonious images when the control signals complex or partially conflicting.
In this paper, we present Cross-ControlNe a training-free framework designed to address the challenges of multimodal conditional generation(Fig. 2). Our approach is grounded in two key observations detailed in Section 3.1: first, the intermediate features across different ControlNet branches are naturally spatially aligned, and second, the relative strength of each condition can be effectively quantified through both spatial and channel-wise variance. To realize this, we have developed three tightly coupled Ceapoonntss-iiiuuuss ChannelFusion, and V-IIneection that work in concert to progressively refine feature fusion and enforce conditional consistency, even when signals conflict.
We first introduce PixFusion, which performs pixel-level fusion under the guidance of Gaussian- smoothed spatial standard deviation (Section 3.2), handling spatial-level noise and alignment. By enhancing informative features and suppressing early-stage noise, PixFusion offers an effective, noise- resilient approach for from the well- phenomenon where, as feature dimensionality increases, cosine similarities cluster near zero, causing a fixed threshold to lose discriminative power rapidly. To alleviate this, propose ChannelFusion which operates along the channel dimension and, for each channel, adaptively applies either hard selection or soft fusion according to the channel-wise consistency ratio R. (Section 3.2), thereby addressing high-ddmensional fusion challenges. This mechanism effectively counteracts the loss of discriminative power in nigh-dimensional settings and complements PixFusion's 's pixel-level fusion. Although PixFusion and ChannelFusion enhance the fusion of spatial conditions, conflicting multimodal signals can blur the roles of foreground and background, undermining precise conditional control. To stabilize generation such cases, we introduce KV-- Injection, which transfers key-value pairs across ControlNet branches under text-derived attention masks (Section 3.3); this mechanism temporarily decouples foreground and background cues to resolve semantic-level conflicts, that each condition can be enforced more faithfully, thereby improving visual quality and conditional consistency.
feature fusion. However, pixel-level decisions alone suffer
Together, these three modules enable control signals impose contradictory spatial cues and complementary conditions- -where different controls reinforce the same scene structure. Our method preserves text-image alignment while accurately following the supplied spatial controls. On a newly curated 2,000-image multi-condition benchmark, Cross-ControlNett improves mloU by 5.4% and lowers MSE over the strongest training- free baseline under conflicting conditions while maintaining text alignment, and further boosts SSIM under complementary conditions. We further demonstrate its adaptability by producing qualitative results on the DiT-based FLUX model, showing that Coss--ontrollet transfers effectively beyond UNet backbones (see Appendix A.8).
handle both conflicting conditions-wher
2 RELATED WORK
Controllable generation. Text-to-image diffusion models often lack fine-grained spatial control, motivating the use of auxiliary conditions in addition to text prompts. Early works introduce specific signals-such as layouts/boxes (Zheng al., 2023; Xie et al., 2023), semantic segmentation maps (Avrahami et al., 2023), sketches (Bashkirova et al., 2023), and human pose keypoints (Ju et al., 2023) to anchor generation and enable interactive or artistic design (Bar-Tal Tal et al., 2023).0ooo More general frameworks (Mo et al., 2024; Zhang et al., 2023; Mou et al., 2024; Tan et al., 2024) emerged; among them, ControlNet duplicates the UNet encoder to encode single-modal conditions (e.g., pose, segmentation) into latent features that are injected into the diffusion UNet. To handle multiple spatial constraints, recent methods (Hu et al., 2023; Qin et al., 2023; Sun et al., 2024; Nair et al., 2024; Sun al., 2025) either train unified multimodal models combine several pretrained ControlNets. However, these approaches (Hu et al., 2023; Sun et al., 2024; Nair et al., 2024) often fail to produce harmonious and natural images when control signals interact in complex or conflicting ways.
Training-free image editing, Cross- attention manipulation enables text-driven edits without fine- tuning-for for example, Prompt-to-Prompt and Plug-and-Play (Hertz et al., 2022; Tumanyan al.,
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Figure I: Visualization of cemmeeddate-eaature statisties for different ControlNet modalities. Columns 1-2: original image and its corresponding control map. Columns 3-4: spatial-level variance maps. Columns 5-6: feature maps from the single channel with relatively high variance across channels. Columns 7-8: feature maps from the single channel with relatively low variance across channels.
2023) preserve structural consistency by modifying queries or keys but give limited control over value (V) features and thus struggle with large non-rigid transformations. Subsequent works inject key-value pairs to maintain consistency across editing tasks that involve multiple images (Cao al., 2023; Tewel et al., 2024; Alaluf al., 2024; Zhu et al., 2025; Li, 2024; Chung al., 2024). Inspired by these ideas, we inject key-value pairs across multiple ControlNet branches, allowing foreground- backgrruud decoupling and improving conditional consistency both complementary and conflicting settings while retaining the flexibility of text-to- eox-tto-image generation.
3 METHODS
3.1 GUIDING OBSERVATIONS
Before presenting the details of our method, we first introduce two key observations that motivate our design.
Observation Alignment of cross-modality features. Features produced by different ControlNet branches at identical spatial locations in Stable Diffusion (SD) (Rombach et al., 2022) exhibit natural spatial alignment. ControlNet encodes each conditional input into feature maps and injects them into SD's 's intermediate layers through element summation. Because the outputs from different ControlNets enter SD at the same spatial positions, their features share a common reference frame and can be compared directly across modalities without any additional spatial calibration.
Observation 2: Quantifiable condition strength. The strength of each control condition can be directly indirectly quantified via spatial-level variance maps and channel-wise variance vectors. As illustrated in Fig. 1, spatial variance maps of diverse control signals highlight regions where the condition exerts stronger influence. while channels with larger variance values indicate a more pronounced response to that condition. Together, these measures provide reliable estimate of each condition's relative strength across both spatial regions and feature channels, enabling informed feature selection during fusion.
Taken together, these observations provide empirical grounding for our design: cross-mode feature fusion can be systematically guided by simple, quantifiable measures of condition strength. Building on this, our approach performs targeted feature fusion across modalities, enabling more faithful and consistent image generation under multimodal conditions.
3.2 ROBUST FEATURE FUSION
Let us begin with a simple setting: fusing two independent modalities. Consider two ControlNet models Mi and M2 that process different types of input (e.g., edge detection and semantic segmenta- tion) and produce intermediate features fi and f2, respectively. A naive strategy is to average these features, but equal weighting often fails when each modality dominates different spatial region (for example, left vs. right halves of the image). In such cases, uniform averaging dilutes crucial information, motivating the need for adaptive fusion.
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Fonspround
Bockground
"Saddrman"
Mask Extraction
M
Layer-n
KV CV-Jnjection
Loyer-it
9b Encoder
PisFaaion
Sb Encoder
Cross: Attention
Layer (m=))
Injection
Layers/m=:1)
Laye-N
KV-Injection
LoyerNN
"Spidernan
ChsrneFusion
(a) Overall Pipeline of Cross-ControlNe
Foneground
EV-Injection
Vi
MAAT
Bockground
K,
V.
sofemast
O.Kf MMWW:
Va
PiFF/
(b) KV-Injection
(c) PixFusion ChannelFusioo
Figure 2: Cross-ControlNet Overview. (a) Intermediate features from different ControlNet modalities first fused through either PixFusion ChannelFusion, and the fused features then injected into the main denoising backbone. In parallel, the key-value pairs from the self-attention layers of the foreground ControlNet injected into the corresponding layers of the background ControlNet. (b) KV-Injection mechanism. Foreground and background key-value pairs are selectively merged within the self-attention layers to enforce cross-modal consistency. (c) Intermediate features are subsequently fused via PixFusion or ChannelFusion.
PixFusion. An ideal fusion method should assign different priorities to different spatial locations (i, k) according to the local conditional intensity, which can be measured by spatial-level variance maps of E HWW To make the variance maps from different modalities comparable, we introduce relative Htandardddeviation metric that normalizes the variance at each spatial location:
(i,k)
(j.k)
Σ (e o. (p.9)
Directly comparing these normalized maps, however, can be unreliable because the standard deviation maps themselves contaminated by noise. Recall that the diffusion process generates images by iterative denoising: at early sampling stages the latent space still contains strong high-frequency noise. Pixel-wise fusion this stage would therefore make the fused features highly susceptible to distortion.
