Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
Authors: Jiang Qin
Alexandra Gomez-Villa
Senmao Li
Shiqi Yang
Yaxing Wang
Kai Wang
Joost van de Weijer
Venue: NeurIPS 2025 poster ()
Keywords: diffusion models
style transfer
disentangled stylized image generation
Primary area: applications

Abstract: Recent advances in Text-to-Image (T2I) diffusion models have transformed image generation, enabling significant progress in stylized generation using only a few style reference images. However, current diffusion-based methods struggle with \textit{fine-grained} style customization due to challenges in controlling multiple style attributes, such as color and texture. This paper introduces the first tuning-free approach to achieve free-lunch color-texture disentanglement in stylized T2I generation, addressing the need for independently controlled style elements for the Disentangled Stylized Image Generation (DisIG) problem. Our approach leverages the \textit{Image-Prompt Additivity} property in the CLIP image embedding space to develop techniques for separating and extracting Color-Texture Embeddings (CTE) from individual color and texture reference images. To ensure that the color palette of the generated image aligns closely with the color reference, we apply a whitening and coloring transformation to enhance color consistency.  Additionally, to prevent texture loss due to the signal-leak bias inherent in diffusion training, we introduce a noise term that preserves textural fidelity during the Regularized Whitening and Coloring Transformation (RegWCT). Through these methods, our Style Attributes Disentanglement approach (SADis) delivers a more precise and customizable solution for stylized image generation. Experiments on images from the WikiArt and StyleDrop datasets demonstrate that, both qualitatively and quantitatively, SADis surpasses state-of-the-art stylization methods in the DisIG task.

Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
Jiang Qin" Alexandra Gomez-Villa'4" Senmao Li2* Shiqi Yang", Yaxing Wang", Kai Wang$63; , Joost van de Weijer
Harbin Institute of Technology, China VCIP, CS, Nankai University, China Computer Vision Center, Spain Universitat Autònoma de Barcelona, Spain Program of Computer Science, City University of Hong Kong (Dongguan), China CCity University of Hong Kong, HK SAR, China httpss//deepfffff githuubio/sadis.giig .ittuubbbii
Target
'A Lightkouse by the sea"
Targel Content
Figure 1: Stylized images generated by training-free method, SADis. (Up) Example of texture (left) and color (above) conditioned text-to-image (T2I) generation. This approach offers creators enhanced color control, including the use of color palettes as shown the last two columns. (Down) Example with conditioning on content image (left) and color (above), showing it extends to color-only stylized image generation.
Abstract
Recent advances in Text-to-lmage (T2I) diffusion models have transformed image generation, enabling significant progress stylized generation using only few style reference images. However, current diffusion-basee based methods struggle with fine-grained style customization due to challenges in controlling multiple style attributes, such color and texture. This paper introduces the first tuning- free approach to achieve free-lunch color-texture disentanglement in stylized T2I gen- eration, addressing the need for independently controlled style elements for the Disentangled Stylized Image Generation (DisIG) problem. Our approach leverages the Image- Prompt Additivity property in the CLIP image embedding space to de- velop techniques for separating and extracting Color-Texture Embeddings (CTE) from individual color and texture reference images. To ensure that the color palette of the generated image aligns closely with the color reference, we apply a whitening
"Equal contribution.
I Viiiiting researcher in Nankai University. + *Correspodiigg authors: Senmao Li, Kai Wang
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

and coloring transformation to enhance color consistency. Additionally, to prevent texture loss due to the signal-leak bias inherent in diffusion training, we introduce a noise term that preserves textural fidelity during the Regularized Whitening and Coloring Transformation (Reg RT). Through these methods, our Style Attributes Disentanglement approach (SADis) delivers more precise and customizable solu- tion for stylized image generation. Experiments on images from the WikiArt and StyleDrop datasets demonstrate that, both qualitatively and quantitatively, SADis surpasses state-of-the- stylization methods in the DisIG task.
Introduction
Stylized Image Generation ||| 30 36 aims to transfer a style from reference image to target image. This field has evolved through several technological paradigms, beginning with CNN- based feature manipulation (20 65 36], advancing to attention mechanisms 4 75 46 and further developing through GAN-based image translation [37 83 73 A significant advancement came with multimodal CLIP guidance [99 40 51], which bridged the gap between visual and textual representations. This breakthrough enabled a novel approach where style transfer could be guided by textual descriptions rather than being limited to reference images [19 41 [3]. The field underwent
another transformation with the emergence of Text-to-lmage (T2I) diffusion models [56
59 57].
which demonstrated remarkable capabilities in personalized image generation 58
18
48
T2I diffusion models catalyzed new developments in stylized image generation 54
67
62
a
specialized paradigm that focuses creating new images that incorporate specific visual characteris- ties from reference styles, rather than simply transferring style between existing images. Initially, many approaches [18 2] relied on extensive fine-tuning using datasets of similarly styled images. However, this requirement proved impractical in real-world scenarios, where collecting cohesive style-specific datasets is often challenging. Addressing these limitations, recent research has focused on developing tuning-free (free- methods (76] 54 38). These approaches eliminate the need for costly retraining while maintaining efficient style integration capabilities. Despite their method- ological differences, both tuning-based and tuning-fres free approaches share a common characteristic: they transfer entangled style representations during the image generation process, simultaneously incorporating both color and texture elements from the style images into the final output.
Although existing stylized image generation methods offer promising advancements, for content creators, these methods have critical limitations. A key challenge is the lack of granular control over style elements. Content creators often need to adopt specific aspects of a reference style while preserving elements of their original vision 13 (27). Color palettes, in particular, play a crucial role in this process they are often meticulously crafted to evoke specific emotions maintain brand consistency. Creators may wish to preserve these while adopting rexturaf]elemer from other reference styles. However, current approaches force all-or-nothing choice, where accepting reference style means incorporating all its visual (texture and color) characteristics simultaneously. seriously limiting artistic freedom and practical applicability.
To address these limitations, we introduce the problem of Disentangled Stylized Image Generation (DisIG), which aims to decompose reference styles into independently controllable attributes like color, texture, and semantic content. This formulation enables artists to selectively transfer spe- cific style elements while maintaining control over the content using text prompts. One potential workaround for DisIG using existing methods is to specify desired style attributes through text prompts. However, this approach requires extensive prompt engineering (47 77 72] expertise and considerable aridl-and-error to achieve results that can be obtained more intuitively through image prompts. Even with significant effort in crafting precise textual deseriptions, text prompts often fall short of capturing the nuanced characteristics of the target style. This limitation stems from the inherent expressiveness gap between text and visual information compared with text prompts, image prompts inherently contain more fine-grained semantic information that is difficult to describe
'In this paper, we use the term texture to refer to the arrangement, repetition, and local patterns of visual elements such as shapes, edges, intensities, and gradients and excluding color aspects.

---

accurately in text, including style attributes such as color and texture. Such nuanced details contribute
to improved generation quality (as shown in Fig. 2 2nd row.).
In this work, we propose what we believe to be the first DisIG method that enables inde- pendent control of color and texture through separate reference images, without any training. Such application and performance can be seen from Fig. I To achieve this, we first analyze the addirivity property
InstantStyle
DF ADim
Lecture
a characteristic previ- and
ously studied in textual spaces 6]3] demonstrate that it also holds true in the image prompt space. Then, we work with each style component separately: we isolate the color rep- resentation through feature subtraction between the original color image and its grayscale equiv- alent, while deriving the texture representation using grayscale version of the texture reference image and SVD rescaling. Finally, to ensure ac- curate color matching while preserving textures, we introduce Reg WCT, an enhanced whitening and coloring transform with noise regularization, which counteracts the signal-leak bias [6. 80) in diffusion models.
SADIs (Chrs)
Proege isttriee
Figure 2: Compared to SOTA stylization meth- ods, SADis enables more precise and fine-grained control of color and texture by leveraging style element images, which allows for more accurate specification of visual attributes
In experiments, we use images from the WikiArt [64] and StyleDrop 662 datasets and evaluate our approach based on the SDXL model [53]. By comparing against several state-of-the-art stylization methods, SADis consistently outperforms these baselines over both qualitative and quantitative results, particularly in achieving accurate color and texture expression for the stylized T21 generation. In summary, our contributions are as follows:
We introduce the Disentangled Stylized Image Generation (DisIG) problem, critical challenge in real-world applications that enables richer semantic control diffusion-based stylization.
We are the first to identify and use the image prrompt additivity property in the CLIP image encoder, showing how image features can be decomposed and combined similarly to text embeddings.
We present the first tuning-free (free-lunch) style attribute disentanglement method, SADis, em- phasizing color and texture as key style attributes. Our color-texture extraction (CTE) and reg- ularized mhitening-coloring transformation (RegWCT) techniques enable effective disentangled color-texture stylization without additional tuning.
Through thorough qualitative and quantitative evaluations in standard style transfer benchmarks, SADis consistently outperforms existing baselines in realistic generation under the DisIG scenario.
2
Related Work
Stylized image generation (30 |1 37 83,, also related to classical style transfer, aims to generate
images with the artistic style of reference image. Early research focused on statistical feature manipulation to transfer artistic styles from reference images. Gatys et al. (20] pioneered this direction by using covariance matrices for style representation. Following works like AdaIN (36) improved efficiency by transferring feature statistics between style and content feature maps, while WCT 451 introduced whitening and coloring transformations match covariance matrices. Recent architectural innovations have enhanced stylization capabilities.
A significant paradigm shift occurred with the introduction of CLIP 55
enabling connections between text and image representations in a shared embedding space. This advancement spawned methods like ClipStyler [41] to combine global and patch-level CLIP losses for image stylization.
'Here we use the prompt "A ballerina" as an example. The prompt used in InstantStyle and DEADiff is detailed description of the color reference image generated by GPT-4o [
3

---

Sec 3.3.2
Sec 3.3.
photo of tird
27
C1.1P
Iinge Uncocer
Original Enbeldiing
U-Not
Embaar
Color Image
Onyscale Embosking
Color Extraction
Ensaee Liceddr
2q
HUEV
UUVV
EA.3
Texture Image
Toxture Extraction
Emb
2 Zo
Crepsoale
Enteckling
Ershelling
Misigee
Figure Our method, SADiS, begins with color-texture extraction (CTE), leveraging the Image- Prompt Additivity property, which is verified in this paper for the first time. The color embedding Emb is obtained by exploiting the Image-Prompt Additivity property, while the texture embedding Emb is extracted via singular value decomposition (SVD) operation. Afterwards, we incorporate these embeddings into the style cross-attention layer of the SIXL. model. Subsequently, we refine the latent variable 2t-1 at each inference step with proposed RegWCT transformation, aligning color palettes precisely while retaining essential texture details.
Nowadays, T21 Diffusion models [6l 29 8 have emerged as the new state-of-the-ard models for text-
to-image generation. Then the existing T2I stylization methods [2 60 12
achieve stylized image generation via fine-ture tinettunin generative models on few style reference images. However, this process gathering a suitable subset of shared-stye style images can be difficult. To address these limitations, interest
consuming and struggles to generalize to real- world scenarios where
wning-ffree (free-lunch) approaches for stylized image generation has grown T0 15
26 24
These methods introduce lightweight adapters that extract style information from reference images and inject into the diffusion process via self-attention or cross-attention layers. Representative examples include IP-Atepter (76) and Style-Adaple [71], which employ decoupled cross-attention mechanism that separates cross-attention layers for handling text and image features independently. DEADiff [54] introduces different approach by focusing extracting disentangled representations of content and style using paired datasets.
Although existing stylization methods show promise, they lack granular control over individual style elements crucial need for content creators who often want to selectively adopt specific aspects of reference styles. This introduces challenge we term the Disentangled Stylized Image Generation (DisIG). Focusing on color and texture as the most significant style attributes, this paper introduces the first free-lunch approach to color-texture disentanglement for stylized image generation.
3 Method
3.1 Preliminaries
T2I Diffusion Models. We built on the SDXL 53
model, consisting of two primary components: an autoencoder and diffusion model co(2,, (P)),, where co is UNet, conditioning latent input 2,, a timestep t ~ U1,, and text embedding T3 (P). More specifically, text-guided diffusion models generate an image from the textual condition as Crest (P()), where Tę the CLIP text encoder [55 The cross-attention map is derived from cn t, C.eat). After predicting the noise, 663 49 are used to predict the latent which we simplify as 2t-1 = ((2,, 1, Crext).
diffusion schedulers
Ad-ter Building on T2I diffusion models, the IP-Adapter 766 introduces additional controlla- bility by conditioning the T2I model on conditional image Zip- Practically, this involves leveraging a pre-trained T2I diffusion model and incorporating a cross-attention layer to the (projected) image condition following each pexm-prompt conditioning layer. The conditional image is encoded in the low-dimensional CL.IP image embedding space [55] to capture high-level semantic information. By denoting the CLIP image encoder TΦ and IP-Adapter projection as IP, this process is adding new image condition Cimg = IP ( (I.)) to the T2I model as 2 = g(z,, t, Crezt Comg).
'SDXL text encoders and concatenate the embeddings.

