DiffNat : Exploiting the Kurtosis Concentration Property for Image quality improvement
Authors: Aniket Roy
Maitreya Suin
Anshul Shah
Ketul Shah
Jiang Liu
Rama Chellappa
Venue: Accepted by TMLR ()

Abstract: Diffusion models have significantly advanced generative AI in terms of creating and editing natural images. However, improving the image quality of generated images is still of paramount interest. In this context, we propose a generic kurtosis concentration (KC) loss that can be readily applied to any standard diffusion model pipeline to improve image quality. Our motivation stems from the projected kurtosis concentration property of natural images, which states that natural images have nearly constant kurtosis values across different band-pass filtered versions of the image. To improve the image quality of generated images, we reduce the gap between the highest and lowest kurtosis values across the band-pass filtered versions (e.g., Discrete Wavelet Transform (DWT)) of images. In addition, we also propose a novel condition-agnostic perceptual guidance strategy during inference to further improve the quality. We validate the proposed approach  on four diverse tasks, viz., (1) personalized few-shot finetuning using text guidance, (2) unconditional image generation, (3) image super-resolution, and (4) blind face-restoration. Integrating the proposed KC loss and perceptual guidance has improved the perceptual quality in all these tasks in terms of FID, MUSIQ score, and user evaluation. Code: https://github.com/aniket004/DiffNat.git

Published in Transactions on Machine Learning Research (07/2025)
DiffNat : Exploiting the Kurtosis Concentration Property for Image Quality Improvement
Aniket Roy Johns Hopkins University
onk. roy4 @gmaill com
Maitreya Suin
m.suinn@ssmmsuugg.com com
Samsung AI Center Toronto
Anshul Shah
anshulbshoh@gmail..com
Johns Hopkins University
Ketul Shah Johns Hopkins University
kshah999@jhu. edu
Jiang Liu AMD
Jiang. Liw@and.dcoommmm com
Rama Chellappa Johns Hopkins University
rchella4 @jhu. edu
Reviewed on OpenReview: https: //::::/opppp openreview. net/ forum? Hd207pMPR4
Abstract
Diffusion models have significantly advanced generative AT in terms of creating and editing natural images. However, improving the image quality of generated images is still of paramount interest. In this context, propose a generic kurtosis concentration (KC) loss that can be readily applied to any standard diffusion model pipeline to improve image quality. Our motivation stems from the projected kurtosis concentration property of natural images, which states that natural images have nearly constant kurtosis values across different band-pass filtered versions of the image. To improve the image quality of generated images, we reduce the gap between the highest and lowest kurtosis values across the band-pass filtered versions (e.g., Discrete Wavelet Transform (DWT)) of images. In addition, we also propose a novel condition-agnostie perceptual guidance strategy during inference to further improve the quality. We validate the proposed approach four diverse tasks, viz., (1) personalized few-shot finetuning using text guidance, (2) unconditional image generation, (3) image super-resolution, and (4) blind face-restoration. Integrating the proposed KC loss and perceptual guidance has improved the perceptual quality in all these tasks in terms of FID, MUSIQ score, and user evaluation. Code: /stps://github cwwcco//aaaaaaaa commaaalet004/iifllatttt git.
1 Introduction
Multi-modal generative AI has advanced by leaps and bounds with the advent of the diffusion model. Large-scale text-to-image diffusion models, e.g., DALLE Ramesh et al. 2022 Stable-diffusion Rombach et al. 2202)) synthesize high-quality images in diverse scenes, views, and lighting conditions from text prompts. These models generate high-quality and diverse images since they have been trained on large collection of image-text text pairs and can capture the visual-semantic correspondence effectively. While diffusion models generate images that appear highly realistic, recent studies have demonstrated that these images can still be distinguished from natural ones using advanced image forensic tools Corvi et al. (2023). This suggests that although state-of-the-art generative models excel tasks like image editing, they often leave behind