To mitigate this, we apply Gaussian kernel smoothing, which yields the following fusion rule: f(A
((,,))
arg max Gr. * o (jA))
(2)
where Gn, is fixed Gaussian kernel and [Gx, * ōv](j.k) represents the value at location (j, k) after smoothing, i.e., average its neighborhood rather than a single pixel.
Nevertheless, blindly choosing the channel with the largest standard deviation not always desirable. When multimodal conditions overlap meaning different modalities describe the same object is preferable to integrate information from both. To this end we compute the correlation at each location (j, and, whenever features strongly correlated, we adopt mean fusion:
f6. f (i. + (i.
if (G..)) > 81,
where 8n is predefined threshold and glā.k) denotes the correlation between the centered features
p(a.8) and fo c(i.). Because these correlations can also be distorted by noise, we again apply Gaussian smoothing:
(() p(i.k)
C
(..)
= (fi f.) #group= Gwzi
F...N)
Σ
p
(()))())
(2.R)
(c), (4)
((.))))))))
|1
4

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3,000
5y60
By 0.
(a)
(b)
Figure 3: (a) Threshold degradation curve for different values. The horizontal axis indicates the depth of the feature layer, and the vertical axis indicates the proportion reaching the fusion threshold. (b) Variance maps of the fused intermediate features. Column 3: after PixFusion; Column 4: after ChannelFusion.
where Gx2 is a fixed Gaussian kernel, fi is the channel-wise mean of fi, and the subscript group = C indicates depth-wise wise convolution (the number of groups equals the channel count C).
In summary, if p(a.)) >> 81, we use the mean-fusion rule in equation 3; otherwise, we apply the noa-sttadarrddeviaatoo rule in equation This mechanism automatically selects the more relevant spatial conditions and provides reliable conditioning even when the modalities conflict. Moreover, Gaussian smoothing suppresses the influence of early-stage noise, greatly improving the robustness and aceuracy of the fusion process.
ChannelFusion. Before introducing ChannelFusion, first revisit the limitations of PixFusion. Although PixFusion shows strong robustness to noise, it relies on a fixed-threshold fusion strategy that suffers from what we term threshold degradation in high- dine-niinnii spaces. More precisely, as the feature dimensionality increases, the effectiveness any -hee-threshold fusion progressively deteriorates.
For high-dimensional random vectors X, Y E Rdd with i.i.d. Gaussian components. (( o²), their cosine similarity p(X, Y) satisfies
[p 0, Var(p) d'
(5)
(See Appendix A.2 for proof.) This indicates that, in high dimensions, similarities between random vectors become increasingly concentrated around zero. Consequently, the pixel-level fixed-threshold fusion strategy becomes unreliable in high-dimensional regimes. Our empirical validation (Fig. 3a) confirms this phenomenon, showing a monotonic decrease in the proportion of features that satisfy the threshold as dimensionality grows.
To overcome this limitation, we propose ChannelFusion, hybrid fusion strategy that adaptively combines hard selection and soft fusion based on standard-deviation consistency ratio. This ratio leverages channel-level standard deviation statistics to quantify the relative condition strength of each modality, providing a dependable approach for feature fusion in high-dimensional embedding spaces.
Formally, consider the intermediate-layer features along the channel dimension. For the e-th channel, let &1,c; 02 E denote the standard deviations of the two modalities. We define the standard- deviation consistency ratio
101. 02.el
Re 1
(6)
max (o1,x T2,e)
which measures the relative consistency of conditional intensity between modalities for each channel (R, E [0, 1)).
Hard selection. When the consistency ratio Rs is below predefined threshold 82, we adopt a hard selection strategy and keep only the feature from the modality with the larger standard deviation:
fc =
fsr.c
arg max ōi.c-
This preserves the most informative features while discarding potentially noisy components.
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Soft fusion. When R. 2 62, employ soft fusion strategy that blends the features of both modalities using standard-devine -eviataio deteeiaiiiiioooooootoonn proportional weights:
fe = (A1,e + A2.))
(A1.c A
A2. f2.c
(8)
Here Ai denotes the precision (inverse covariance) of channel in branch i. In practice the isotropic case Ai. = A where the scalar A is proportional to the feature strength, A x Ōz,c- For numerical stability we set Ar, max(f1,c.02..) (see Appendix A.3 for details). This weighted combination fully exploits the informative features from both branches.
ai.e
This channel-wise adaptive strategy enables flexible feature fusion guided by conditional intensity, thereby improving image quality under multimodal conditions. By operating along the channel dimen- sion rather than the spatial dimension, ChannelFusion effectively circumvents the high-dimensional threshold degradation problem and maximizes information retention.
3.3 FOREGROUND-BACKKGOUUN DECOUPLED KV-INJECTION
In multimodal settings with conflicting control signals, directly fusing intermediate-layer features of- leads ambiguous spatial conditioning, where different control inputs compete and the generator struggles to separate their contributions. We observe, however, that most generated images naturally exhibit a clear structural decomposition: a foreground object against background scene. This obser- vation suggests that treating foreground and background as distinct targets can reduce interference between competing conditions. Guided by this insight, we propose Foreground Backurgund De- coupled KV-Injection, which injects key-value pairs across ControlNet branches under text-derived attention masks to explicitly disentangle foreground and background representations.
Following prior works (Parmar et al., 2023; Liu et al., 2024; Mou et al., 2023; Hertz et al., 2024), we exploit the fact that cross-attention maps derived from the text prompt predominantly capture shape and structural cues. We therefore use these semantic cross-attention maps to automatically generate masks that distinguish foreground from background regions.
At denoising step 1+1, within the backbone network's cross-attention layers. aggregate all attention maps of resolution 256 x 77 (as in standard P2P practice) by averaging them to obtain 77 mask matrices of size 16 x 16, where 77 is the number of encoded prompt tokens (including padding). At step f, we select the averaged cross-attention map corresponding to the token most semantically relevant the foreground object and, after post-processing, obtain the foreground mask M and its complementary background mask &
Using these masks, perform region-isolated injection inside the ControlNet self-attention layers: the key-value pairs of the foreground ControlNet are constrained to their designated regions via mask modulation, while the background ControlNet acts as the primary driver of global generation:
K4 = [K2 + Kı],
V+ = 112 + Vil,
M+ = [0.=[1e 1 M²},
Q2KI Vd
Autn = softmax
+ log M
Vi,
Q2K+ + log M4
Att softmax
V+,
Vd
Actn Mr.Attin + (1 (1-MM))AAA M't-Attn'.
Here Q1, K1. Vi denote the query, key, and value from the ControlNet conditioned on the foreground map, while Q2, K2, V/2 are from the background ControlNet; @ indicates matrix concatenation. The final attention output Aftn ensures that each region retrieves information exclusively from its designated feature domain rather than from global features (see Fig. 2(b)).
In practice, KV-Injection is applied to all self-attention layers beginning at denoising step 5. This timing allows the early stages of the diffusion process to establish coarse structure before the injected signals guide finer foreground foreground-buckground background alignment, consistent with established denoising principles (Cao et al., 2023; Tewel et al., 2024).

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Foreground
Bockground
Multi-Adapter
Multi-ControiNet
Uni-ControiNet
AnyControl
MaxFuusion
Ours
Figure 4: Qualitative comparison under conflicting conditions. Prompts: (1) "Teddy bear in red scarf on train windowsill." (2) "A woman stands golden rapeseed field." " (3) "Po meditates beneath the ginkgo tree, watercolor style".