---

a)
b)
Color Grayscale
Ral inegas
6
Genestl bid
CLIP Image Eneeder
Goyscll Irages
2-
TP-AAapter
SpX
+B Byy
Protue
B"
"A photo of bird!
Yolf"
-36
Figure 4: a) Image-prompt additivity: Image encoder latent space enables color extraction via grayscale subtraction. This color information can then be added latent representations of other grey- scale images; b) By subtracting the grayscale image embeddings from the color image embeddings, we can effectively remove the texture information and isolate the color information.
3.2 Disentangled Stylized Image Generation
In typical artistic workflow, creators draw inspiration from multiple sources produce their final results. They might select color palette from a sunset photograph, adopt textural patterns from baroque painting, and incorporate structural elements from architectural blueprints- all while maintaining precise control over which elements they adopt from each reference. We term this ideal multi-target style transfer problem as Disentangled Stylized Image Generation (DisIG).
This multi-source approach to artistic creation (DisIG) contrast to current computational stylization methods 45
g 40), which typically extract and apply bundled representation of "style" from single reference image. To address the limitation, we first analyze how color and texture are entangled in existing stylization approaches. We focus specifically on these two attributes because they represent fundamental, measurable components of visual style that artists frequently manipulate independently.
Problem Setup. We formally define the DisIG task as follows: Given a text prompt P, a color reference image Ietr, and a texture reference image Iua goal is to generate image that semantically aligns with P while independently adopting the color distribution of Zer and the textural characteristics of Itz- The color reference Iatr be either a standard RGB image discrete color palette. Unlike traditional style transfer that applies a holistic "style" from a single reference, formulation enables precise, titribute-speciff control by explicitly disentangling and separately transferring color and texture components.
3.3 Style Attributes Disentanglement
Here, we present our Style Attributes Disentanglement method (SADis). An overview is provided in Fig. 3 To successfully extract the color and texture information, we exploit the image-prompt additivity property as Sec. 3.3.1 Next, we explain the color-texture extraction (CTE) in Sec. 3.3.2
and the noise-regularized whitening-colone transformation (Reg is detailed in Sec. 3.3.3
3.3.1 Image-Prompt Additivity
To achieve disentangled stylization, first analyze the additivity property of image prompts. This property is the main insight on which our color-texture disentanglement based. It is illustrated using the CLIP image encoder, as adopted in the IP- IP-Adapter (76). Note that this property has been explored text prompt spaces [50 6
here we show that it also holds in the image embedding space.
Consider the image embedding space as shown in Fig. (a). Here we show a PCA plot of the embedding space for distribution of images generated with three different text prompts. The embedding of color image contains all information of the image (see e.g. points A and B). If we compare this embedding with the embedding of grey-scale version (4', B'), the difference between the two embeddings is due to the removal of color information. We here hypothesize that
5

---

Auggl An Ap
conngreun oesoen Aetine
ReghnEY wentgy
Figure 5: We extract texture information and suppress gray expression through SVD reweighting, and color alignment is improved by RegWCT. 'Baseline' denotes color-texture disentanglement with Image-Prompt A Additivity only (i.e., without SVD and Reg WC7). (a) shows the Roles of SVD and RegWCT within SADis. (b) Our approach enables more precise color alignment with Tatr via RegWCT, whereas direct WCT manipulation compresses texture details and high-fre frghffreyeenyy components. (c) Reg WCT rectifies the latent distribution that is affected by WCT. The distribution of the image latent generated with Reg RCT technique is closer to that of the image generated without WCT in the frequency domain, across diverse time steps.
adding this difference to the embedding of another grey image, would transfer the color information to this image. This illustrated for the figure of the grey bird image C. We can also see that the differences of A A' and B B' form relatively compact distribution.
3.3.2 Texture Extraction
Leveraging this image-prompt additivity, we disentangle color and texture representations from their respective reference images. For color extraction, take the color image embedding and subtract the grayscale image embedding:
Emb = Tá (Tetr) + Tip (Ga())r))
where GS is the grayscale operation, Emb E Rx This strips away semantic information to retain only the color attributes (See b)
For texture extraction, we first convert the texture reference image to grayscale remove any influence of its color palette, which is formulated as Emb;, To (GS(( Emb;, R However, using only the grayscale texture embedding creates mismatch scale between the color and texture branches, often resulting in overly gray tones in T2I generations (as shown in Fig. 5 (a)-2nd col.). To address this, we average the gray texture image to obtain pure gray image, which summarizes the main gray tone information in the GS(Izz) image. Following that, we concatenate both embeddings from bosen-wise to obtain the initial texture representations:
Emb Emb C To (Avg (rS(()))(I.))
Emb;.. E 2 Next, we conduct the Singular- Value Decomposition (SVD) over the texture Value
representations. Inspired by 21 [44), we assume that the main singular values of Emb;,, correspond to the shared fundamental information of these two grayscale images, specifically the grayscale tone. We then have: Emb tzr UEVT, where
diag (To: 01: . 2n) the singular To suppress the gray tone expression and extract the texture information,
values do 2 ... 2 2 we introduce the augmentation for each singular value as:
θ = Be O.
(3)
where e is the exponential, 7 and 3 are parameters with positive numbers. We then recover it as Embre = UÊVT, with the updated Σ = diag(do o.. -1). The texture embedding is obtained by Emb;e Embree: : ne, :]. That effectively reduces residual gray-color influences while preserving texture details. After extracting both color and texture reference image embeddings, we achieve a baseline of the DisIG problem. The T2I inference process now formulated as:
2 = G(z,, L, L,eezt, Emb @ Embar). Note that, we only inject the disentangled embedding Emb @ Emb,a into the first decoder layer to compute cross-attention maps, which shows better Examples shown Fig. (2nd
stylization performance as previous works proved [67 68 66
cols.) demonstrate the effectiveness of our proposal.

---

3.3.3 Regularized Whitening-Coloring Transforms
WCT: whitening-coloring transformation. Solely applying CTE method does not fully ensure a precise color palette match between the reference color image Zetr and the generated output (see Fi We hypothesize that while CLIP embeddings capture high-level semantic information, they may not fully represent nuanced color distribution details. To better align the color distributions of the generated image with the color reference, we incorporate whitening-colonine
transform applied to the noisy latent z (=) (detailed WCT formulas in the Supplementary). This approach allows for more faithful color transfer by aligning the latent representations with the reference color palette, enhancing the stylistic consistency of generated images. Examples of applying the WCT transform during the T2I generation process are presented Fig. (b), demonstrating an improved alignment of color palettes between the generated images and the reference color images. This enhancement visually confirms the effectiveness of WCT in achieving more accurate color transfer, resulting closer match to the desired color distribution.
(WCT) 45 g 31
Noise Regularization for WCT. However, shown in Fig. (b). the WCT process tends to compress texture details, distorting high-frequend information, which is also observed in previous works [80 42. This issue is attributed to the signal-leak bias [16] and the inverted latent distribution gap from the inversion process [80]. These terms essentially describe the same underlying phenomenon: during training. the distribution of 2--the latent representation after adding Gaussian noise to 20 does not perfectly match a standard Gaussian. Instead, it always contains the leakage or some certain prior information towards the original image, causing the residual structure to persist even high noise levels. This biased noise in the forward diffusion process leads to a slight misalignment in texture representation during generation. To address this, we propose adding a small-scale noise to the latent as 2 2t uec + X 5, ~ (0, 1). determined by a scale hyperparameter X, to recover the lost high-frequency details. By introducing this noise regularization term, the regularized whitening- coloring transformation (Reg WCT) achieves improved generative performance, better aligning both color and texture in the generated images with the reference images. Given a latent 2, at timestep t, the color rectified latent 2, after Reg WCT is formulated as 2 (1 (1-aa w) 2 + w Reg RegWCT(E)) (4)). where w acts as a balance weight. To prevent the inference trajectory from deviating too much from the original one, we only perform the Reg WCT transformation during the intermediate steps as [T ] With all these techniques, including color-texture extraction (C7E) and Regularized WCT transformation (CT), our method SADis achieves customizable and flexible Disentangled Stylized Image Generation (as shown in Fig. (a)-4th col.).
3.3.4 ControlNet-basedd real image stylization
Our method can be integrated with ControlNet (79] CA to facilitate content-based stylized image generation, as illustrated in Fig. (down) and Fig. (a). By using any pretrained ControlNet model (e.g. Canny-conditioned) as the base pipeline while maintaining all other hyperparameters consistent with SADis, are able significantly broaden the applicability of our method. More specifically, we have an input I, as the content image, is passed through the ControlNet C.((I.) as conditions for T2I generation model as 24-1 = Giz,, t, CL,())), Emb,, + Emb where 2T NN 1) and the textual prompt as null -07
4 Experiments
4.1 Experimental Setups
Datasets. To ensure fair comparison, we randomly select 40 images total from the WikiArt 64 and StyleDrop (62 datasets. For our method, SADis, each of these images serves as either a color reference or texture reference to enable color-texture disentanglement. In contrast, since the comparison methods lack disentanglement capabilities, we treat each of these 40 images as a style reference for these methods, supplemented by auxiliary text prompts generated from GPT-4o [ as a strong captioning model. For quantitative comparisons, we used 20 images as the color reference set and 20 as the texture reference set. We also sampled 10 content prompts from StyleDrop, resulting in 4,000 stylized images per method to ensure fair and extensive comparisons with numerous images.
7

---

SEALE
Sest
Decandolrr
Arsier
DEADvr
CIGO
A AS
Figure 6: Disentangled Stylized Image Generation (DisIG) compared with baseline methods. For other approaches, we used GPT-4o to generate color descriptions based on the color reference image, incorporating these descriptions into the text prompts (details shown in the Supplementary). Our SADis accepts separate color and texture reference images to achieve flexible control.
Evaluation Metrics. We evaluate our method using multiple quantitative metrics. (1) For general quality evaluation, we use CLIP-Score (CLIP) (28) to evaluate the T2I generation performance, specifically measuring the semantic consistency between the generated image and the text prompt. (2) For the evaluation of color attribute alignment, the MS-SWD 25 metric specifically evaluates color distance between generated and reference images. Color histogram distance (C-Hist) computes the distance between both color histograms. The GPT-4o color score [ requests the GPT model to rate from 0-5 according to the color consistency between the color reference and the generated image. (3) For the texture quality, the Kernel Inception Distance (KID) [5] is assessing the quality of generated images by measuring the dissimilarity between the real and generated image distributions (We use feature dimension as 2048 and evaluate on the 4000 images). CLIPPII used to evaluate the similarity between the texture image and the generated image in grayscale.
Implementation Details. We build our method SADis upon the SDXL (53 To apply the image embeddings as additional conditions the T2I model, use the IP-Adapter [76] pretrained projectors. Both of these models are based on the CLIP model (55), where the SDXL model utilizes the CLIP text encoder generate textual embeddings and the IP-Adapter leverages the CLIP image encoder to extract image embeddings before the embedding projector. For the hyperparameters, we set A 0.01, I start 0.8T,, Tond 0.6T, w 0.5, 7 0.003, 1.0. For the time cost, we reported for 50-step DDIM. All the experiments are conducted on single L40s GPU.
Comparison Methods. We compare with several -fate-of-thheart tuning-free stylization approaches based the T2I diffusion models: DEADiff [54). InstantStyle (67], IP-Adapter (76), CSGO (74), DreamStyler 33. StyleDrop [62] and Artist (39 For these baseline methods, we utilize the GPT- 4o ① model to generate captions from the color reference image, which are appended to the original textual prompt to guide the T21 generation. Additionally, SDXL (53] and Artist model are included in comparisons by generating captions from both the color and texture reference images, appending these captions to the textual prompts to guide the generation process. We include much more experimental setup details, qualitative and quantitative results in the Suppimentary Material.
4.2 Experimental Results
Qualitative Comparison. In our comparison of generation quality, visual results under the Disentan- gled Stylized Image Generation (DisIG) scenario are shown in Fig. [ and Fig. 7 While baseline methods manage to capture some style information from the style reference image (the texture image for SADis), they fail to represent the color palette provided by the color reference accurately. The IP-Adapter and InstantStyle methods even retain original layouts and elements from the style images, deviating from the intended stylization. Other baselines, such as Artist (3 and DEADiff (54), exhibit poor generation quality, reflecting a limited understanding of both color and texture attributes. In contrast, our method, SADis, allows for both texture and color reference inputs, offering precise, con-

---

Table Quantitative Comparison with existing image stylization methods. The best and second-best
numbers are marked with bold and underlined respectively.
Culor C-Histl
Texture
User study (2)
Method
CLIP
Time Cost (s)
GPT4et
CLIP-II
KIDJ
Colort
Texture
Both
SDXL
0.272
9.51
1.30
3.01
0.69
0.08
16.99
5.84
11.63
IPAdapter 776
0.233
11:54
1.23
2.84
0.84
0,043
9.52
6,08
22.54
14.35
InstantStyle 67
0.261
12.53
1.32
2,80
0.74
0.056
9.31
4.81
24.94
13.10
Artist (39
0.269
10.48
1.39
2.82
0.69
0.089
12.32
9.38
1.79
3.40
DEADiff [4
0.267
11.20
1.24
2.73
0.69
0.087
1.86
4.57
2.59
2.67
StyleDrop
0.275
13.52
2.67
0.70
0.054
5.07
5.34
DreamStyler 3
0.277
12.17
1.26
2.39
0.71
0.060
5.23
4.67
3.57
5.39
CSGO 74
0,280
14.25
1.36
2.63
0.69
0.071
15.99
6.59
9.73
6.06
SADis (Ours)
0.281
5.37
0.96
3.34
0.74
10.30
41.83
25.42
37.84
Table 2: Ablation by removing each components in our method SADís.
Color
Texture CLIP-It
Method
Time cost (s)
MS-SWDL
C-Hist.
SADis (Ours)
5.57
0.96
0.74
z 10.30
SVD
5.70
1.01
0.76
2 10.29
RegWCT
8.05
1.06
0.75
2 9.42
SVD RegWCT
8.93
1.10
0.76
9.41
trollable T2I stylization. The generated images by SADis exhibit significantly improved color fidelity and texture detail compared to other approaches, demonstrating its effectiveness disentangled style attribute transfer. To further validate SADis's robustness, we conduct two tests: (1) swapping the color and texture reference images as shown in Fig. (c). and (2) modifying their saturation and illumination levels, as shown in Fig. 7 (d). Unlike existing approaches such as InstantStyle, which cannot preserve image content under these conditions (detailed in Supplementary), SADis successfully applies color and texture independently while keeping the content largely unchanged.
Quantitative Comparison. A detailed quantitative comparison is provided in Tab. to further support our findings. Our method, SADis, retains text-image alignment quality at level comparable the base SDXL model [53], as indicated by the CLIP score. This comparison demonstrates that SADis preserves alignment with the textual prompt better than other methods. Regarding color alignment, SADís significantly outperforms other approaches. For texture representation, the IP-Adapter- based methods, including InstantStyle [67] and SADis, show superior performance over other baselines. They achieve higher texture-related metrics at the expense of color fidelity and text-image alignment quality. The high texture scores for IP-Adapter can be explained by the fact that they control all cross-attention layers; However, this leads to high-semantic content leakage (like the same stars in the Van Gogh bear in Fig. (5). We decided to prevent this by only controlling the cross-attention of the low-level layers following InstantStyle [67 2 66); Therefore, our texture results are close to those of InstantStyle, but at the same time we greatly improve color fidelity. In addition, their generated images follow the same structure as the original texture image, showing that they do not disentangle structure from texture but overfit to the contents of the texture images.
A Ablation Study. The ablation study for each component of SADis is listed in Tab 2 Results show that SVD rescaling in the CTE process and the noise-regularized Reg RCT techniques significantly enhance color alignment while only slightly reducing texture precision, which is almost undetectable in the CLIP-I metrie. The offee-off between texture and color alignment is optional for users, allowing them to adjust the balance based on the requirements.
User Study. To better assess alignment with human preferences, as shown in Tab. rright), conducted a user study with 24 participants (30 sextuplets/user), collecting 720 data for each method. Each was asked to "select the best image from pairs of generated images, taking into account overall quality (considering both color and texture alignments). color alignment, and texture alignment, respectively." Our method outperformed other baseline approaches with at least a 20% improvement. This demonstrates the potency of SADis and its high alignment with human preference.
4.3 Additional Features
Image-based stylization. SADis Integrated with ControlNet achieves image-based stylized gen- eration, as shown Fig. (a). Here, we adopt ControlNet (Canny) as the base model, setting its conditioning scale to 0.6. This integration broadens the application scenarios of SADis.

---

Coboradd baadel
Figure 7: (a) SADís allows for real-image stylization. (b) SADís effectively disentangles color and material elements from separate images, enabling precise control over color and material in image generation. (c) SADis remains effective when exchanging texture and color images. (d) SADis can capture color image properties such as saturation and illuminant. This further demonstrates the
robustness of SADis in realistic DisIG scenario.
Color
Texture
Prompt: A depieston of disanlvi into flock of
Prospt:
glant: phawei soari the Sts, fwethers achoing the colors of the sunset:
bubtarflien. she walks through mystical forest.
(o) FLUX.I-dev
(b) SD3.5
Figure 8: SADis compatible with MMDiT-based models. (a) demonstrates color-texture disentan- gled stylization results on FLUX.I-dew, while (b) shows the corresponding results on SD3.5.
Color and material transfer. Given color and material references from separate images, SADis is also capable of disentangling color and material elements, and provides precise controls over color and materials in T21 generation. The color and material transfer results are shown in Fig. (b). This capability of SADis highlights its potential applications in artistic creation and industrial design.
Compatible with MMDeT-based models. SADis is also compatible with MMDiT-based models (SD3.5 [4] and FLUX.I-deW to achieve color-texture disentangled stylization. Specifically, we use SD3.5 and FLUX.1-de as the base models. These models employ the original text encoders (i.e.. T5, CLIP-L, and CLIP-G) for content control. We utilize SigLIP 78 with a pretrained adapte FPp for style control. Color-texture embeddings are extracted and disentangled from SigLIP features, following the same workflow as illustrated in Fig. B As shown in Fig. 88 SADis achieves color- oolorteexture disentanglement and enables stylized image generation these MMDiT-based models well. This demonstrates that the color-texture disentanglement capability of SADis does not rely on the U-Net architecture and be generalized MMDiT-based models for stylized image generation. It also indicates that SADis is not limited to specific CLIP models and can be applied to other text-image pretrained encoders.
5 Conclusion
We addressed a critical challenge in stylized image generation by introducing the concept of disen- tangled stylized image generation (DisIG). We focused on color and texture as key style attributes. presenting the first approach, named style attribute disentanglement (SADis). for independent control over these elements. Using the image-prompt additivity property, we proposed novel techniques, including the color-texture extraction (CTE) and regularized whitening-colorite (RegWCT) to ensure enhanced color-texture consistency and more accurate results. Experimental evaluations demonstrate that SADis significantly outperforms existing stylization methods, both qual- itatively and quantitatively. This work also opens new avenues for more flexible and customizable image generation, paving the way for future innovations for art creators.
transformation
http:://wagginngnececooohheeeeee -Pdpter.
1edv..
http://hwwggggaaccc.oo/////sasaa hatp:://e.........././///aaaaa.. Adaptee.
10

---

Acknowledgements
We acknowledge project PID2022-143257NB-I0 financed by MCIN/AEE//O.. 13039/501 13033///0/000.339/500010 and ERDF/EU, and the Generalitat de Catalunya CERCA Program, and ELLIOT project funded by the European Union under Grant Agreement 101214398. This work was also supported by NSFC (NO. 62225604) and Youth Foundation (62202243). We acknowledge "Science and Technology Yongjiang 2035" key technology breakthrough plan project and Chinese government-guie guided local science and technology development fund projects (scientific and technological achievement transfer and transformation projects) (25470102G). Kai Wang acknowledges the funding from Guangdong and Hong Kong Universities 1+1+1 Joint Research Collaboration Scheme and the start-up grant 901040000108 from CityU-DG.
10001 1033
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. 2023.
[2] Aishwarya Agarwal, Srikrishna Karanam, Tripti Shukla, and Balaji Vasan Srinivasan. An image is worth multiple words: Multi-attribute inversion for constrained text-to-image synthesis. International Conference Machine Learning, 2024.
[3] Namhyuk Ahn, Junsoo Lee, Chunggi Lee, Kunhee Kim, Daesik Kim, Seung- Hun Nam, and Kibeom Hong. Dreamstyler: Paint by style inversion with text-to-image diffusion models. Proceedings of the Conference on Artificial Intelligence. 2024.
[4] Yuval Alaluf, Daniel Garibi, Or Patashnik, Hadar Averbuch- and Daniel Cohen-Or. Cross- image attention for zero-shot appearance transfer. In ACM SIGGRAPH 2024 Conference Papers, pages 1-12, 2024.
[5] Mikolaj Binkowski, Dougal Sutherland, Michael Arbel, and Arthur Gretton. Demystifying MMD GANs. In International Conference on Learning Representations, 2018.
[6] Manuel Brack, Felix Friedrich, Dominik Hintersdorf, Lukas Struppek, Patrick Schramowski, and Kristian Kersting. Sega: Instructing text-to-image models using semantic guidance. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, editors, Advances in Neural Information Processing Systems, volume 36, pages 25365-25389. Curran Associates, Inc., 2023.
[7] Muhammad Atif Butt, Kai Wang. Javier Sizquez-Corral and Joost van de Weijer. Colorpeel: Color prompt learning with diffusion models via color and shape disentanglement. In European Conference on Computer Vision, pages 456-472. Springer, 2025.
[8] Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-o: Fast training of diffusion transformer for photorealistic text-to to-imageemeeeeoe synthesis, 2023.
[9] Wonwoong Cho, Sungha Choi, David Keetae Park, Inkyu Shin, and Jaegul Choo. Image-to- image translation via group-wise -iise deep whitening-amd and- and-coloring transformation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 10639-106477, 2019.
[10] Jiwoo Chung, Sangeek Hyun, and Jae-Pil Heo. Style injection diffusion: A training free approach for adapting large-scale diffusion models for style transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8795-8805, 2024.
[11) Yingying Deng, Fan Tang, Weiming Dong, Chongyang Ma, Xingjia Pan, Lei Wang, and Changsheng Xu. Stytr2: Image style transfer with transformers. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pages 11326-11336. 2022.
[12) Ziyi Dong, Pengxu Wei, and Liang Lin. Dreamartist: Towards controllable one-shot text-to- image generation via contrastive prompt-tuning. arXiv preprint arXiv:2211.11333 2022.
11

---

[13] Ziv Epstein, Aaron Hertzmann, Investigators of Human Creativity, Memo Akten, Hany Farid, Jessica Fjeld. Morgan R Frank, Matthew Groh, Laura Herman, Neil Leach, al. Art and the science of generative ai. Science, 380(6650): 1 11011111, 2023.
[14] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow trans- formers for high-resolution image synthesis. In Forty-first international conference on machine learning, 2024.
[15] Martin Nicolas Everaert, Marco Bocchio, Sami Arpa, Sabine Süsstrunk, and Radhakrishna Achanta. Diffusion in Style. In Proceedings of the IEEE/CVE International Conference on Computer Vision (ICCV), pages 2251 -2611, October 2023.
[16] Martin Nicolas Everaert, Athanasios Fitsios, Marco Bocchio, Sami Arpa, Sabine Süsstrunk, and Radhakrishna Achanta. Exploiting the signal-leak bias in diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages
2024.
[17] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Patashnik, Amit Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022.
[18] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. image is worth one word: Personalizing text-to-image generation using textual inversion. International Conference Learning Representations, 2023.
[19] Rinon Gal, Or Patashnik, Haggai Maron, Amit Bermano, Gal Chechik, and Daniel Cohen-Or. Stylegan-nada: Clip-guided domain adaptation of image generators. ACM Transactions Graphics (TOG), 41(4):1-13, 2022.
[20] Leon A Gatys, Alexander S Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2414-2423, 2016.
[21] Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng. Weighted nuclear norm min- imization with application to image denoising. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 2862-2869, 2014.
[22] Inhwa Han, Serin Yang, Taesung Kwon, and Jong Chul Ye. Highly personalized text embedding for image manipulation by stable diffusion. arXiv preprint arXiv:2303.085 087677 2023.
[23] Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang. Svdiff: Compact parameter space for diffusion fine-tuning. Proceedings of the International Conference Computer Vision, 2023.
[24] Feihong He, Gang Li, Mengyuan Zhang, Leilei Yan, Lingyu Si, Fanzhang Li, and Li Shen. Freestyle: Free lunch for text-guided style transfer using diffusion models. arXiv preprint arXiv:2401. 15636, 2024.
[25] Jiaqi He, Zhihua Wang, Leon Wang, Tsein-I Liu, Yuming Fang, Qilin Sun, and Kede Ma. Multiscale sliced wasserstein distances as perceptual color difference measures. European Conference Computer Vision, 2024.
[26] Amir Hertz, Andrey Voynov, Shlomi Fruchter, and Daniel Cohen-Or. Style aligned image generation via shared attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4775-4785, June 2024.
[27] Aaron Hertzmann. Toward modeling creative processes for algorithmic painting. arXiv preprint arXiv:2205.01605 2022.
[28] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7514-7528, 2021.
12