4 EXPERIMENT
4.1 IMPLEMENTATION DETAILS
All experiments are conducted on a single NVIDIA RTX 3090 GPU. We adopt Stable Diffusion v1.5 as the T2I backbone because of its rich ControlNet ecosystem and relatively low GPU-memory footprint. Our Cross-ControlNet is also compatible with DiT-based architectures, as demonstrated in Appendix A.8. Sampling uses the UniPC scheduler (Zhao et al., 2023b) with 50 steps and classifier-free guidance scale 7.5. The correlation threshold 82 and the consistency ratio threshold 82 are both set to unified value 5 0.7. ChannelFusion is applied only in the final ControlNet layer, whereas PixFusion is used in all remaining layers. The kernels Gx2 and G are standard 3x3 Gaussian kernels normalized unit
Because no public dataset exists for conflicting multi-condition generation tasks, we construct a synthetic dataset based on COC02017 (Lin et al., 2014). Following the procedure of the original ControlNet paper, we generate multiple conditional maps Canny edges, depth maps, HED soft edges, OpenPose keypoints, and segmentation masks-using publicly available pre-trained models (Ranftl et al., 2020; Xie & Tu, 2015; Cao et al., 2019; Ravi et al., 2024). These maps are combined to form our synthetic dataset. For evaluation, 2,000 images are randomly sampled from the COC02017 validation set, and BLIP-2 (Li et al., 2023a) is used to produce descriptive captions as text prompts. We compare Cee with state-of-the-an approaches including Multi-ControlNet (Zhang et al., 2023), Multi-Adapter (Mou et al., 2024), Cocktail (Hu et al., 2023), Uni-ControlNet (Zhao et al., 2023a), AnyControl (Sun et al., 2024), and MaxFusion (Nair et al., 2024).
4.2 QUALITATIVE EVALUATIONS
We present qualitative comparisons of Cross-ControiNNet under both conflicting and complemen- tary control conditions in Fig. 4 and Fig. respectively. For conflicting conditions, where two control inputs impose contradictory spatial cues, we compare our method with Multi-Adapter, Multi- ControlNet. AnyControl. Lni-ControlNee, and MaxFusion. These baselines often fail reconcile the conflicting signals: they typically introduce artifacts or degrade one of the intended structures. In contrast, Cross-ControlNet preserves both control cues and aligns more faithfully with the text prompt, producing sharp and semantically consistent results. For complementary conditions, where the control inputs reinforce each other, all methods generally generate plausible images. Neverthe- less, our approach yields crisper object boundaries and more coherent backgrounds, demonstrating stronger alignment with both local and global structures. Overall, these results show that Cross- ControlNet achieves robust conditional consistency and high visual fidelity across both challenging conflicting scenarios and easier complementary ones. Additional qualitative examples are provided in Appendix A. for reference.
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Aulpi-Adoster
AnyControl
Qualitative comparison under complementary conditions. Prompts: (1) "Brown-white dog on bench, blue sky." (2) "A deer the snowy forest" (3) Corgi by red ancient wall." ".
Figure 5:
Table I:
Quantitative results for conflicting conditions, means higher is better, J lower is better.
Best in bold, second-bes best underlined.
Train- free
Pose, Seg
Pose, Depth
Method
CLIP
MSE-S $
mloU-P 1
CLIP
MSE-D
mloU-P t
Multi-Adapter
0.2738
0.2606
0.2911
0.2860
0.0516
0.2589
Multi-ControlNet
V
0.2999
0.2282
0.3145
0.2978
0.0625
0.3150
AnyControl
X
0.2853
0,2522
0,2042
0.2638
0.0338
0,1519
Ani-ControlNet
0.2748
0.2310
0.1858
0.2695
0.0355
0.1445
Cocktail
0.2946
0.2037
0.1079
MaxFusion
0.2945
0.2016
0.3528
0.2870
0.0498
0.3555
PixFusion (ours)
0.2947
0,2004
0,3610
0.2887
0.0495
0.3583
+ChannelFusion (ours)
0,2952
0,2009
0,3648
0,2865
0.0476
0.3618
Cross Cosss-ContolNeet (ours)
0.2978
0.1983
0.3719
0.2893
0,0456
0.3671
4.3
QUANTITATIVE EVALUATIONS
Analysis of Conflicting Conditions. The dataset constructed in Section 4.1 is insufficient for this analysis because lacks cleanly separated foreground and background images. To address this limitation, we curated background images from BG-20K (Li et al., 2022) and generated pose maps for human-centris images selected from COCO2017, forming roughly 2,000 foreground Koreground-buckgroundt CLIP-Scone to measure text-image alignment and MSE and mloU to
pairs. For evaluation, quantify condition fidelity. As shown Table I, Cross- Coss--oontolNet consistently outperforms other multimodal methods in condition fidelity while maintaining competitive text alignment. These results indicate that our model can reconcile complex combinations of conflicting conditions and produce high-quality outputs that remain faithful to both the text prompt and the control inputs.
Analysis of Complementary Conditions. AI- though Cotos-CCotoooNet is primarily designed for conffictin--conditonn condition scenarios, it also performs strongly on complementary tasks. Here we em- ploy the dataset introduced in Section 4.1 and fol- low the experimental setup of MaxFusion. Image quality is assessed with NIQE, text- rext-image align- ment with CLIP-Score, and condition fidelity with MSE and SSIM. As reported in Tables 2 and 3, Cross-Controlle surpasses competing models overall,
Table 2: complementary results (Hed, Seg)
Method
|SSIM
NIQE
CLIP
MSE-H 1 MSE-S J.
Multi-
0.2021
3,9493
0.2802
0.0746
0.1512
MaxFusion
0.2382
3,2499
0.2992
0.0766
0.1135
CnnCrnl
0.2251
4.8219
0.2989
0.0554
0.1046
Ours
0.2404
3.2434
0,3005
0.0736
0.1008
particularly in condition consistency and text-image alignment.
4.4 ABLATION STUDY
We conduct systematic ablation studies (Table 1) to assess the contribution each core component in Ceoss-ControlNet. Three critical modules- PixFusion, ChannelFusion,, and K KV-Injection inte-
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Table 3: Quantitative results for complementary conditions.
Hed, Depth
Seg. Depth
Method
SSIM t NIQE L
CLIP 1 MSE-H 1 MSE-D J
SSIM f NIQE
CLIP f MSE-S
MSE-D
Multi-Adapter
0.2411
3,5993
0.2962
0.0617
0.0218
0.2186
3.7793
0.2824
0.1384
0,0314
MaxFusion
0.2566
3.3913
0.2961
0.0615
0.0231
0.2358
3,4324
0.3022
0.1126
0.0257
AnyControl
0.2276
4.7683
0.2986
0.0549
0,0222
0.1975
4.5265
0.3014
0.1116
0,0241
Ours
0.2622
3.4153
0.2971
0.0570
0.0212
0.2428
3.3887
0.3022
0.1105
0.0241
grated incrementally, and we evaluate performance at each stage. Quantitative results show that each added component progressively improves conditional consistency while preserving textual alignment. In particular, even the standalone PixFusion configuration already surpasses the MaxFusion baseline in image synthesis metries under conflicting scenarios. These findings highlight that the combined use of our proposed modules establishes state-of-the-art performance for controllable generation under conflicting conditions.
We also analyze the influence of the threshold å on image synthesis. As illustrated in Fig. 6, when = 0 the strategy degenerates to naive averaging, which weakens conditional information and yields images that not faithful the pose map. This effect persists until ő reaches roughly 0.6, where the images become more faithful to the conditions; however, some artifacts remain, such as faint "lines" in front of the person. At 6 0.8, the results achieve the best trade-off between conditional faithfulness and image quality. When 8 = 1.0, the boundary between the person and the mountain becomes noticeably unelear. Overall, we find that d 0.7 provides favorable balance between semantic coherence and detail preservation.
Conditions
6 ==0
6 = 0.2
= 0.4
0.6
6 0.8
= 1.0
Figure Ablation study on different values. The prompt is "A man in a mountain, Monet style." "
Furthermore, we perform fine-grained ablation of KV-Injection to determine its optimal insertion policy (see Appendix A.7). By varying both the starting denoising timestep and the first Cross- ControlNet block that receives injection, we find that beginning injection an early but not initial timestep (timestep = 5) and starting from the earliest Cross-ControlNe block (layer = O) yields the best trade-off:: injecting step can slightly destabilize early denoising and harm perceptual scores, while very late injection reduces controllability; similarly, delaying the start-layer to deeper blocks weakens foreground foreground-backgroundd background propagation. These observations validate our default choices (starting timestep 5, starting layer = 0).
5 CONCLUSION AND LIMITATION
We presented Cross Net, a training-free tion that effectively handles both conflicting and complementary control signals. By integrating three key Moddules-PixFusion ChannelFusion, and KV-Injection-our method mitigates challenges such as noise sensitivity and the degradation of fixed-threshold strategies in high-dimensional settings. These modules work synergistically to strengthen feature-fusie fuuure robustness, improve conditional consistency. and preserve semantic aligmment with text prompts. Extensive experiments demonstrate that Cross-ControlNet delivers high-quality, harmonious images and consistently outperforms existing multimodal baselines. Looking ahead, we plan to refine these mechanisms and explore their extension to spatiotemporal domains such video synthesis and 3D scene generation.
framework for multimodal conditional image genera-
Limitation. Our framework relies on the availability pre-trained single-condition ControlNets; cannot directly support novel control modalities without such models. Combining multiple ControlNet branches also increases memory footprint and inference latency, which may limit deployment at very high resolutions in real-time settings. Moreover, while method improves consistency under conflicting conditions, extreme and mutually incompatible constraints can still lead to foreground- boundary artifacts or color bleeding.

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frreinng--eeeeee free image editing for
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A APPENDIX
A.1 STATEMENT
LLM Usage Statement. This paper's ideas, methodology, code, and experimental design were entirely conceived and implemented by the authors. Large Language Models (OpenAI GPT-5) were used only for minor English language polishing of the manuscript; no model output contributed to the research content itself.
Ethics Statement. Our work focuses on improving controllable text-to-image generation. All datasets used in experiments are publicly available and contain only non-personal, non-sensitive images. The proposed method does not introduce additional privacy, safety, or fairness concerns beyond those already present in standard diffusion models.
Reproducibility Statement. We release source code at http://aaoonyoouu-oop...cici Cross- ControlNet-6CST: All implementation details, hyperparameters, and experimental settings are provided in the main text and appendix to facilitate full replication of our results.
A.2 COSINE SIMILARITY OF INDEPENDENT GAUSSIANS
Let X, Y Rd be independent random vectors with i.id. components X;, Y; NN o²). Define the cosine similarity
X X
(10)
Y
is well known that for a Gaussian vector X ~ NO0, o² Ia), the direction U X is indepen- dent of the radius ||X|| and is uniformly distributed on the unit sphere 5d-1, same holds for Y. Hence we can write
U
(11)
where U.V Ve E 5 are independent and uniformly distributed on the sphere.
By symmetry, the mean of each coordinate of V zero, which implies E[[] = 0. Therefore
Esp] E V E E [[]]
(12)
To compute the variance, observe that
Var(p) = Blp²) B((U V)
(13)
Since U and are independent, we can condition on U:
B[(U V) [E 2 U]]
(14)
For fixed U, we have
BIU
= URVV
(15)
Rotational invariance of the uniform distribution on the sphere implies that E[VV must be scalar multiple of the identity, say E[VV = cla. Taking traces gives
ed td= tr (E(VV) =[]]]]]
(16)
hence 1/d. Therefore
(1 110 1. Since this conditional expectation is constant (does not depend on U), we have
(17)
Elp²| 4
(18)
and consequently
[pp 0, (ar(p)
(19)
d.
This completes the proof of Formula 5. Notably, the result does not depend on the variance o², but only the rotational symmetry of the Gaussian distribution.
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A.3 DERIVATION OF CHANNELFUSION VIA VARIATIONAL INFERENCE
We derive the ChannelFusion method using variational inference and evidence lower bound (ELBO) maximization. For fixed channel index e, we consider two feature maps k1. kg R"* which we vectorize to treat as vectors in g!W
Notation clarification: in the main text, these correspond to the channel-specifie features fl,e and f2.e. i.e., K1 fi.e and kę = f2.e- We assume that the actual semantic feature is which is unobservable, while the observable features are and kg. Since y has no exact observed value, can only use q(y) to describe its distribution and estimate g(y) through kj and k2.
A.3.1 GENERATIVE MODEL AND VARIATIONAL SETUP
To enhance robustness, assume that in the absence of any observed values, y generally follows Gaussian distribution ply). Furthermore, since k1 and kg are observations from potentially biased channels, they inevitably deviate from y. We model this observation bias using diffusion process, where k1 and k2 follow Gaussian distributions p(kıly) and p(kg y) with y as the mean and Σ,, Eg (representing the respective channel biases) the covariance matrices:
p(y) (y po: Eo) pkk,ly) = (Vkyyy E,), i 1,2,2
(20)
Similarly, we also use Gaussian distribution to model the estimated g(y).
g(y) = (yl po. En)
(21)
where μgl is the mean and Σy is the covariance matrix.
A.3.2 EVIDENCE LOWER BOUND (ELBO)
We perform variational inference by maximizing the Evidence Lower Bound (ELBO) of g(y) given the two observations kj and k2, where the ELBO C(g) is defined as:
((4) = 1ytt)) log ply, k1, kz)
= Eg(ry) log p(k k2ly)p(y)
(22)
q(y)
g(y)
A.3.3 DETAILED DERIVATION OF THE ELBO AND OPTIMAL PARAMETERS
Substituting the Gaussian densities into the ELBO and letting D HW denote the dimensionality, we have:
((1) / Nylmo, Ey) log
[N(y|mo, Ex) IT Wik,ly. Ei) dy
N(y 10 En)
= N Ey)
(o)- T Σ, ly mo) I Mi=1 (2m) E 1/2 [kk (0) (
log
Ed
dy
1/1 = p2o) E, (y-
(23)
Simplifying the logarithm, obtain:
2
C(g) / NYy)u us: Eny
log [Eyl 1/2 -log Ev 1/2
log (/2))
1=1
2
(yy - TinTTTn1 - /40) +
Σ
(k; - yy '0- (k; - yy)
(24)
1=1
(y - uua)")" " mm0)
dy.
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Since N Myl Σy) is probability density, the terms independent of y be moved outside the integral. The integral of (yyl (gy En) is I, leading to:
[(g) = 1 log Eyy
1 log Eol
Σ 1=1
log (2T)
2 a(y)) ((y))((> [Iy 110) -D0 (y
mm) +
E(k; (k: - y) (ea--yy) Ik; - -y)
(25)
(y u " (y po)
dy
We now expand the quadratic forms and use the properties of expectations under q(y): Ely] 448 and Elyy?
Ey + u0
[ m40 En 1 (y - [O01 = (wo - (10) "nj (mo 140 + tr tr(2ij) m,,)
[[(k: -y) " D;; (k; y)] = (ki14a) E (k: (40) + tr(2, Mny E,,)
(26)
E[(y (u)) " (y [1o) = -Ir(Z, Eyy) = -
Substituting these expectations back into the ELBO, get the final expression:
2 1 Σ
1
D
((g)
2 log Eyl 2
log Eol
log
2
2
1=1
2 [1100 - -100 ' - Mo) + tr( (Ej tr(E)'Ey)
(27)
2 [ik: m1)) T m0) +r(2,-10)
Optimal Variational Parameters
To find the optimal variational parameters, maximize the ELBO with respect μ4a.
Optimal Mean pa: The terms containing 44o are:
1 (wg -40 "ET'"" (mr - 1o) - 1 2
(k:
m4)
1k= - 13)
(28)
2 1=1
Taking the gradient with respect to p4 and setting it to zero:
2
V Puo = -E (HO
po)
+
(k, mu)
(29)
2 + Σ
E
p1g +
En uy + Σ
Σ K;
=
i=1
1=1
Solving for pa gives the optimal mean:
n (2g' Ej + )
(Σ) no + En 'k + -²)
(30)
A.3.4 SPECIALIZATION TO CIANNEEFCCSION
For practical implementation in ChannelFusion, we make the following simplifying assumptions: 1. Isotropic Covariances: Σ, A,I. where I the identity matrix. Uninformative Prior: A ->
0, which implies En1 + 0. 3. Precision Proportional to Feature Strength: A; max(o1...2.e)
where 0g represents the feature strength of the c-th channel in the i-th feature map.
Remark. Here the quantity ô,, is the empirical standard deviation of pixel values within the c-th channel of the feature map, obtained by treating the channel elements as samples. serves only as
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proxy for the amount of useful information carried by that channel and should not be confused with the observation noise variance in the Gaussian generative model. Accordingly, in practice we instantiate the Bayesian "precision" weights X via monotonic proxy of channel strength, setting X; ix Öi This serves as a practical surrogate for the (typically unavailable) inverse noise variance and does not alter the underlying model.