---

[29] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao. Alexey Gritsenko, Diederik P Kingma, Ben Poole. Mohammad Norouzi, David J Fleet, al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:221000230:3 2022.
[30] Kibeom Hong, Seogkyu Jeon, Junsoo Lee, Namhyuk Ahn, Kunhee Kim, Pilhyeon Lee, Daesik Kim, Youngjung Uh, and Hyeran Byun. Aespa-net: Aesthetic pattern-aware style transfer networks. In Proceedings of the IEEE/CVF International Conference on Compuler Vision, pages 22758-22767, 2023.
[31] Maliha Hossain. Whitening and coloring transformations for multivariate gaussian data. A slecture partly based on the ECE662 Spring, 2014.
[32] Miliha Hossain. Whitening and coloring transforms for multivariate gaussian random variables. Project Rhea, 3:1-7, 2016.
[33] Taihang Hu, Linxuan Li, Joost van de Weijer, Hongcheng Gao, Fahad Shahbaz Khan, Jian Yang. Mingming Cheng, Kai Wang, and Yaxing Wang. Token merging for training-free semantic binding in text-to-image synthesis. In Advances in Neural Information Processing Systems, 2024.
[34] Kaiyi Huang. Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems, 2023.
[35] Nisha Huang, Yuxin Zhang, Fan Tang, Chongyang Ma, Haibin Huang, Weiming Dong, and Changsheng Xu. Diffstyler: Controllable dual diffusion for text-driven image stylization. IEEE Transactions on Neural Networks and Learning Systems, 2024.
[36] Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE international conference computer vision, pages 1501-1510, 2017.
[37] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-imane translation with conditional adversarial networks. In Proceedings the IEEE conference on computer vision and pattern recognition, pages 1125-1134, 2017.
[38] Jaeseok Jeong, Junho Kim, Yunjey Choi, Gayoung Lee, and Youngjung Uh. Visual style prompting with swapping self-attention. arXiv preprint arXiv:2402.12977 12974, 2024.
[39] Ruixiang Jiang and Changwen Chen, Artist: Aesthetically controllable text-driven stylization without training. arXiv preprint M.Xiv:2407..18422 2024.
[40] Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. Diffusionclip: Text-guided image manipulation using diffusion models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2022.
[41] Gihyun Kwon and Jong Chul Ye. Clipstyler: Image style transfer with single text condition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2022.
[42] Senmao Li, Taihang Hu, Fahad Shahbaz Khan, Linxuan Li, Shiqi Yang, Yaxing Wang. Ming- Ming Cheng, and Jian Yang. Faster diffusion: Rethinking the role of unet encoder in diffusion models, 2023.
[43] Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, and Jian Yang. Stylediffusion: Prompt-ombedding inversion for text-based editing, arXiv preprint arXiv: 15649, 2023.
[44] Senmao Li, Joost van de Weijer, Fahad Khan, Qibin Hou, Yaxing Wang, et al. Get what you want, not what you don't: Image content suppression for text-to-image diffusion models. In The Twelfth International Conference on Learning Representations 2023.
[45] Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming- Yang. Universal style transfer via feature transforms. Advances neural information processing systems, 30, 2017.
13

---

[46] Songhua Liu, Tianwei Lin, Dongliang He. Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, and Errui Ding. Adaattn: Revisit attention mechanism in arbitrary neural style transfer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6649-6658. 2021.
[47] Vivian Liu and Lydia B Chilton. Design guidelines for prompt engineering text-to- to-image generative models. In Proceedings of the 2022 CHI conference on human factors in computing systems, pages 1-23, 2022.
[48] Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. Cones: Concept neurons in diffusion models for customized generation. International Conference on Machine Learning. 2023.
[49] Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775-5787, 2022.
[50] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed represen- tations of words and phrases and their compositionality. In C.J. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013.
[51] Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen Or, and Dani Lischinski. Styleclip: Text-driven manipulation of stylegan imagery. Proceedings of the International Conference on Computer Vision, 2021.
[52] Yuang Peng, Yuxin Cui, Haomiao Tang, Zekun Qi, Runpei Dong, Jing Bai, Chunrui Han, Zheng Ge, Xiangyu Zhang, and Shu-TTao Xia. Dreambencht+: A human-aligned benchmark for personalized image generation. In The Thirteenth International Conference on Learning Representations, 2025.
[53] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint 2rXiv:2307.01992, 2023.
[54] Tianhao Qi, Shancheng Fang, Yanze Wu, Hongtao Xie, Jiawei Liu, Lang Chen, Qian He, and Yongdong Zhang. Deadiff: An efficient stylization diffusion model with disentangled representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern 2024.
Recognition, pages
[55] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763 PMLR, 2021.
[56] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06/25, 2022.
[57] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684-10695, 06 2022.
[58] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aber- man. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023.
[59] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. Photoreal- istic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 2022.
14

---

[60] Jing Shi, Wei Xiong, Zhe Lin, and Hyun Joon Jung. Instantbooth: Personalized text-to-image generation without test-time finetuning. Proceedings of the IEEE Conference on Computer Vision and Puttern Recognition, 2024.
[61] Alex Shonenkov, Misha Konstantinov, Daria Bakshandaeva, Christoph Schuhmann, Ksenia Ivanova, and Nadiia Klokova. Deepfloyd-if. mttps://githun com wwwm/eep-floodd//eeeeoooooo- 2023.
[62] Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, et al. Styledrop: Text-to-image generation in any style. Advances in Neural Information Processing Systems, 2023.
[63] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021.
[64] Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, and Kiyoshi Tanaka. Improved artgan for conditional synthesis of natural image and artwork. IEEE Transactions on Image Processing. 28(1):394-409, 2019.
[65] Narek Tumanyan, Omer Bar-Tal, Shai Bagon, and Tali Dekel. Splicing vit features for semantic appearance transfer. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 10748-10757, 2022.
[66] Andrey Voynov, Qinghao Chu. Daniel Cohen-Or, and Kfir Aberman. p+: Extended textual conditioning in text-to-image generation. arXiv preprint arXiv:2303.09522 2023.
[67] Haofan Wang, Matteo Spinelli, Qixun Wang, Xu Bai, Zekui Qin, and Anthony Chen. In- stantstyle: Free lunch towards style-preserving in text-to-image generation. arXiv preprint arX4v:240..02733, 2024.
[68] Haofan Wang, Peng Xing, Renyuan Huang, Hao Ai, Qixun Wang, and Xu Bai. Instantstyle- plus: Style transfer with content-preserving in text-to-image generation. arXiv preprint arXiv:2407.00788. 2024.
[69] Kai Wang, Fei Yang, Shiqi Yang. Muhammad Atif Butt, and Joost de Weijer. Dynamic prompt learning: Addressing cross-attention leakage for text-based image editing. Advances in Neural Information Processing Systems, 2023.
[70] Zhizhong Wang, Lei Zhao, and Wei Xing. Stylediffusion: Controllable disentangled style transfer via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7677-7689, 2023.
[71] Zhouxia Wang, Xintao Wang, Liangbin Xie, Zhongang Qi, Ying Shan, Wenping Wang, and Ping Luo. Styleadapter: A single-pas, lora-free model for stylized image generation. International Journal of Computer Vision, 2023.
[72] Sam Witteveen and Martin Andrews. Investigating prompt engineering in diffusion models. arXiv preprint arXiv:2211.15460 2022.
[73] Zongze Wu, Yotam Nitzan, Eli Shechtman, and Dani Lischinski. Stylealign: Analysis and applications of aligned stylegan models, 2021.
[74] Peng Xing, Haofan Wang, Yanpeng Sun, Qixun Wang, Xu Bai, Hao Ai, Renyuan Huang, and Zechao Li. Csgo: Content-style composition in text-to-image generation. arXiv 2408.16766. 2024.
[75] Yuan Yao, Jianqiang Ren, Xuansong Xie. Weidong Liu, Yong- Liu, and Jun Wang. Attention- aware multi-stroke style transfer. In Proceedings of the IEEF/CVF conference computer vision and pattern recognition, pages 1467-1475, 2019.
[76] Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. Proceedings of the Conference on Artificial Intelligence, 2024.
15

---

[77] Chang Yu, Junran Peng, Xiangyu Zhu, Zhaoxiang Zhang, Qi Tian, and Zhen Lei. Seek for incantations: Towards accurate text-to-image diffusion synthesis through prompt engineering. arXiv preprini arXiv:2401.06345 2024.
[78] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. Proceedings of the IEEE/CVF international conference on computer vision, pages 1975--1986, 2023.
[79] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836-3847, 2023.
[80] Yuechen Zhang, Jinbo Xing, Eric Lo, and Jiaya Jia. Real-world image variation by aligning diffusion inversion chain. Advances in Neural Information Processing Systems, 36, 2024.
[81]
Yuxin Zhang. Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong- Yee Lee, Oliver Deussen, and Changsheng Xu. Prospect: Expanded conditioning for the personalization of attribute-aware image generation. SIGGRAPH Asia 2023, 2023.
[82] Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu. Inversion- based style transfer with diffusion models. In Proceedings of the IEEE/CVE conference computer vision and pattern recognition, pages 10146-10156, 2023.
[83] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017.
16

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: See Abstract and Introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results
be expected to generalize other settings.
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: See Appendix
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question:
For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
17

---

Justification: S.2
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: Sec.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm.
(b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
18

---

Answer: [Yes]
Justification:
Supplementary Material.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.cm
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ cppppi.ccppulli//uuuiu wwwiicccguie//eeeeeeeeeis CoodeSubeiiie ssi wswonnolli onnolicy)))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Sec.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [NA]
Justification: Not applicable.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
19

---

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Appendix (Supplementary Material).
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g.. preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: http://:////iiss..eeee httpp///s...cpp...p.tttcccciiiee
Answer:
[Yes]
Justification:
We have carefully checked the NeurIPS code of ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is a special consid- eration due to laws or regulations their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: See Appendix
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to
20

---

generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
there are negative socictal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: Not applicable.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: We politely cited the existing assets and read their usage license.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, snperswwihcoode com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's is creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
21

---

Answer: [NA]
Justification: Not applicable.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [Yes]
Justification:
Sec 4.2 and Supplementary Material.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: Not applicable.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Answer: [Yes]
22

---

Justification:
Supplementary Material.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy htteep////earrppcccc////f/rreees for what should or should not be described.
23

---

Appendix
A Statements
Limitations. The present study focuses on disentangling the color and texture elements in training- free manner, and performing stylized image generation using these elements. This method can offer a flexible and customizable image generation for art creators and designers. However, when applying to content-consistency stylization, the consistency generation ability of this work can be further improved. In our future work, will explore deeper with the consistency and expand our research into content-consistency stylization.
Broader Impacts. SADis enhances the flexible stylization capability in text-to-image synthesis by disentangling the color and texture elements. However, it also carries potential negative implications. could be used to generate false or misleading images, thereby spreading misinformation. If SADis is applied to generate images of public figures, it poses risk of infringing on personal privacy, Additionally, the automatically generated images may also touch upon copyright and intellectual property issues.
Ethical Statement. We acknowledge the potential ethical implications of deploying generative models, including issues related to privacy, data misuse, and the propagation of biases. All models used in this paper are publicly available. We will release the modified codes to reproduce the results of this paper. We also want to point out the potential role of customization approaches in the generation of fake news, and we encourage and support responsible usage.
Reproducibility Statement. To facilitate reproducibility, we will make the entire source code and seripts needed to replicate all results presented in this paper available after the peer review period. We will release the code for the novel color metric we have introduced. We conducted all experiments using publicly accessible datasets. Elaborate details of all experiments have been provided in the Appendices.
B Image-Prompt Additivity
B.1 Broader Property of Image-Prompt Additivity
We demonstrate the broader property of Image-Prompt Additivity in Fig. 9 For instance, in Fig. (a), subtracting the embedding of a hat from the embedding of person wearing hat results in the generation of a person without hat. Similarly, in Fig. (b), adding the embedding of glasses to person results in the generation of that person wearing glasses. We refer to this phenomenon as Image-Promp Additivity. We hypothesize this property originates from the image-text paired training in CLIP models [55]. The training process endows the image branch with the additivity property
inherent to the text embedding space (50 33 6.. Although the person identities are altered after embedding additivity manipulations, this approach demonstrates promising property for enabling our training-free free color-texture disentanglement. Building on this property, we develop our method, SADis, extract color and texture information from reference images and effectively apply these attributes in T2I generation.
In future work, aim to address the limitations of Image- Prompp Additivity by enhancing identity consistency after additivity manipulations. This could broaden the impact and applicability this property, advancing its utility in the field of image generation.
B.2 Extra Analysis on Image-Prompt Additivity
As another illustration of image prompt additivity, we construct set of blue, red, and green images. For example, the blue set is constructed by setting the red and green color channels to zero of a set of 100 images (similarly, for the green and red set). We isolate the color representation by performing a feature subtraction between the blue image and its grayscale equivalent. We plot their projected embeddings as blue dots Fig. (Right); note how these embeddings always maintain close the pure blue embedding and are far from the green and red embeddings. Also, generated images with these subtraction blue color embeddings keep the bluish color palette as expected (see the car and t-shirt). An extended analysis is presented in Fig, where we apply similar manipulations using
24