Substituting these assumptions into the optimal mean equation 30:
14 (AoI+ A
(AoI + X IK + x2Ik2)
= (AII + x
(A1kı
22k2) (since Ào - 0)
(31)
\ k1
A2
K2
A + A2 \1 22
This gives the ChannelFusion soft fusion rule:
A1 ki d1 A2
22 A1
14
k2
(32)
+
The hard selection rule is obtained in the limit where one A₃ dominates the other (effectively setting the smaller zero), which corresponds to complete certainty in one feature map.
A.3.5 DISCUSSION
This derivation shows that ChannelFusion can be interpreted as performing variational Bayesian inference under conjugate Gaussian generative model. The optimal fusion rule 32 is derived by maximizing the ELBO, providing principled justification for the weighted average of feature maps. The precision parameters Å; act reliable estimates of the useful information content in each feature map, determining their contribution to the fused output. The assumptions isotropy and an uninformative prior make the method computationally efficient and suitable for integration into deep neural networks.
A.4 LATENCY AND MEMORY USAGE
Table 4 compares the efficiency of different multi-condition control methods. In terms of computa- tional overhead, training-free Cross-ControlNee demonstrates clear advantages over training- based approaches such as AnyControl and Uni-ControlNet, achieving faster inference while consuming less memory. Mcanwhile, compared with MaxFusion-thhe strongest existing training-freee baseline-the
Table 4: Efficiency comparison of different multi-condition models.
Metric
Multi-Adapter Cocktail Multi-ControlNet AnyControl Uni-ControlNet MaxFusion
ours
Time (s)
8.135
10.361
12.161 10.52
21.434
15.558
12.932
14.759
VRAM (GiB)
8.40
9,99
13.81
12.63
10.60
12.07
additional inference time and memory cost introduced by our method are very limited. This small overhead yields substantial improvements in generation quality when handling conflicting or com- plementary conditions (as discussed in Section 4), showing that our framework achieves a favorable balance between efficiency and performance.
A.S ABLATION ON CHANNELFUSION
To further demonstrate the rationale for applying ChannelFusion only at the final layer, conduct an ablation study on the starting layer where ChannelFusion is applied. We disable the KV-injection module and retain only PixFusion and ChannelFusion to avoid confounding factors.
Table reports the ablation results of applying ChannelFusion at different network depths. Consistent with our theoretical expectations, applying ChannelFusion to deep layers yields the most significant performance gains, whereas applying it to shallow layers leads to noticeable degradation across all metrics.
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Table 5: Ablation on the starting layer of the ChannelFusion under conflicting conditions.
Pose, Seg
Pose, Depth
Variant
CLIP f
MSE-S 4
mloU-P
CLIP 1
MSE-D
mloU-P
Only PixFusion
0.29477
0.2004
0.3610
0.2887
0.0495
0.3583
+ ChannelFusion (final layer)
0.2952
0.2009
0.3648
0.2865
0.0476
0.3617
+ ChannelFusion (layer-5)
0.2950
0.2017
0.3638
0.2865
0.0480
0,3632
+ ChannelFusion (layer-4)
0.2953
0.2029
0.3632
0.2869
0.0481
0.3621
+ ChannelFusion (layer-3)
0.2954
0.2028
0.3636
0.2868
0.0480
0.3623
+ ChannelFusion (layer-2)
0.2959
0.2053
0.3512
0.2893
0.0454
0.3267
+ ChannelFusion (layer-1)
0.2951
0.2052
0.3536
0.2885
0.0435
0.3161
+ ChannelFusion (layer-0) 0.2951
0.2053
0.3447
0.2883
0.0427
0,3003
Table 6: Ablation on Gaussian kernel under conflicting conditions.
Pose, Seg
Pose, Depth
Variant
CLIP
MSE-S
mloU-P f
CLIP t
MSE-D L mIoU-P
Cross-Controlle (Gx=0.2)
0.2969
0.1986
0.3703
0.2887
0.0449
0.3654
Cross-ControlNec (G.=0.44
0.2972
0.1984
0.3714
0.2884
0.0445
0.3658
Cooss-onttolNet (Gx=0.6)
0.2980
0.1986
0.3708
0.2882
0.0460
0.3677
Cross-ControlNee (Gx=0.8)
0.2983
0.1998
0.3718
0.2889
0.0463
0.3695
Cross-Controlle (Gx=
0.2978
0.1983
0.3719
0.2893
0.0456
0.3671
Cross- Centrolent (Gm==1.2
0.2985
0,2002
0.3693
0.2889
0.0465
0,3690
Cross-ControlNy (G,=1.5)
0.2989
0.2010
0.3657
0.2893
0.0462
0.3661
Cross-Controld (Gx=2.0)
0.2989
0.2005
0.3668
0.2894
0.0460
0.3649
Cross-ControlNet (Gm=3.o)
0.2990
0.2008
0.3667
0.2894
0.0460
0.3636
We attribute this phenomenon to the fundamental nature of features learned at different levels of the U-Net encoder. Shallow features typically contain basic and general structural information-sun as edges and textures-tte that respond to multiple control signals. However, their variance primarily reflects local pixel intensity rather than high-level semantic condition strength. Forcing variance- based channel selection (ChannelFusion) at this stage prematurely discards these general features that form the backbone of the image, disrupting subsequent feature construction and ultimately weakening structural controllability.
In contrast, deep-layer features correspond to more abstract and semantically meaningful repre- sentations. At this stage, the feature channels associated with different control conditions have been progressively distilled, and their variance can more reliably reflect each condition's 's semantic importance. Applying ChannelFusion here enables effective information selection and fusion in high-dimensional semantic space. precisely compensating for the "threshold degradation" issue that PixFusion faces at deeper layers.
Therefore, our default design design-applying ChannelFusion only in the final layer-is carefully considered trade-off. It allows PixFusion to freely handle spatial noise and fuse basic features the shallow and middle stages, while enabling ChannelFusion operate at the most discriminative semantic layer. This ensures robust information fusion and achieves the best performance balance under conflicting conditions.
A.6 ABLATION ON GAUSSIAN KERNEL.
To evaluate the robustness of Cross-ControlNett to noise, we conduct an ablation study on the Gaussian kernels Gx2 and Gr2 used for smoothing variance maps in the PixFusion module. This experiment is performed with the complete framework where PixFusion, ChannelFusion and K KV-Injection are all activated, assessing the role of Gaussian kernels within the full system. In our experiments, fix the kernel size to 3 x and set λ1 R2 for simplicity, where denotes the standard deviation of the Gaussian kernel. We systematically vary y and observe its impact on generation quality, with results shown in Table 6.
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The experimental results indicate that when S 1.2, the model performs stably across all metrics, demonstrating that appropriate smoothing provided by Ge, and Ge2 effectively suppresses early- stage noise and works synergistically with ChannelFusion and KV-Injection to enhance the reliability of feature fusion. Particularly, around k = 0.8, the model achieves the best trade-off between conditional consistency and image quality. When increases further (e.g., K 2 1.5), some metrics show slight degradation, suggesting that excessive smoothing may weaken the model's ability to preserve ene-grained conditional details, thereby affecting its coordination with other modules.
In summary, the Gaussian kernels Gx₃ and Gx2 play a crucial role in noise suppression and feature stabilization within the complete Cooss-ContoolNet framework. moderate value (e.g., 0.8) achieves an optimal balance between noise robustness and conditional fidelity.
A.7 ABLATION ON KV-INJECTION
We further examine the control policy of KV-Injection by varying (1) the starting timestep in the denoising process and (2) the starting layer within Coass-onnoooNet.co which injection begins. Here, we explicitly clarify that the term starting layer denotes the index of the first Cross- Constolont block that receives K KV-Injection; injection applied from this block onward subsequent blocks in the network. This distinction is important because carlier vs. later start layers determine how much of the feature hierarchy is exposed to the injected key-value information.
All other components are kept identical to the main experiment; only the KV-Injection settings are modified.
The upper block of Table 7 reports the results when the starting layer is fixed to and the starting timestep is varied. Intuitively, an earlier timestep gives the injected correlation signal more opportunity to shape the denoising trajectory, whereas very late injection fails to sufficiently influence the reconstruction. Empirically, starting too early (e.g., step 0) can slightly destabilize the early denoising dynamics and degrade perceptual scores, while starting too late reduces controllability. A starting timestep therefore provides a favorable trade-off between influence and stability.
In the lower block, fix the starting timestep at 5 and vary the starting layer. Injection beginning at layer O yields the best performance: by introducing the conditioning signal at the earliest Cross- ControlNet block, the guidance can propagate through the full depth of the module and inffuence both low- and high-level features. Delaying the start to deeper layers reduces this propagation, leaving earlier features effectively unconditioned and weakening foreground-baccgroundd alignment; this manifests as lower scores in the reported metrics.
Overall, these ablations validate the default choice used in the main paper (starting timestep 5, starting layer = O). They also show that K KV-Injection is reasonably robust across a range of timesteps and start-layer choices, but attains the strongest and most consistent control when initialized early in both the denoising schedule and the Cross-ControoNet hierarchy.
Table 7: Ablation the starting timestep and starting layer under confficting conditions.
Pose, Seg
Pose, Depth
Variant
CLIP
MSE-S | mIoU-P t
CLIP
MSE-D
mIoU-P
Cross-ControlNet (step-0, layer-0)
0.2976
0.2003
0.3741
0.2898
0.0478
0.3587
Cross-ControlNet (step-5, layer-0)
0.2978
0.1983
0.3719
0.2893
0.0456
0.3671
Cooss-ContoolNet (step-10, layer-0)
0.2968
0.1981
0.3712
0.2862
0.0447
0.3647
Cooss-ContooNNettt (step-15, 15, layer-O)
0.2956
0.1989
0.3725
0.2863
0.0449
0.3657
Cross-ControlNet (2tep-20, layer-0)
0.2952
0.2011
0.3736
0.2862
0.0462
0.3641
Cross-ControoNet (layer-O, step-5)
0.2978
0.1983
0,3719
0.2893
0.0456
0.3671
Cross-ControlNet (layer-2, step-5)
0.2974
0.2068
0.3703
0.2877
0.0483
0.3680
Cross-ControlNet (layer-4, step-5)
0.2970
0.2072
0.3691
0.2902
0.0448
0,3653
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Conditions
==0 6 == 0.2
6 == 0.4
0.6
== 0.8
6 = 1.0
Figure 7: Visual comparison under varying threshold 6. For small 5 (< 0.4) the generated image is not sufficiently faithful to the given foreground/background conditions. As ó increases, adherence gradually improves; however, for large ó (> 0.6) distinct white rim appears between foreground and background. Although such results are extremely faithful, the rim destroys photorealism and breaks the overall harmony. Prompt: photo of cowboy riding black horse in street, harmoniously".
Figure 8: Spatial variance maps of image tokens processed by the MM-DiT blocks of FLUX.1-de ControlNets. These variance maps also reflect the strength of the conditional information.
A.8 CROSS-ARCHITECTURR GENERALIZATION, U-NET DIT (FLUX)
Our method is not limited to U-Net architectures; it can be seamlessly transferred to diffusion- transformer models such as FLUX. We conducted tests using FLUX.1-dev with 28 sampling steps. and the classifier-free guidance was set to 3.5. The correlation threshold 5 was set to 0.50 (see Fig. 7 for visual examples). KV-Injection is recommended to take effect from steps 5 onward. When adapting to Transformer architectures like FLUX, the explicit Gaussian smoothing in PixFusion is naturally superseded by the self-attention mechanism. Through attention weighted aggregation of token features and layer normalization, the Transformer inherently provides adaptive noise smoothing, offering more elegant alternative to manually designed Gaussian kernels. Beyond these changes, the model maintained the same settings as in the main paper.
Although FLUX replaces the convolutional U-Net backbone with a DiT-style Transformer archi- tecture that relies global self-attention, proposed variance-based feature fusion (PixFusion and ChannelFusion) and KV-Injection remain entirely compatible without additional architectural tuning. The former leverages feature statisties that are urchitecture-agnostic while the latter oper- ates directly on attention mechanisms. As further evidenced in Fig. 8, the feature variance within FLUX likewise reveals clear intensity patterns, confirming that our correlation- based mechanism can exploit informative representations despite the architectural change. In practice, the injection of correlation- guideed key-value pairs integrates smoothly into the Transformer blocks, demonstrating that the method is agnostic to whether the latent features originate from convolutional layers purely transformer- based representations.
Qualitatively, observe that FLUX produces sharper fine-graines textures and more globally consistent structures compared to its U-Net counterparts, while maintaining the same level of foreground backunnuudd controllability (see Fig. 9 and Fig. 10). This indicates that robust to architectural changes and can preserve the intended conditioning signals even when the underlying diffusion backbone shifts from U-Net to DIT.
Naturally, the method method-originally designed for U-Net also performs well on the J-Net-based SDXL model; we omit these results for brevity.
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Brown bear standing on mossy rock in forest.
Camping tent grass with mountain backdrap.
Berry-topppd cream cake wooden plate with roses.
Colorful hot-air ballon over terra-cotta mountains.
brown drick drinks water from a pond.
A bird with red chest perches on snow-laden branch.
Orange tabby cat lying on the floor.
Orange vintage car parked by old European buildings.
Figure 9: Qualitative results of the Cooss-ControlNet.omm on FLUX.1-dew Because only a limited number of open-source ControlNet extensions currently available for FLUX, present conditioning only with Canny, Depth, and HED maps.
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Ghibli 2D: teen witch on broom over Eiffel Tower.
WALL-E is in the supermarket.
Baby penguin seffie in museum gallery.
Red bicycle, purple wall, white window, minimal.
CO CA
Figure 10: Qualitative results the Cross-ControlNet on FLUX.
A.9 EXPERIMENTAL COMPARISON ON BLUXX1-DEV
To further validate the effectiveness of our method on DiT-based architectures, we compare Cross- ControlNet with EasyControl (Zhang et al., 2025), a state-of-the-us training-free approach for multi-condition control on FLUX. 1-dev. Due to the limited availability of open-source ControlNet extensions for FLUX.11dev, our comparison is conducted under complementary conditions.
Quantitative Evaluation. Quantitative results are presented in Table 8. Our method achieves slightly lower conditional fidelity metrics compared to EasyControl, which is reasonable since Cross- ControlNet was specifically designed for handling conflicting conditions rather than complementary ones. In contrast, our approach demonstrates superior text alignment as evidenced by higher CLIP scores. terms of efficiency,
reduces inference time by 24.8% while maintaining comparable memory usage with EasyControl. This efficiency advantage, combined with competitive performance, highlights the practical value of our approach within the training- free paradigm.
Table 8: Quantitative results for complementary conditions.
Mem (GiB)
Time
Canny, Depth
Hed, Depth
Method
SSIMI CLIPt MSE-CJ MSE-D]
SSIM CLIPt MSE-H MSE-D.
EasyControl 40.384 26.043 0.4129 0.2983
0.1345
0.0137
0.3030 0.2977 0.0547
0.0192
Ours
39.573 19.589 0.3735 0.3075 0.1443
0.0140
0.3216 0.3034 0.0575
0.0216
Qualitative Evaluation. We present qualitative comparisons under conflicting conditions in Fig. 11, featuring diverse scenarios including urban scenes, natural landscapes, and character compositions. Both methods demonstrate strong adherence to the given control conditions, with our method exhibiting marginally better preservation of conditional details in complex scenarios. More notably, while EasyControl tends to produce images with an overly smoothed appearance, Cross Pross-ControlNee generates more natural and photorealistic textures, maintaining better overall visual harmony.
These results demonstrate that Conssolell effectively transfers DiT-based architectures like FLUX, achieving a favorable balance between conditional consistency, textual alignment, and visual quality while maintaining superior inference efficiency compared to other mareeng-free alternatives.
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The performance characteristics reflect the different design emphases of each method, with Cross- ControlNet excelling particularly in challenging conflicting scenarios.
yello and pulls down suilit cofficatons airy. Sanllay for rel-naffal fonson yurh.
whoo of sloer tunguatie sucthorms plrving bef fornt, sandyy vuntke
Lantern glovs a vangroe dusk pirk
Wilffower éouque mary stou vastalle foids rsiety norrtr
fall, gelden filtt through fucyy dag.
mussorverr log freat surcharre filee through troes, durpfing fusti graen uedeegrenti
oyea on many Jiguness garden, mupla fine bit gravel parh, traydl brygedd offt aurimun light.