---

Subtraction
Addition
Oljec
Input
Enooder
IP-Adapler
Object
Impur
GLIP
Encoder
IP-Adaater
TONE
Ostpui
Ouipul
Ouggu
Impu:
Inpux
AIPAY
KIX
Figure 9: By subtracting or adding the object image embeddings from the input image embeddings, can effectively remove add the object the scene, although some degree of identity information for the person is also diminished.
Prompe A placo
Deriau sind Mtastbuttor
CLP image Enooder
IPuAcaple
TTahhii
FDOC
Grascali
Arpar
Ghapncalr
Figure 10: (Left) By subtracting the grayscale image embeddings from the color image embeddings, we can effectively remove the texture information and isolate the color information. Combined with the prompt "A photo of bird" input images. (Right) By subtracting the bluish image embeddings with the grayscale embeddings for random generated images with 100 prompts, we visualize the subtracted embedding via PCA decomposition. We observe them gathering around the pure blue image. The generation with these subtracted embeddings further prove the consistency.
generate images in the same color schemes as the
colorful images color references. The successful clustering further confirms that the resulting embeddings effectively preserve essential color information.
C Implementation details
We develop our method, SADis, based on the SDXL model 53 which is among the leading open- source T2I generative models available. To construct the experimental datasets, randomly select 40 images from the WikiArt dataset [64] and StyleDrop 62 image collections. Each image can be used either to 512 x 512 before feeding into models. Also, for quantitative comparisons, 20 images are designated the color reference set, while the remaining 20 serve the texture reference set. Additionally, we sample 10 content prompts from StyleDrop, yielding a total of 4000 stylized images for each method for comparison. Since some comparison methods (such as SDXL (53) and Artist [39] require text prompts as style controls for image generation, we employ the state-of-the toftthe-art art vision-language model GPT-4o as the image captioning tool to generate precise color and texture prompts from the reference images. Subsequently, the content, color, and texture prompts are concatenated in the format: '/content prompt), /texture prompt) in the color (color promptJ'. As for the comparison models (like DEADiff [54], IP- IP-Adapter (76). Instantstyle [67], DreamStyler K. StyleDrop [62], and CSGO (74) that require text prompts for color control, the text prompt constructed as: '/content prompt), in the color (color prompt/". All experiments were conducted NVIDIA-L40s GPU.
color reference
texture reference in the experiments. All input images resized
25

---

Extracted Color Embedding Visualizaaion
color
celor
A design bag
B
SDXL
A
B
A cabin
SDXL
= A
A
Texture
Color Input
Grayscale
Figure 11: By subtracting the color image embeddings with the grayscale embeddings for random generated images with 100 prompts, we visualize the subtracted embedding via PCA decomposition. We observe them gathering around the color reference image. The generation with these subtracted embeddings further prove the consistency.
We further include the details of each evaluation metric. CLIP Score [55] is used evaluate the semantic alignment between the text prompt and the generated image. We calucate the CLIP Score according to configuration of the T21-Compeench repository (34). For the color alignment evaluation. we use MS-SWD (25), color histogram distance (C-Hist), and GPT4o to calculate the color attribute similarity between the color reference and the generated image. MS-SWD 25 since the generated imgae are usually not spatially aligned with the color reference image, we use MS-SWD to better evaluate the color attribute alignment according to the default setting their repository 25 C-Hist: we first compute the RGB histograms of the color reference and generated images. Afterwards, the Bhattacharyya distance is used measure the differences between their color histograms. GPT- 4o score I to comprehensively evaluate the color alignment performance, we furture adopt the multimodal model GPT-4o to compute the color alignment score between the generated image and the color reference image. Specifically, the GPT-4o metric is computed according to Fig, 12 Firstly, following the previous Work[ we extract dominant colors by ColorThice Afterwards, we feed the extracted color names to GPT-4o as the reference color from the color image, and ask GPT-4o to give 1-5 points according to the criteria shown in Fig.
D WCT transformations formulas
Given latent 2, E RC and a color reference latent 2f E RCxHW at timestep adopt Whitening and Coloring Transforms (WCT) 45 31 to transform E, to match covariance matrix of color reference latent of. There are two step for WCT: Whitening transform and Coloring transform.
Whitening transform: the latent 2 is firstly centered by subtracting its mean vector m, and then an uncorrelated latent E, is obtained by:
1 = E-E--112 ET
where D denotes diagonal matrix of 2 E RCC and E is the corresponding orthogonal matrix of eigenvectors which satisfy 2, EDET.
26

---

refertncs imudge
refortess inulgs
Stylizet tnuiee
(I) Extract dominamt colors of color reference imuges:
20 color thief CokrThecookke pel_)uth)
be color names - [webeolors.ggh samz(tupl(rrb)) for thief.get pulletefoooor color
for e. nume in gl. polor somee
color sir color str c_nome
Scor::
Explanation: The shows and indiaared
Prompe GPT-4a:
deminast coloss, anong others.
my
identify objects their colors image.
According the input image, evaluate there are (color str) the inpur image. Give score from I to according the critera::
reffrtenn Leaise
rnfortond laagge
Styliaed Image
S: there
than four types dominsmii colors from (solor the
image.
4: there three types deminamt colors from (color stri in image.
3: there are two types domirant colors froes [solor_ [erorrssr]] in the
the image. there dominant color from image.
dominant
Provide your analysis explanstion JSON
following keys:
Score:
score and explansiion (within 20 words).
Explamation: dominant colors from steeiblue. silver. skybluc, slategmy, midnighthlue present.
Figure 12: GPT-4o metric for color alignment evaluation.
Insger Anagees
Was Llls piwe while
seldon yoluss Light bax, dork grenn
grewn aur wiile, DOx, buch
Moscian Blue, effebiie sanly begg
Rost Sprrastantat testure wits thick, oxtri vivid, dyeant coler poluett, glvtng senze
Surret astenciton fladigg expressore enrgetic truskerre
letemnaion sostes sattly etery dationee
expressionist syse Inslscrdees sodyy upene" coter poletoe
Figure 13: Some color and texture prompts generated by GPT-4o for comparison methods in the main paper for qualitative evaluation.
Coloring transform: it's the reverse process of Whitening transform 132 45
Beforehand, the color reference latent 2f is centered by subtracting its mean vector m.. Subsequently, we obtain the z wet? =f 7:
transformed latent 2/eet which satisfies the desired correlations 2, 2t
2, E.D.
(5)
where D. a diagonal matrix with the eigenvalues of the covariance matrix =f and E, is corresponding orthogonal matrix of eigenvectors. Finally, re-center the WCT transformed latent ueet by adding the mean vector m. of the color reference latent 2f:
2t
m.-
Additional Experimental Results
In Fig. 14 include more color-texture disentanglement examples of method SADis, which is under the Disentangled Stylized Image Generation (DisIG) scenario.
Additional comparison results with complex prompts. To further evaluate performance with complex prompts, we conducted additional experiments by randomly sampling 10 complex prompts from DREAMBENCH++. [52], together with 10 color and texture images from our dataset, resulting in 1000 generated images for evaluation. As shown in Tab. compared other methods,
27

---

lightrens By tse s69
A Atridtional Chinas balding
Amillng dog
Flarret
Calor Oudyycc
Tortord Soutsee
Figure 14: Additional experimental results SADis.
our approach achieves the best disentanglement of color and texture, resulting in a more balanced performance in both color and texture consistency. Specifically, method attains significantly higher color consistency, while other methods often exhibit strong color-texture entanglement, with color being overly influenced by the texture reference image. Although IP-Adapter achieves the highest numerical score for texture consistency, it suffers from severe semantic leakage from the texture reference image (Row 2 and Row Fig. 6). leading poor text-image alignment (CLIP score: 0.257), which is inadequate for stylization. In contrast, except for IP-Adapter, our method achieves the highest scores in both texture consistency and text-image alignment.
Image-based stylization. Furthermore, ControlNet (79 to enable image-basee stylized generation and material transfer, as demonstrated in Fig. 16 and Fig. the last row) respectively. Here, adopt ControlNet (Canny) the base pipeline, setting its conditioning scale to 0.6. All other hyper-parameter are kept consistent with those of SADis. This integration broadens the application scenarios of proposed approach.
method, SADis, be scamlessly integrated with
Color and material transfer. Given color and material references from separate images, our method, SADis, is also capable disentangling color and material elements, and provides precise controls over color and materials in T2I generation. The color and material transfer results shown in
28

---

Calle
Color
Color
Color Colerimuteraal
Real aplizationt
transfor
Matorraf
Figure 15: Stylized images generated by training-free method, SADis. (Up) As shown in the first rows, enables disentangled control over color and style attributes in text-to-image diffussin models using separate image prompts. This approach offers creators enhanced color control, including the use of color palettes as in the last two columns. (Down) SADis also enables real cal-immae stylization by incorporating content image as additional condition via ControlNet. Furthermore, it extends color-only stylized generation and material transfer for more flexible image generation.
Table 3: Quantitative comparison with complex prompts. The complex prompts sampled from DREAMBENCH++ [52]. The best and second-best numbers are marked with bold and underlined respectively.
The
Color C-Hist.
Texture
Method
CLIP
SWD4
GPT4ot
CLIP-If
KIDI
SDXL [53
0.291
9.00
1.14
3.07
0.698
0.091
IPAdapter [76
0.257
9.99
1.15
2.96
0.817
0.058
InstantStyle 66
0.278
11.21
1.25
2.85
0.751
0.065
Artist |39
0.253
11.15
1.24
2.67
0.742
0.082
DEADifF [54
0.280
10.01
1.14
2.80
0.718
0.090
StyleDrop 62
0,290
11.90
1.35
2.83
0.731
0.078
DreamStyler
0.300
11.27
1.21
2.57
0.677
0.083
CSGO 74
0.299
12.60
1.22
2.64
0.728
0.081
SADis (Ours)
0.301
6.08
0.95
3.14
0.751
0.064
Fig. 19 and Fig. the last row). This capability of SADis highlights its potential applications in artistic creation and industrial design. We also desire to note that, the term "color palette" typically refers to the overall color scheme of image, not specifie local regions. Our SADis does not control the color palette of individual objects in general. However, Fig. [9 demonstratess controlled color and material transfer using a masking mechanism with method SADis.
Color and texture transfer from the same image. SADis performs exceptionally well when using the same image as both the color and texture reference (as shown in Fig. 20), showcasing its remarkable flexibility and adaptability. This ability highlights the method's robusiness in leveraging single source to effectively guide both color and texture information, ensuring consistent results. The comparative experimental results presented in Tab. In terms of texture consistency, our method achieves the best performance among all methods except for IP-Adapter. However, important to note that although the IP- IP-Adapter achieves the highest numerical score for texture consistency, it introduces severe semantic leakage from the texture reference image (see Row. and Row. of IP-adapter results in Fig. 6) resulting in poor text-image alignment (CLIP: 0.260 in Tab. 44. which fails to meet the requirements of stylization. contrast, excluding the IP-Adapter, method achieves the highest scores in color consistency (MS-SWD: 3.19), texture consistency (CLIP-I: 0.754), and text-image alignment (CLIP: 0.302).
coherent
Robust color variations. As shown in Fig 21 and Fig. 22 we vary the saturation and illuminant continuously. To be specific, for saturation, developed Python-based saturation adjustment tool
29

---

Toblort
omage
mange
Gonital youge
Figure 16: Our work is compatible with ControlNet to achieve image-based stylization.
Baseline
SVD
+ SVD; + RegWOT
Color
Texture
Figure 17: Visualization of ablating each component of SADis.
utilizing HSV (Hue, Saturation, Value) color space transformation. The tool linearly modifies the saturation channel using factors of [0.2, 0.6, 1.0, 1.5, 3.0], respectively, before converting the images back to RGB format. The results of saturation variations shown in Fig. 21 Regarding the color of the illuminant, we modify images by applying calibrated RGB channel multipliers. For warm temperatures (red) as shown in Fig. [2] red increases by 30%, green reduces by 10%, and blue by 20%. The intensity of these adjustments can be controlled (0-1 range), enabling fine-grained color temperature manipulation. We present 3 increasing intensities in Fig- 22 Our method SADis is able to generate images with smooth change along with the saturation and illuminant color variations, while the other method InstantStyle (67] fails. That further proves the robustness of method SADis for Disentangled Stylized Image Generation (DisIG).
Each component works independently. The T2I generations in Fig. 18ft provide evidence that the color and texture branches in SADis function independently. Additionally, the visualization for our ablation study in Fig. I7 further demonstrates improved color alignment with minimal texture degradation. achieving a good trade-off. However, the trade-off between texture and color alignment is optional for users, allowing them to adjust the balance based on the requirements of specific application scenarios.
Stability of different sampling rounds. We conducted additional experiments to assess the stability of color and texture in repeated generations. For each setting, the process was repeated five times. generating 1 1,000 images per round and computing the relevant metrics. As summarized in Tab. 5 the results show minimal variation color and texture consistency across rounds, demonstrating robustness and stability of our method.
30

---

+Color ref
Heetture ref
A lighthouse of van gogh painting style
Figure 18: SADis can control color and texture generation separately using reference images.
Materíal
Images
Color Images
photo of a cup.
A chaár.
Figure 19: SADis effectively disentangles color and material elements from separate images, enabling precise control over color and material in image generation.
31

---

Chinesse bullding
eabin In fleld
in the mounttais
Figure 20: SADis performs well using the same image as both the color and texture reference, demonstrating its flexible capability.
Table 4:
Quantitative comparison where both color and texture are derived from the same image. The best and second-bess numbers are marked with bold and underlined, respectively.
Color
Texture
Method
CLIP
MS-SWDJ
C-Histi.
GPT4ot
CLIP-II
KIDJ
SDXL |53
0.292
9.12
1.15
3.04
0.696
0.090
IPAdapter 76
0.260
4.18
0.63
3.44
0.815
0.057
InstantStyle 67
0.277
4.21
0.64
3.41
0.753
0.066
Artist [39
0.272
10.98
1.18
2.83
0.744
0.077
DEADiff 54
0.295
10.81
1.14
2.66
0.721
0.089
StyleDrop 62
0.292
12.16
1.28
2.51
0.717
0.090
DreamStyler 3
0.301
11.90
1.09
2.53
0.691
0.095
CSGO 74
0.298
6.43
0.83
3.09
0.716
0.089
SADis (Ours)
0.302
3.19
0.53
3.51
0.754
0.066
SSat
Original
*sat
Color images
Style image
Ours
Instantstyle
Figure 21: Compared to InstantStyle, SADis preserves the content more effectively under saturation variations.
F
Ablation studies on Reg WCT
Reg WCT scales. Given latent 2 at timestep f, the color rectified latent 2, after RegWCT is presented
- w)
RegWCT(=)).
Here, we ablate the balancing factor w, as shown in Tab. 6 With the increasing scale w of RCgWCT, the color score of SADis improves significantly, while texture preservation remains largely unaffected. Based on the ablation results in Tab. 6 we set to 0.5 to achieve balanced performance between color and texture alignment.
32

---

tred intensity
Color images
Style imagee
Ours
Instantstyle
Figure 22: Compared to InstantStyle, SADis preserves the content more effectively under diverse illuminant variations.
Table 5: Quantitative results for different sampling rounds.
Color
Texture
Round
CLIP
MS-SWDL
C-Hist
GPT4o*
CLIP-It
KID
0.301
6.08
0.95
3.14
0.751
0.064
2
0.301
6.05
0.95
3.15
0.750
0.065
3
0.300
6.23
0.96
3.12
0.752
0,064
4 0.301
6.16
0.95
3.12
0.751
0.066
5
0.304
6.08
0.95
3.14
0.749
0.066
Timestep intervals. The ablation study on different timesteps apply RegWCT is shown in Fig. 23 (a). During the denoising process, applying Reg WCT to the latent enhances the color alignment performance but sacrifices texture preservation. As observed in Fig. (3)(a), the early stages of denoising contribute more to the color than the latter stages, which is also supported by previous works [17 81 66). Therefore, we only apply RegWCT during the early stages of the denoising, specifically within [0.8I;, 0.67] to achieve a balance between the color and texture generation.
Scale À of noise injection. The ablation study on the noise scale illustrated in Fig. 23 (b). Without noise injection (A = 0), applying WCT improves color alignment but also causes great texture degradation for DisIG. This issue alleviated by injecting the specific degree of latent noise zr, which is demonstrated in Fig. (b). To achieve balanced color and texture alignment, we set 0.01 in the experiments based on the results shown in Fig. (b).
G Ablation studies on CTE.
Scale of SVD. The ablation results of the scaling factor and 3 is presented in Tab. 7 and Tab. 8 respectively. As the scaling factor y increases, the color scores (such as MS-SWD and C-HIST) improve, with the sacrifice of the slightly texture performance degradation (revealed by CLIP-I). When 3 increases beyond 1, the texture consistency metric (CLIP-I) improves, indicating enhanced texture alignment. However, this comes at the cost of decreased color consistency, as reflected by the MS-SWD and C-Hist scores. Therefore, to achieve better balanced performance between texture and color consistency, set the default values to 3 and 0.003 in main experiments.
33

---

Table 6: Ablation studies of Weg scales. considering the trade-off of texture and color alignment, the RegWCT scale is set as 0.5 by default.
Reg WCT Scale
0.3
0.5
0.7
1.0
MS-SWD (L)
8.05
5.65
5.18 0.96
5.57
5.23
Color
C-Hist (L)
1.06
0.98
0.89
0,889
Texture
CLIP-I (T)
0.747
0.744
0.743
0.741
0.740
6.0
MS-SWD (1) CILIP-IIT)
(1)
0.745
0-
(T)PM
0.900
0.74
0.73
01
MS-SWD
7.0
6.91
MS.
3.66
0.00
3.5-
0.700
6-
3.4
0.67
3.8
0.0
3.2
(b)
Figure 23: Ablation studies applying Reg to different timestep intervals (left). Ablation studies on the scale of noise injection (right) during applying RegWCT. By default, À set to 0.01.
mmgge
Coler sollems
Color scale- 0.2
Color solle- 0.4
Coker sokle
Oolor soale 0.5
MARe
KoRiE
L.
Texture paage
Figure 24: Results of different scales of color embedding Embar- Here, the scale Emb fixed as 1.
Table Ablation studies of scaling factor 7. y is set to 0.003 according to the ablation results.
= I)
0.001
0.003
0.005
0.007
0.009
0.011
MS-SWD (L)
5.70
5.71
5.57
5.36
5.22
5.21
5.20
Color
C-Hist (L)
1.01
1.017
0.962
0.939
0.897
0.887
0.861
Texture
CLIP-I(1)
0.759
0.755
0.743
0.736
0.730
0.723
0.713
Table 8: Ablation studies of scaling factor B. B is set to 1 according the ablation results.
( 0.003)
0.5
0.7
0.9
1.1
1.3
1.5
MS-SWD ())
5.20
5.28
5,45
5.57
5.70
5.92
6.17
Color
C-Hist (L)
0.890
0.923
0.949
0.962
0.973
0.992
1.011
Texture
CL)
0.711
0.729
0.740
0.743
0.747
0.749
0.752
34

---

Table 9: Ablation results of controlling different cross-attention layers of SADis.
Color
Texture
Different CA layers
CLIP
MS-SWDL
C-Histl
GPT4of
CLIP-It
KID
(ours)
0.301
6.08
0.95
3.14
0.751
0.064
b0a0+bOal
0.292
6.54
0.981
3.13
0.785
0.057
b0a0+b0al+b0a2
0.292
6.53
0.982
3.12
0.793
0.056
b0al+bla0
0.291
6.55
0,981
3.11
0.791
0,057
40al+blal
0.300
6.54
0.983
3.12
0.751
0.065
b0al+bla2
0.297
7.19
1.005
3.10
0.753
0,066
b0+b1
0.291
7.21
0.998
3.07
0.794
0.055
b0+b1+b2
0,278
7.24
1.003
3.08
0.807
0.056
Color-texture scales. As shown in Fig. 24 we fix the scale the texture embedding Emb,, to while varying the scale of the color embedding Emb..- from 0 to 1.8. With greater weight for the color embedding, the color score improves. However, color embedding is too high, it may negatively impact texture preservation and introduce artifacts that are not derived from either the color or texture reference images.
worth noting that when the scale of the
H
Ablation studies on controlling different cross-attention layers.
The purpose of employing multiple Cross-Attention (CA) layers IP-Adapter is to ensure consistency in the identity of the input. Prior researches [67 68) indicate that different CA layers in IP-Adapter control different attributes. For example, some are more associated with content, while others relate more to style. Our decision to inject at the first decoder layer was initially inspired by InstantStyle [67]. Experiments with multiple CA layers are provided in Tab. 9 Here, 'bidx0aidx denotes the 'idx1' 1thh CA layer in the "idx0"-th -th decoder block of the denoising UNet. As shown in the table, injecting into additional CA layers beyond the style-relevant one (i.e., the first decoder layer) improves texture metrics but degrades color representation. Moreover, we observed that controlling additional CA layers leads to generated images containing more semantic content from the texture reference image (similar to IP-Adapter's results shown in Fig. []. which is inconsistent with the goal of texture transfer. This observation is consistent with the findings reported InstantStyle.
35