Areti for with wnd für curfed grare mos rundrn, patelies mns, losfilled varer and quie umort day beima.
Takhy and mpd io sumut-fr Japaner valagge street, roofs agfnn, serve stormate
Gaville curved acarie vommms, gohin-ffowr light rolling owr grass and tock, serent cismurs fic..
Gelén wtriner rume josfelly smort, waws yelnting pinkspuryi sty ovar distant cift.
Small smalllprpplle airplan on rucsay duck varn liglt greren fills, long shadaes, uattenal chuuh.
Pannter is jumpuut with palett and frush capoures Taj Mahal's refiction mater garsem at Simm, misy and wrew.
Conditions
EasyControl
Ours
Conditions
EasyControl
Ours
Figure 11: Qualitative comparison between Cross Cooss-ControlNet and EasyControl on FLUX.I-der
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A.10 ADDITIONAL QUALITATIVE RESULTS
To further illustrate the effectiveness of our Cross-ControlNet when applied to the U-Ner-based Stable Diffusion v1.5, we provide additional qualitative samples in Fig. 13, Fig. 14, and Fig. 15. These examples cover a diverse set of wreground-backgrone single-object foregrounds (e.g., an astronaut on the moon, sailing boat on calm lake, lighthouse on a rocky cliff, a cherry blossom tree with Mt. Fuji, a desert cactus, or a canyon hiker), everyday still-life scenes (e.g.. a vintage typewriter or camera on a wooden desk, or a macro glass terrarium), street and architectural compositions (e.g., classic red telephone booth or sunglasses on marble column framing the Parthenon), natural landscapes (e.g., a quiet snowy village or a dawn pasture with shepherd), cultural scenes (e.g., Chinese lion dance), and illustrative artworks (e.g., cartoon dog and cat in minimal line-art or a pastel origami fox). Notably, Fig. 15 presents more complex compositions where foreground and background elements exhibit significant overlap intricate interplay, such as legible text on a sign held by a person in a field; horse behind a fence; a glass bottle fused with forest foliage; and a guitar on log. The gallery spans rendering styles from photorealistic to minimal line-art and painterly oil textures. Across these variations, Cross-Controll preserves the intended conditioning signals (foreground shape and background layout), generates high-fidelit details, and maintains coherent foreground-backgroun separation (see Fig. 13, Fig. 14, and Fig. 15). These supplementary results further corroborate the robustness and versatility of our method beyond the representative cases shown in the main paper.
configurations and artistic styles:
consistently
A.11 BACKGROUND ON CONTROLNET
The design philosophy of ControlNet is simple yet efficient. Its core idea involves introducing trainable parameters corresponding to each control condition and integrating them into the denoising U-Net, thereby enabling precise control over image generation through spatial conditions. To achieve this, ControlNet clones the encoder of the denodising U-Net, passes the features encoded by the cloned encoder through zero-initialized convolutional layers (zero-conv), and then adds the resulting residuals to the skip connections of the original U-Net's decoder. This approach effectively results in a dual-encoder, single-decoder configuration.
Prompt
Time 1
Text
Time
Condition 4G
Encoder
Encoder
zero convolution
Input
Prompt& Time
Encoder
Encoder Block A
64-64
x3
64+64 (trainable copy)
3
SD Encoder Block
SD Encoder Block B
32x32
x3
32x32 (trainable copy)
SD Encoder Block
Eneoder Block C 16=16 (trainable copy)
x3
16x16
SD Encoder Block
SD Encoder Block
x3
8+8 (trainable copy)
SD Middle Block 8-8
SD Middle Black 8-5 (trainable copy)
zero convolution
Block D 8x8
xero convolution
x3
SD Decoder Block 16x16
convolution
SD Decoder Block 32x32
zero convolution
SD Decader Block 61-64
x3
zero convolution
Output ea (R, f, 4, q:)
(a) Stable Diffusion
(b) ControlNet
Figure 12: Overview of ControlNet
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whitt spacesstit standss the surface, Earth behindd
1 cartaen yeloww day and gray cat, simple line art style, minimal style
A vintagr typowriter on wasden desk with bookshelf full of old books in the background, clear typowriter keys
classic rod triephore booth empty coneiestont
stugle satling foot lake; snowy mountatis far fackground
single ligithouus standing on a racky eliff. amam waver the backgrrundd
3 vintage camera placed on wooden disk, blurred bookshelves behindd
cheerry blosown with Mr. Fuii Jutintfy visible the distancee, oil painting texture, abvious brusherokes
Figure 13: More visual results from Cross-ControINet on Stable Diffusion-vl. 1.5 model.
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origami fox pastel, whimsical origami forest with pelygomal drees-dreamy, pranssyyaa
A tall cuctus in the desert foroground
Macro, raflitk glass temarruu on weeden desk fush moss, tiny detatled house, sharp.gesttcc bazktight.
A winter with covered ground and wosden finuse, frostad trees, and pale bluz sky, quiet and bozutiful
shopherdd lantern herds the ficck across ragged, humonocky meadow.
Lion dance Chíinese courtyard, red lanterns, traditiinal reafs, yellow Plaffy blue sky
A hiker on the sage of canyon, river and rugged cliffs stretching into the horizon, warm morning light sky
Black sunglasses weatherea whitz marble catumus Partienon hill under blue sky, soft botch, crisp foreground
Figure 14: More visual results from Cooss-ControlNet on Stable Diffusion-vl.5 model.
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A person bold worn "Cross ControlNie" sign on dirt patí in a grassy field sunset.
Cress CentreINat
Cross- ControlNeet
Gross Cont to INtt
CLoAR Com and Met
Choss Con iivile
31 wiltee horse fetind wstddn fance in meadow, golden sucriie with mist, tries and fitlis in the background, photomafisttc,, warm toties
Clear glass water futtlle withi in forest (frreground)) fush green wondiand (hackground) photorealisti, ditatied.
Vintage accustic guitar on loy (freyroundd) forest campsite campfire, tent (backgroudd) photorenfistic detanied
Open vintage suitcase en rocls in Jorogrownd. Greek cosstal town with bouts at sunser tn buckgrownd, photorealistic
Seagull rools (orgrrundd) calm with ship in backyround, blus sky, plotooeatisticc detatiee.
Giant (forgground) African
(hackgroundl, plotorealistic, détailaa.
Figure 15: More visual results from on Stable Diffusion-vi model.