Reviews:
[{"review_id": "yTHR6vMEw1", "reviewer": "Reviewer_L5Gk", "summary": "The author proposes a tuning-free style-preserving scheme for texture and color disentanglement. Leveraging the additive property of the CLIP embedding space and assuming that image style features can be decomposed into color and texture features as a strong hypothesis, the disentanglement scheme is designed. Different from existing methods that require model fine-tuning, SADis achieves style disentanglement solely through forward propagation and feature transformation, without modifying the parameters of pre-trained diffusion models, thus significantly reducing computational costs.", "questions": "1.How to verify the correctness of the strong hypothesis in the process of Eq1?\n\n2.The author demonstrates some simple prompt scenarios, but lacks verification with complex prompts.\n\n3.How to verify the correctness of the strong hypothesis in the process of Eq3?\n\n4.I'm more concerned about the issue of stability. Will there be significant differences when generating multiple images (rolling).\n\n5.Some recent methods only support single-image processing. How to perform inference for multiple images?", "limitations": "N/A", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n\n1. The writing of the paper is clear.\n\n2.The motivation of the paper is clear.\n\n3. The method proposed in the paper is interesting, which decouples CLIP features to obtain texture and color information. However, I still worry that such information is unstable.\n\n4. The paper provides some quantitative and user studies.\n\nWeaknesses:\n\n1.Many contents of the paper are based on some strong assumptions, which may lack sufficient theoretical basis.\n\n2.Lack of verification with some complex prompts.\n\n3.The visualization results in Figure 5 do not demonstrate that the proposed method has significant advantages.", "was_revised": true, "final_justification": "The rebuttal provided helpful clarifications and new experimental results that addressed my concerns. I recommend including these analyses in the main paper to improve clarity and completeness. Thus, I am increasing my score.", "rebuttal": "We sincerely appreciate your feedback. We are grateful for your recognition that the paper is ***well-written***, the research motivation is ***clear***, and our proposed method is ***interesting***. Below, we provide detailed responses to each of your specific points.\n\n**W1&Q1(strong hypothesis in Eq.1):** Our verification of the image-prompt additivity hypothesis in Eq. 1 is empirical rather than formally proven, and this approach is consistent with established practices in analyzing learned representations of T2I models[1,2]. Prior works such as SEGA [3] and ToMe [4] have demonstrated that CLIP-encoded embeddings exhibit semantic compositionality through additive and subtractive properties in textual spaces, providing foundation for similar behavior in image embedding spaces. Furthermore, the difference vectors between pairs of images with the same color (e.g., A − A* and B − B*) point in consistent directions (Fig. 3a), demonstrating the correctness of this subtraction-based color decoupling method. Besides, the consistent superior performance in color alignment metrics (MS-SWD, C-Hist, GPT-4 color scores) and successful disentanglement results shown in Fig. 4-Fig.6 also provide strong empirical evidence for the correctness of this hypothesis.\n\n**W1&Q3(correctness of Eq.3)**: Eq. 3 is inspired by WNNM[5], a method originally proposed for image denoising. WNNM assumes that most of the noise in a noisy image is concentrated in the lower-K singular values. Each singular value σ of an image patch is updated according to the formula:\n$$\\sigma \\leftarrow \\sigma - \\frac{\\lambda}{\\sigma + \\varepsilon}$$\n\nIn this paper, we apply **Eq. 3** to further suppress the gray color in grayscale images, while preserving only the texture information. Specifically, our goal is to decouple texture from the *textural image* and eliminate the influence of color information. We observe that directly using the Y channel (grayscale) of the textural image introduces a grayish color bias in the generated results (see Fig. 4(a), *Baseline*). To address this, we aim to remove the residual gray information from the grayscale image.\n\nTo achieve this, we concatenate the Y channel of the textural image with its mean grayscale intensity (i.e., the average gray value), forming a new embedding using **Eq. 2**. This embedding mainly captures the gray color information. Therefore, the **top-K singular values** in the constructed embedding matrix $\\text{Emb}'_{\\text{tx}}$ predominantly correspond to the color information we intend to suppress. At the same time, WNNM demonstrates that singular values carry clear physical meaning and should be weighted differently. Thus, we adopt **Eq. 3** to ensure that components corresponding to **larger singular values** undergo **stronger shrinkage**, effectively removing the undesired gray color while retaining informative texture features.\n\n**W2&Q2 (complex prompts):** \nTo further evaluate performance with complex prompts, we conducted additional experiments by randomly sampling 10 complex prompts from DREAMBENCH++ [6], together with 10 color and 10 texture images from our dataset, resulting in 1,000 generated images for evaluation.\nThe performance trend under long and complex prompts remains consistent with the results in our main paper (Table 1). Compared to other methods, our approach achieves the best disentanglement of color and texture, resulting in a more balanced performance in both color and texture consistency. Specifically, our method attains significantly higher color consistency, while other methods often exhibit strong color-texture entanglement, with color being overly influenced by the texture reference image.\nAlthough IP-Adapter achieves the highest numerical score for texture consistency, it suffers from severe semantic leakage from the texture reference image (see Row 2 and Row 3 in Fig. 5), leading to poor text-image alignment (CLIP score: 0.257), which is inadequate for stylization. In contrast, except for IP-Adapter, our method achieves the highest scores in both texture consistency and text-image alignment.\n\n|Method|CLIP↑|Color(MS-SWD↓)|Color(C-Hist↓)|Color(GPT4o↑)| Texture(CLIP-I↑)| Texture(KID↓)|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|SDXL|0.291|9.00|1.14|3.07|0.698|0.091|\n|IP-Adapter|0.257|9.99|1.15|2.96|0.817|0.058|\n|InstantStyle|0.278|11.21|1.25|2.85|0.751|0.065|\n|Artist|0.253|11.15|1.24|2.67|0.742|0.082|\n|DEADiff|0.280|10.01|1.14|2.80|0.718|0.090|\n|StyleDrop|0.290|11.90|1.35|2.83|0.731|0.078|\n|Dreamstyler|0.300|11.27|1.21|2.57|0.677|0.083|\n|CSGO|0.299|12.60|1.22|2.64|0.728|0.081|\n|SADis(Ours)|0.301|6.08|0.95|3.14|0.751|0.064|\n\n**W3 (visual results in Fig. 5):** Compared to the comparison methods, our approach offers best ***disentanglement of color and texture***, achieving a more balanced performance in terms of color and texture consistency.\nAs shown in Fig. 5, even with prompts providing detailed color descriptions from the reference image, baseline methods fail to accurately reproduce the intended colors. In contrast, our method achieves much higher color consistency and avoids the color-texture entanglement seen in other approaches, where color is overly influenced by the texture reference image. Notably, our method maintains strong texture preservation while improving color consistency. For instance, in Fig. 4 (last row), our generated \"bear\" image better matches the color of the “wave” reference, while other methods—such as StyleDrop, IP-Adapter, InstantStyle, DreamStyler, DEADiff, and CSGO—tend to inherit the colors of Van Gogh's Starry Night (the texture image). Although SDXL and Artist also produce colors closer to the “wave” image, their results are overly coarse. Similar trends appear in examples in Fig. 4, with additional results on color-texture disentanglement provided in Fig. S6 of the supplementary material.\n\n**Q4(stability of generating multiple images)**:  To assess the stability of color and texture in repeated generations, we conducted additional experiments by using the data in **W2&Q2**. For each setting, the process was repeated five times. In each round, 1,000 images were generated, and the relevant metrics were computed. The results, summarized in the table below, show minimal variation in color and texture consistency across rounds, demonstrating the robustness and stability of our method.\n\n|Round|CLIP↑|Color(MS-SWD↓)|Color(C-Hist↓)|Color(GPT4o↑)|Texture(CLIP-I↑)|Texture(KID↓)|\n|:--:|:---:|:--:|:--:|:-:|:----:|:-:|\n|1|0.301|6.08|0.95|3.14|0.751|0.064|\n|2|0.301|6.05|0.95|3.15|0.750|0.065|\n|3|0.300|6.23|0.96|3.12|0.752|0.064|\n|4|0.301|6.16|0.95|3.12|0.751|0.066|\n|5|0.304|6.08|0.95|3.14|0.749|0.066|\n\n**Q5(support for multiple-image control)**:  Our method supports simultaneous control over multiple colors and textures to generate images with mixed color and texture attributes. To ensure a clearer and more direct comparison of evaluation metrics, we use two color reference images and two texture reference images as inputs in each experiment. The following two tables summarize the results: the first table reports *multi-color control* using two color references and one texture reference, while the second table reports *multi-texture control* using two texture references and one color reference. Complex content prompts are sampled from **Q2**.\nFor *multi-color control*, the comparison methods use concatenated textual prompts containing two color descriptions (generated by GPT-4o) to achieve multi-color control. Experimental results demonstrate that our method consistently outperforms others in color fidelity while maintaining competitive texture consistency. Notably, although IP-Adapter obtains the highest numerical score for texture consistency, it exhibits severe semantic leakage from the texture reference image (Row 2 and Row 3 in Fig. 5), resulting in poor text-image alignment (CLIP: 0.269), which is insufficient for effective stylization. In contrast, our method achieves a better balance between color and texture consistency, demonstrating superior disentanglement and control over color and texture throughout the stylization process.\n\n|Method|CLIP↑|Color (MS-SWD↓)| Color (C-Hist↓)|Color (GPT4o↑)|Texture (CLIP-I↑)|Texture(KID↓)|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|SDXL|0.290|9.06|1.148|2.98|0.695|0.093|\n|IP-Adapter|0.259|10.31|1.185|2.87|0.814|0.060|\n|InstantStyle|0.278|11.23|1.204|2.81|0.749|0.067|\n|Artist|0.262|12.55|1.212|2.40|0.743|0.071|\n|DEADiff|0.275|10.48|1.138|2.66|0.717|0.089|\n|StyleDrop|0.285|11.76|1.173|2.74|0.723|0.077|\n|dreamstyler|0.291|16.54|1.222|2.08|0.679|0.094|\n|CSGO|0.300|12.81|1.213|2.58|0.729|0.081|\n|SADis(Ours)|0.302|7.27|1.002|3.06|0.750|0.066|\n\nFor *multi-texture control*, since other methods do not support multiple texture reference images as input, we report results only for our method in this setting. The generated visualizations demonstrate that our approach effectively blends multiple color and texture attributes. In future versions, we will provide additional experimental results on the mixing of multiple colors and textures in the supplementary materials.\n\n|Method|CLIP↑|Color(MS-SWD↓)|Color(C-Hist↓)|Color(GPT4o↑)|Texture(CLIP-I↑)|Texture(KID↓)|\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|SADis|0.305|6.14|0.955|3.14|0.749|0.071|\n\n**Paper formatting concerns**: Thanks for pointing out our grammatical and logical errors in the original paper, we will correct them in any future versions.\n\n[1] Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models (ECCV 2024)  \\\n[2] IP-Composer: Semantic Composition of Visual Concepts. (SIGGRAPH 2025) \\\n[3] SEGA: Instructing Diffusion using Semantic Dimensions (NIPS 2023)  \\\n[4] ToMe: Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis (NIPS 2024) \\\n[5] WNNM: Weighted Nuclear Norm Minimization with Application to Image Denoising (CVPR 2014) \\\n[6] DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation (ICLR 2025)"}, {"review_id": "GlRotvSiyV", "reviewer": "Reviewer_N4y7", "summary": "This paper introduces a tuning-free method of disentangling color and texture for stylized image generation. The authors discover that the color embedding and textural embedding can be added as the style condition. The authors propose some techniques to improve generation quality, such as SVD embedding optimization and regularized whitening-coloring transforms.", "questions": "Please see weaknesses.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- The idea of disentangling color and texture information addresses a practical need.\n- The proposed method is tuning-free, making it easily applicable to various backbones such as IP-Adapter and ControlNet.\n- The design choice of using additive CLIP embeddings, along with further optimizations via SVD or WCT, is reasonable.\n- The generated results are of good quality.\n\n**Weaknesses**\n\n- From Table 2, SVD improves color persistence but worsens texture, especially when combined with RegWCT.\n- The authors propose various techniques, such as SVD, WCT, and noise regularization. However, all of these methods aim to strike a better trade-off between color and texture quality, and no single component is able to improve both aspects simultaneously.\n- This method relies on the additive property of CLIP as a vision encoder, but its effectiveness with other encoders, such as T5 or DINO, has not been validated.", "was_revised": true, "final_justification": "The authors address my concerns, especially by providing experiments of SADis combined with other vision encoders (DINOv2 and SigLIP). I would like to keep my original rating and lean toward acceptance.", "rebuttal": "We sincerely appreciate your constructive and insightful feedback. We are grateful for your recognition that the method is ***applicable*** and ***addresses practical needs***, the design is ***reasonable***, and the generation is with ***good quality***. Below, we provide detailed responses to each of your specific points.\n\n\n**W1&W2(texture consistency)**:\nThe objective of our method is to explore tuning-free color-texture disentanglement using pre-trained models. ***In our approach, texture consistency is primarily inherited from the pre-trained models(InstantStyle), while our goal is to enhance color consistency with minimal loss of texture***, thereby achieving a balance between color and texture attributes. To this end, both SVD and RegWCT are designed to address poor color consistency while minimizing texture loss as much as possible.\n\nIn terms of texture performance as reported in Table 1, our method achieves texture consistency second only to IP-Adapter and is comparable to InstantStyle (Our base model). However, it is important to note that although the IP-Adapter achieves the highest numerical score for texture consistency, it introduces severe semantic leakage from the texture reference image (see Row 2 and Row 3 in Fig. 5), resulting in poor text-image alignment (CLIP: 0.233), which fails to meet the requirements of stylization. In contrast, apart from the IP-Adapter, our method achieves the highest scores in color consistency (MS-SWD: 5.57), texture consistency (CLIP-I: 0.74), and text-image alignment (CLIP: 0.281). These results are also supported by the user study in Table 2 and Fig. 4. Additionally, following the suggestion of Reviewer R1.Q2, we have included performance comparisons under complex prompts, and the resulting trends are consistent with the above analysis. Overall, our method focuses on improving color consistency while maintaining texture consistency, and the experimental results demonstrate that our approach achieves a better balance between color and texture consistency.\n\nWe agree that our method shows stronger improvements in color accuracy, with slightly lower texture scores. However, it is important to note that the texture metrics used (e.g., CLIP-I, KID) remain sensitive to color content: modifying the color palette inevitably affects the greyscale values, which play a role in defining textures. Consequently, methods that transfer both color and texture from the same reference image—effectively ignoring the color image—benefit from aligned visual cues, making it easier to achieve higher texture scores. In contrast, our method explicitly disentangles color and texture sources and faithfully adapts to the color reference image. This necessary shift in greyscale structure may lead to a modest drop in texture metrics.\n\n\n\n**W3(other encoders)**: Our method is training-free and built upon IP-Adapter. Since IP-Adapter relies solely on the CLIP encoder as its vision encoder and does not utilize other encoders such as T5 or DINO from pre-trained models, conducting such an evaluation is not feasible. Furthermore, our method can be extended to any backbones as long as there is an IP-Adapter-like pretrained adapter for the backbone model, including Flux, SD3, SD3.5, etc."}, {"review_id": "AYzlybDyLP", "reviewer": "Reviewer_Z8Hi", "summary": "This paper investigates the additivity of the CLIP image embedding, especially for extracting color information of arbitrary images. Based on the observation, the authors propose a novel method, namely SADis, that disentangles the learning of color and texture to represent style characteristics. To remove the redundant color information from the texture image, several strategies are introduced, manipulating image embeddings by average operation, SVD, and WCT. Experiments show the disentanglement of color and texture benefits stylized image generation, as well as in-depth investigations on a variety of possible applications.", "questions": "1. The setting of \"Baseline\" in Fig. 4 (a) is not clear. I found it in lines 175-177 as \"using only the grayscale texture embedding\". Does this mean the texture branch w/o averaged embedding, SVD, and RegWCT?\n\n2. Since previous methods lack disentanglement ability, why not use the same style image for both the color and texture references to SADis, and then compare with these methods? In my opinion this would be fairer than using generated captions. In this case, I am curious about the difference between SADis, IP-Adapter, and InstantStyle, as the other two methods seem to capture texture very well but are inferior in color (observe from Fig. 5 and  user study in Tab. 1) \n\n3. Ablation studies are only conducted on two components, i.e., SVD and RegWCT. I found these two components contribute more on the color consistency in Tab. 2. It seems that they help remove color information from the texture image to inject color information, but I guess the learning of texture might benefit from another design, e.g., the concatenated averaged embedding, in which an ablation study is missing. \n\nMinor suggestions: \n1. Fig.4 better provides the text prompts or the content images (if content-conditioned generation using ControlNet) for the generated images.\n\n2. Should the line 177 be \"Fig. 4 (a)-2nd column\", line 193 be \"Fig. 4 (a)-3rd column\", and line 226 be \"Fig. 4 (a)-4th column\"?\n\n3. Fig. 4 (b) the description \"+z_T injection\", if it is the formulation in line 217, I recommend rephrasing it into noise injection, or directly RegWCT, as T is the starting point, but this injection seems to perform in a period.", "limitations": "The authors describe the limitations of the proposed SADis method in the appendix.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths**: \n\nThe paper is well-written and easy to follow. The phenomenon of image-prompt additivity is very intuitive, and additional analysis in the Appendix is also interesting. I really like these illustrations, which help me to grasp the idea quickly. The final method is well motivated based on the observations. The evaluation is thorough and demonstrates the effectiveness of the proposed method very well. I also appreciate the human judgment and time cost reported.\n\n**Weaknesses**:\n\nWhile the method clearly surpasses existing style transfer methods in the color-texture-content reference scenario, the authors use LLM-generated descriptions during comparison. This setting might be unfair for other approaches.", "was_revised": true, "final_justification": "This paper investigates an interesting phenomenon--the additivity property of the pre-trained CLIP image embedding space. Motivated by this, the authors propose the SADis method that disentangles color and texture attributes from style images. While SADis builds on  IP-Adapter for texture extraction, it shows notable improvement particularly in color and texture disentanglement, as evidenced in Fig. 5 and quantitative comparisons. This work provides valuable insights into understanding the color representation and has great potential to inspire future research. Therefore, I recommend a score of 5 with a confidence of 4.", "rebuttal": "We sincerely appreciate your constructive and insightful feedback. We are grateful for your recognition that the method is ***intuitive*** and the analysis is ***interesting***, the design is ***well-motivated***, and the paper is with ***good writing***. Below, we provide detailed responses to each of your specific points.\n\n\n\n**Q1(clarification of the baseline)**: Your understanding is absolutely correct. We apologize for any inconvenience caused by the lack of clarity. In Fig.4(a), Our ‘Baseline’ denotes color-texture disentanglement with Image-Prompt Additivity only (i.e., without SVD and RegWCT). In the revised version, we will add clarifications regarding our ‘Baseline’ in the captions.\n\n\n\n\n**Q2&W1(experiments with the same reference image)**: According to your suggestion, we have added results where both texture and color are derived from the same reference image, using the same prompt as in R1.Q2. The comparative experimental results are presented in the table below. In any future versions, we plan to include these results in the supplementary materials. As shown by the experimental results, our method still achieves the best color consistency, which is consistent with the findings reported in the original paper.\n\nIn terms of texture consistency, our method achieves the best performance among all methods except for IP-Adapter. However, it is important to note that although the IP-Adapter achieves the highest numerical score for texture consistency, it introduces severe semantic leakage from the texture reference image (see Row 2 and Row 3 in Fig. 5), resulting in poor text-image alignment (CLIP: 0.260 in the table below), which fails to meet the requirements of stylization. In contrast, excluding the IP-Adapter, our method achieves the highest scores in color consistency (MS-SWD: 3.19), texture consistency (CLIP-I: 0.754), and text-image alignment (CLIP: 0.302).\n\n\n|        Method       |     CLIP↑    |     Color (MS-SWD↓)    |     Color (C-Hist↓)    |     Color (GPT4o↑)    |     Texture (CLIP-I↑)    |     Texture (KID↓)    |\n|:-------------------:|:------------:|:----------------------:|:----------------------:|:---------------------:|:------------------------:|:---------------------:|\n|         SDXL        |     0.292    |         9.12           |         1.15           |        3.04           |         0.696            |        0.090          |\n|       IPAdapter     |     0.260    |         4.18           |         0.63           |        3.44           |         0.815            |        0.057          |\n|     InstantStyle    |     0.277    |         4.21           |         0.64           |        3.41           |         0.753            |        0.066          |\n|        Artist       |     0.272    |        10.98           |         1.18           |        2.83           |         0.744            |        0.077          |\n|        DEADiff      |     0.295    |        10.81           |         1.14           |        2.66           |         0.721            |        0.089          |\n|       StyleDrop     |     0.292    |        12.16           |         1.28           |        2.51           |         0.717            |        0.090          |\n|      dreamstyler    |     0.301    |        11.90           |         1.09           |        2.53           |         0.691            |        0.095          |\n|         CSGO        |     0.298    |        6.43           |          0.83           |        3.09           |         0.716            |        0.089          |\n|     SADis (Ours)    |     0.302    |        3.19           |          0.53           |        3.51           |         0.754            |        0.066          |\n\n\nIn addition, it should be noted that the ability of our method to represent textual information primarily originates from the pre-trained model (i.e., IP-Adapter and InstantStyle). However, IP-Adapter injects image features into all blocks of the network, which leads to semantic leakage  (see Row 2 and Row 3 in Fig. 5) from the reference image and results in poor text-image alignment . In contrast, InstantStyle only injects features into style-relevant blocks, effectively avoiding the semantic leakage issue present in the original IP-Adapter. We adopt the same injection strategy as InstantStyle, which allows for better adherence to the textual description rather than the semantics of the reference image. Given that our method and InstantStyle utilize the same style injection strategy, it is reasonable that their texture performance is comparable. Nevertheless, our main contribution lies in further improving color consistency while maintaining texture performance on par with InstantStyle, thereby achieving effective color-texture disentanglement.\n\n\n\n\n\n**Q3(ablation study of concatenation)**:\nRegarding the concatenated averaged embedding, it serves as the basis for SVD-based grayscale suppression, and therefore, SVD cannot be separated from the concatenation operation. The purpose of this concatenation is to enable the identification of the principal singular value(s) corresponding to grayscale information in the feature space after the SVD transformation.\n\nYour understanding regarding the improvements in color consistency brought by SVD and RefWCT is correct. To achieve stylized image generation, we extract color information from the image-prompt and texture information from real images. When isolating color from the image prompt, two key challenges arise: (1) extracting color while minimizing the influence of texture from the image-prompt, and (2) removing color information from the real image to prevent it from interfering with the intended color guidance.\nThe first challenge can be easily addressed by our proposed color-extraction method. To address the second challenge, we adopt SVD to suppress the color influence of the real image, enabling the extraction of cleaner texture features (as shown in Figure 2, Texture Extraction). Although SVD effectively reduces the impact of real-image colors on the final generation, it leads to only a slight degradation in texture fidelity, evidenced by a minor 0.01 drop in the CLIP-I score.\nFurthermore, your understanding of the role of the concatenated averaged embedding is also accurate. This embedding serves as the foundation for the SVD-based grayscale suppression. Therefore, SVD cannot be decoupled from the concatenation operation. The purpose of this concatenation is to identify the principal singular value(s) associated with grayscale information in the feature space after the SVD transformation.\n\nTo ensure better color consistency, we further introduce RegWCT, which enhances the traditional Whitening and Coloring Transform (WCT) by incorporating noise regularization in the latent space of diffusion models. This modification further improves color fidelity, again with only a minimal impact on image quality (a 0.01 drop in CLIP-I).\n\n\n**Minor points**: Thank you very much for your suggestion. We will incorporate your feedback in our final revisions."}, {"review_id": "dWYqvcoYOS", "reviewer": "Reviewer_3c1E", "summary": "This paper proposes a novel approach called Style Attribute Disentanglement (SADis), designed to address a key challenge in stylized image generation: achieving independent control over color and texture. Specifically, SADis leverages the additive property of image prompts to disentangle color and texture for the first time without any training. This enables users to independently extract color and texture information from different reference images.", "questions": "My major concerns are the weakness. I would like the authors to directly address these concerns in their response. My final rating is subject to change based on the quality and clarity of the authors’ feedback.", "limitations": "The authors are encouraged to answer the questions and address the weakness above.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1、The method is easy to follow. This paper proposes using the CLIP model to extract disentangled color and texture features from reference images and guide the generation of target images through the IPA plugin. Both qualitative analysis and quantitative metrics demonstrate strong performance.\n\n2、It requires no additional data training, can leverage any existing T2I model, and is fully compatible with the SD community ecosystem.\n\nWeaknesses\n\n1、The ablation study in Table 2 suggests that the proposed component contributes primarily to the injection of style color information, with limited impact on texture preservation. This raises the question of whether the texture-related performance gain is mainly attributable to the IPA module, rather than the disentangled texture features themselves.\n\n2、The original IPA module applies cross-attention across multiple decoder layers, whereas the proposed method restricts injection to only the first decoder layer. It remains unclear whether this simplification affects the integrity of the generation process, potentially limiting the model’s ability to integrate style features progressively.\n\n3、The ablation study does not sufficiently isolate the effect of the proposed TextureExtraction module. It is therefore difficult to determine whether improvements in texture retention are due to this module or the inherent capabilities of IPA. More targeted experiments would help clarify this.\n\n4、How significant is the impact of the singular value modulation parameters γ (gamma) and β (beta) on the generation process? How are they set during training, and are they learnable.", "was_revised": true, "final_justification": "The proposed method, SADis, built upon the U-Net architecture, demonstrates a simple yet effective approach for controlling color and texture. However, the exploration of controllable generation within more recent and mainstream architectures such as MMDiT remains an open and promising direction. I look forward to the authors’ future work addressing this aspect.", "rebuttal": "We sincerely appreciate your constructive and insightful feedback. We are grateful for your recognition that the method is ***compatiblewith the community ecosystem***  and the ***performance is strong***. Below, we provide detailed responses to each of your specific points.\n\n\n\n\n**W1(texture-related performance gain)**: In our method, the ability to represent textual information primarily comes from the pre-trained model (i.e., IPA and SD). However, IPA injects image features into all blocks of the network, which introduces semantic leakage from the reference image and results in poor text-image alignment. In contrast, InstantStyle only injects features into style-relevant blocks, effectively avoiding the semantic leakage issue inherent in the original IPA.\nWe follow the InstantStyle injection strategy, which enables better adherence to textual semantics. As a result, the texture-related performance of our method aligns with that of InstantStyle, as shown by the CLIP-I scores in Table 1.\n\n\n\n\n**W2(supplement experiments with multiple CA layers)**: The purpose of employing multiple Cross-Attention (CA) layers in IPA is to ensure consistency in the identity of the input. Prior research has shown that different CA layers in IPA control different attributes—for example, some are more associated with content, while others relate more to style. Our decision to inject at the first decoder layer was initially inspired by InstantStyle.\nExperiments with multiple CA layers are provided below. Here, ‘b{idx0}a{idx1}’ denotes the ‘idx1’-th CA layer in the ‘idx0’-th decoder block of the denoising UNet. As shown in the table,  injecting into additional CA layers beyond the style-relevant one (i.e., the first decoder layer) negatively affects color representation, despite improvements in texture metrics. Moreover, from the visualization results, we observed that the generated images tend to contain more semantic content from the texture reference image (similar to IPA’s results shown in Fig. 5), which is inconsistent with the goal of texture transfer. This phenomenon is consistent with the findings in InstantStyle.\n\n\n\n|     Different CA layers    |     CLIP↑    |     Color (MS-SWD↓)    |     Color (C-Hist↓)    |     Color (GPT4o↑)    |     Texture (CLIP-I↑)    |     Texture (KID↓)    |\n|:--------------------------:|:------------:|:----------------------:|:----------------------:|:---------------------:|:------------------------:|:---------------------:|\n|          b0a1(ours)        |     0.301    |         6.08           |         0.95           |        3.14           |         0.751            |        0.064          |\n|          b0a0+b0a1         |     0.292    |         6.54           |        0.981           |        3.13           |         0.785            |        0.057          |\n|        b0a0+b0a1+b0a2      |     0.292    |         6.53           |        0.982           |        3.12           |         0.793            |        0.056          |\n|          b0a1+b1a0         |     0.291    |         6.55           |        0.981           |        3.11           |         0.791            |        0.057          |\n|          b0a1+b1a1         |     0.300    |         6.54           |         0.983          |        3.12           |         0.751            |        0.065          |\n|          b0a1+b1a2         |     0.297    |         7.19           |        1.005           |        3.10           |         0.753            |        0.066          |\n|            b0+b1           |     0.291    |         7.21           |        0.998           |        3.07           |         0.794            |        0.055          |\n|           b0+b1+b2         |     0.278    |         7.24           |        1.003           |        3.08           |         0.807            |        0.056          |\n\nWhen texture and color embeddings are injected solely into the style-related CA layer (i.e., the first decoder layer), the visual results show more desirable style transfer performance. We will include the corresponding visualization results in the supplementary material.\n\n\n\n\n**W3(clarification of TextureExtraction module)**: We apologize for any confusion. In our paper, the “SVD” mentioned in Table 2 and in the ablation experiments of Fig. 4 actually refers to the TextureExtraction module. We will revise the terminology accordingly in future versions.\n\n\n**W4(Ablation on γ and β)**: We conduct an ablation study on γ and β. As shown in the table below, when β increases beyond 1, the texture consistency metric (CLIP-I) improves and even surpasses that of InstantStyle (CLIP-I = 0.74), indicating enhanced texture alignment. However, this comes at the cost of decreased color consistency, as reflected by the MS-SWD and C-Hist scores. Therefore, to achieve a better balanced performance between texture and color consistency, we set the default values to β = 1 and γ = 0.003 in our main experiments.\n\n|             γ   (β=1)           |       0      |     0.001    |     0.003    |     0.005    |     0.007    |     0.009    |     0.011    |\n|:-------------------------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|\n|      Color     (MS-SWD   ↓)     |      5.70    |      5.71    |      5.57    |      5.36    |      5.22    |      5.21    |      5.20    |\n|      Color     (C-Hist   ↓)     |      1.01    |     1.017    |     0.962    |     0.939    |     0.897    |     0.887    |     0.861    |\n|     Texture     (CLIP-I   ↑)    |     0.759    |     0.755    |     0.743    |     0.736    |     0.730    |     0.723    |     0.713    |\n\n\n|           β (γ=0.003)         |     β=0.5    |     β=0.7    |     β=0.9    |      β=1     |      β=1.1    |     β=1.3    |     β=1.5    |\n|:-----------------------------:|:------------:|:------------:|:------------:|:------------:|:-------------:|:------------:|:------------:|\n|     Color     (MS-SWD   ↓)    |      5.20    |      5.28    |      5.45    |      5.57    |      5.70     |      5.92    |      6.17    |\n|     Color     (C-Hist   ↓)    |     0.890    |     0.923    |     0.949    |     0.962    |      0.973    |     0.992    |     1.011    |\n|      Texture    (CLIP-I ↑)    |     0.711    |     0.729    |     0.740    |     0.743    |      0.747    |     0.749    |     0.752    |"}]

Author rebuttal:

Meta-review: This paper receives positive ratings of (4, 4, 4, 5). The reviewers acknowledge the simplicity and tuning-free nature of the proposed method, thorough evaluation, and the presentation. Most of the concerns are addressed in the rebuttal. After reading the paper, review, and response, the AC finds no reason to overturn the decision. An acceptance is recommended. The authors should include the discussion in the rebuttal to further improve the paper.
Decision: Accept (poster)