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A.12 ALGORITHM
Algorithm PixFusion for Cross-Model Feature Fusion
Require: M1 layers {?] 1==1- M2 layers {1] conditions сı, C2, threshold ó, kernels GN GNz 1: for I = 1 to I. do
2: fi + (c)): f2 + (22)
3: p - CosSim (fi - μ1) * Ikzz (f2 - -22 * Ixa)
4:
for all position (j. k) do
5:
plik) 2 then
f(a.k) 1
6:
7:
else
f(in)) + plink) arg max; (yr, +.0)
8:
9: end if
10: end for
11: feed to SD decoder
12:
end for
Algorithm 2 ChannelFusion for Cross-Model Feature Fusion
Require: M2 final-layer features f1, f2, threshold 5 1: fi + (fi);; f2 + (f2) 00.c
for each channel 2: compute R. = 1 max
3: for all channel do
4: if RR > then
5: w; / Ōke
6:
fc - 1-11/1c + w2/2.c
7:
else
8:
fo + fi.e if δ1. > م2 else fz.c
9:
end if
10: end for
11: feed to SD decoder
Algorithm Cross-ControlNee Inference
Require: Foreground model M1, background model M2, masks M Mb Ensure: Denoised latent 2p
1: for = T' to do
2: for = 1 to do
3:
[Q1, K1, V1J (t, =1); {Q2. K2, V23 +- Me(1, a1)
4:
Attn/ < Attention Q2, K1, V1;
5:
Attn <- Attention Q2, [2 @ K1), [V2 @ Vı]: @ MD)
6:
Aitn M Alln! + M Alln
7:
if L then
8:
f + ChannelFusion/ M1, M2; Aftn)
9:
else
10:
<- PixFuusion (M, M2; Aftn)
11:
end if
12: feed f to SD decoder
13: end for
Æt-1 SD(1, 2x)
15:
end for
return 20
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Reviews:
[{"review_id": "gVQQSQdpq1", "reviewer": "Reviewer_MNfM", "summary": "The paper explores training-free methods to combine multiple ControlNet conditions via fusing ControlNet outputs of multiple branches. This is done through either PixFusion or ChannelFusion. They also use KV-Injection, to separate the foreground/background more clearly to avoid conflicting requirements.", "questions": "Clarifications:\n- How are the spatial-level variance maps calculated for PixFusion?\n- For KV-Injection, the terms \"background and foreground ControlNets\" are introduced. Does this mean that KV-Injection only works for combinations of ControlNets that work on foreground/background, and not for two ControlNets that both only focus on the foreground?\n- In equation (9), Q1 is not used. Is this correct?\n- In Figure 3a, the x axis is referred to as the \"depth of the feature layer\", which I am taking to mean the depth of the block in the model, but the text referring to it talks about the dimensionality of the feature space, which does not seem to be the same thing?\n\n\nSuggestions:\n- My understanding for ChannelFusion is that each channel is over all the tokens. Could it not be that the channel would be more useful in some spatial areas than others for each ControlNet, especially if the nets do not overlap?\n- For both PixFusion and ChannelFusion, the threshold is a binary choice between taking only one ControlNet, vs an averaging approach. Could a less hard boundary be used instead, such as an interpolation or sliding scale? Perhaps the lack of a hyperparameter in this way would avoid the threshold degredation?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Training-free method, so quick to test/implement for use\n- Works on any architecture that ControlNet works upon\n- Results show improvement over baselines on SD 1.5", "weaknesses": "- Quantitative results for only SD 1.5, though this may be due to ControlNet restrictions\n- Combining the end product of multiple branches will increase latency, though no values are given as to the cost\n- Could be good to see results without the use of the Gaussian Kernels - how much does this use contribute to the results?\n- The paper assumes knowledge of ControlNet implementation - perhaps some time could be spent on introducing it since the paper relies on it entirely\n  - I found the paper hard to follow in places and so was unsure of the exact methodology used - see questions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "bshVTtpYiA", "reviewer": "Reviewer_jzWz", "summary": "The paper presents Cross-ControlNet, a training-free framework designed for multi-condition text-to-image generation. The framework integrates multiple spatial conditions without retraining, using three main components: PixFusion, ChannelFusion, and KV-Injection. These modules allow for robust feature fusion while handling both conflicting and complementary conditions, with demonstrated improvements in controllable generation. The method improves performance over existing models, notably in generating high-quality images under complex, multi-condition prompts.", "questions": "A more concise explanation of why these components are crucial for multimodal consistency could strengthen the paper's readability.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents an innovative solution to the challenge of multimodal conditional image generation by introducing Cross-ControlNet, which uses a combination of PixFusion, ChannelFusion, and KV-Injection to enhance feature fusion and address the issues of noise sensitivity and high-dimensional fusion degradation. \n2. The framework is compatible with existing models (e.g., DiT-based architectures), demonstrating its generalization potential and ability to adapt to different underlying network backbones.\n3. The experiments are comprehensive, covering both quantitative metrics (e.g., mIoU, MSE) and qualitative results. The method consistently outperforms state-of-the-art methods, such as MaxFusion, Multi-ControlNet, and AnyControl, particularly under conflicting conditions.", "weaknesses": "1. The model’s architecture, which combines multiple ControlNet branches, leads to significant increases in memory consumption and inference time. This makes it challenging to deploy Cross-ControlNet for high-resolution image generation or real-time applications where computational efficiency is critical.\n\n2. While the method works well under conflicting and complementary conditions, extreme conflicting signals may still cause artifacts, such as foreground-background misalignment or color bleeding. The paper could further explore ways to mitigate these extreme cases.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "sAcpqTCl5G", "reviewer": "Reviewer_ajmX", "summary": "The paper proposes Cross-ControlNet, a training-free framework for fusing multiple spatial conditions in text-to-image diffusion models. It introduces three modules—PixFusion (pixel-wise fusion guided by smoothed spatial variance), ChannelFusion (channel-wise adaptive fusion using a consistency ratio), and KV-Injection (foreground–background disentanglement via attention masks), to handle both conflicting and complementary conditions.", "questions": "Please see the weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. PixFusion uses Gaussian-smoothed spatial standard deviation to guide pixel-wise selection, suppressing early-stage noise as validated by Fig. 3b showing cleaner fused variance maps. ChannelFusion and KV-Injection are also resonable modules.\n\n2. Qualitative results in Fig. 4 show faithful preservation of both teddy bear pose and train window structure, where baselines fail to reconcile conflicting cues. Other qualitative results also performs better than other baslines.\n\n3. The framework can transfer to DiT-based FLUX without modification, producing sharp, controllable images, demonstrating architecture agnosticism.", "weaknesses": "1. No inference time or memory usage is reported despite combining multiple ControlNet branches\n\n2. The claim that ChannelFusion is applied only in the final layer (Sec. 4.1) is not justified; no ablation tests layer-wise placement (e.g., middle vs. final layer).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "sSPVuPvBCF", "reviewer": "Reviewer_Tyso", "summary": "This submission introduces a novel method for combining conditioning signal from several ControlNets in a single diffusion model. The proposed approach uses two main observations: that different ControlNets are spatially aligned and that the strengths of individual conditions can be quantified via feature variances, that combined with KV-injection allows for accurate fusion of spatial conditions.", "questions": "-", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- There is a clear introduction to the main idea behind the submission in section 3.1. Presented observations are sound and provide great motivation for the proposed solution.", "weaknesses": "- The usability of the proposed technique is very limited to the specific task of fusing multiple spatial conditions for T2I models\n- The proposed technique is a combination of several “add ons” that build on top of the standard controlnet, this slightly limits the novelty of the proposed method\n- Main experiments are performed with quite old SD 1.5. While the qualitative results with FLUX are impressive, the submission lacks proper comparison with other approaches using this model.\n- The experimental section focus mostly on a setup with clear separation between foreground and background which highly utilizes the KV-injection technique. It would be great to see some results with more complex compositions\n\n\nSmal issue:\n(Presentation) - Lines 60-64 in introduction are copies from the abstract. As I didn’t fully understand what authors meant in the abstract, it was not easier when presented with exactly the same sentence for the second time.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "x7o7K0KzBQ", "reviewer": "Reviewer_y6kF", "summary": "* The paper proposes Cross-ControlNet, a training-free framework for fusing multiple conditional branches in T2I generation.\n* It is built upon two key observations: the spatial alignment across ControlNet branches and the variance-based condition strength.\n* The framework introduces three modules:\n  * PixFusion for pixel-level feature fusion guided by Gaussian-smoothed variance maps,\n  * ChannelFusion for adaptive hard, soft fusion based on channel-wise consistency ratios,\n  * KV-Injection for foreground-background disentanglement using text-derived attention masks.\n* Without any additional training, Cross-ControlNet achieves robust controllable generation under both complementary and conflicting conditions.\n* It outperforms existing training-free methods such as MaxFusion, AnyControl, and Uni-ControlNet, and generalizes to DiT-based models.", "questions": "* How stable is the variance-based fusion under time-dependent noise variations during the diffusion process?\n* While generalization to DiT-based models is promising, how does cross-branch fusion behave in transformer-based architectures?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* The method is training-free, making it practical and computationally efficient for multi-condition control.\n* The variance-guided fusion offers an intuitive yet mathematically grounded mechanism for balancing control strength and spatial coherence.\n* The KV-Injection module elegantly leverages textual attention maps to isolate and refine foreground and background regions.\n* The paper provides comprehensive ablations that demonstrate the contribution of each module.\n* Quantitative results show clear improvements over baselines.", "weaknesses": "* The framework is sensitive to several hyperparameters, but their selection rationale is largely empirical.\n* The approach inherently depends on pre-trained single-condition ControlNets, making it less flexible when new modalities are introduced.\n* Using multiple ControlNet branches may increase inference latency and GPU consumption.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)