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Published in Transactions on Machine Learning Research (07 (07/2025)
Dreambooth
290
Kurtosis Concentration (KC) Property
200
150
Kurtosis
Kurtoeis values
DWT
Number of components
Lower quality image
100
50
Dreambooth KC loss
Natural image
Number components
Kurtoels
250
100
50
Proposed KC
Plug and Play
Better Quality
Loss
Number components
quality image
Figure 1: Overview of DifINat: Natural images exhibit consistent kurtosis values across bandpass-filtered versions, shown by the tight blue box in the left figure. Diffusion-generate images (top right) show greater kurtosis spread, indicating lower quality. Adding KC loss (bottom right) reduces kurtosis variance and improves image quality.
subtle, unnatural artifacts. Ensuring high image quality is therefore critical for various generative tasks, such as personalized few-shot finetuning Ruiz et al. (2022): Kumari et al. (002)) super-resolution Karras et al. 2022 Dhariwal Nichol (2021), image restoration, and unconditional image generation.
Our goal is improve the image quality using natural image statistics by exploiting the well-known kurtosis ratcon property of natural images Zhang & Lyu (0144); Zoran & Weiss (2009); Wainwright & Simoncelli (1999). This property states that natural images have nearly constant kurtosis (fourth order moment) values across different band-pass (e.g., Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)) versions of the images Zhang & Lyu (2014). Inspired by this property, we propose a novel kurtosis concentration (KC) loss, which is generic and applicable to any diffusion-based pipeline. More specifically, this loss minimizes the gap the kurtosis of an image across band- pannddppssssss filtered versions and improves the quality of the generated images. We also propose novel perceptual guidance (PG) strategy during inference which is agnostic to conditioning (e.g,, text/class) and further improves image quality. Both KC loss and PG strategies general-purpose and do not require any labels. It can be adapted to various generative tasks with minimal effort. In this work, we experiment with diverse tasks: (1) personalized few-shot finetuning of text-to-ing text-to-image diffusion model, (2) unconditional image generation, (3) image resolttonolutio,, and (4) blind foco-restoration.
Our major contributions ions are as follows:
We introduce DiffNat
framework for improving the image quality of diffusion models using the kurtosis concentration property of natural images.
We provide insights how reducing kurtosis improves image quality. This is the primary motivation for the proposed loss function.
A novel condition-agnostic perceptual guidance strategy is proposed which further improves image quality.
We validate the proposed KC loss and PG strategy on diverse generative tasks, e.g., (1) personalized few-shot finetuning of text-to-image diffusion model using text guidance, (2) unconditional image generation, (3) image super-resolution, and (4) blind face-restoration. Experiments indicate that incorporating the proposed KC loss and PG enhances perceptual quality across various tasks and benchmarks, and this improvement has been validated through a user study.
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2
Related Work
Generative models. Recent progress in generating high-fidelity, diverse images from text inputs has been
remarkable. Initially, GAN-based methods dominated text-lo-image generation Qiao et al. (2019); TTao el al. (2022); Liao al. (2022); Zhu al. (2019); Ruan et al. (2021); Dhar al. (2021), but recent advances
have shifted towards diffusion models like Stable Diffusion Rombach et al. (2022) and Imagen Saharia et al.
22022. which leverage large datasets for training. Text-based image editing has also advanced significantly;
GAN- approaches have improved with CLIP Radford et al. (2021) while diflusion-based methods offer
better control and impressive results Ruiz et al. (2022); Kumari et al.
(2022); 1 Gal et al. (2022); Pal et al.
(2024b a): Roy et al. (2025b a 2024 2022): Pramanick et al. (2022].
Personalization techniques such as Textual Inversion Gal et al. (2022), DreamBooth Ruiz et al. (2022), and Custom Diffusion KKuaari al.
2022) allow for the crcation of unique images by embedding subjects or concepts into the modcl's output.
unconditional image generation, the Denoised Diffusion Probabilistic Model (DDPM) Ho et al. (2020) is leading method, providing superior image quality through variational inference and image-space denoising. For conditional tasks like image super-resolution, guided diffusion Dhariwal & Nichol (2021) and latent
diffusion models Karras et al. 2022 are highly effective, producing high-resolution images from low-resolution inputs.
Natural Image Statistics. Natural images have interesting scale-invariance and noise propertics Zoran & Weiss (2009), which have been used for image restoration problems Roy et al. (2015 2018 2020 2019 2017); Tariang et al. 22017: 2019). Projected KC property of natural images, i.e., natural images tend to have constant kurtosis values across different band-pass (DCT, DWT) filtered versions has been used for blind forgery detection Zhang & Lyu (2014). Inspired by these observations, we propose a novel loss function based on natural image statistios for generating better quality images.
3 Method
In this section, we present the concept of KC loss (Sec. 3.2) which can be applied various generative tasks for
leplace, - 3.224 uniform, -1.206
enhancing the quality of generated images. We start by
0.4
providing basic understanding of the KC property of natural images.
Probability
0.3
0.2
3.1 Kurtosis Concentration Property
0.1
Definition 1 Kurtosis: It measure of the "peakedness" "
of the probability distribution of a random voriable Zhang 8 Lyn (2014)- For random variable x, its kurtosis is defined as,
0.0
-5.0
-2.5
0.0
2.5
5.0
Values
Figure 2: Kurtosis of various distributions where
<(x) = Ma(x) 3.
(1) kurtosis captures the peakeduess of distributions.
(x)
where 0²(x) = Ez((x - (R) (x)) and pa(x) = Ex((a
Ex (x)) are the second order and fourth order moment of x. For ezomple, the Gaussian random variable has kurtosis value of 0.
Kurtosis of well-known distributions is shown Fig. 2 We can observe that a positive kurtosis indicates that the distribution is more peaked than the normal distribution and negative kurtosis indicates it to be less peaked than normal distribution Zhang & Lyu (2014). Kurtosis is useful statistic used for blind source separation Naik et al. (2014) and independent component analysis (ICA) Stone (2002).
For a random vector v, we define the kurtosis of the ID projection of x onto a unit vector to as a projection kurtosis, ie., x(wTx) z). Projection kurtosis is an effective measure of the statistical properties high- dimensional data. E.g., if is a Gaussian, its projection over any w has a 1D Gaussian distribution. Therefore, its projection kurtosis is always zero, which exhibits the kurtosis concentration (to a single value, i.c., zero) of Gaussian.
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It is well-known that natural images can be modeled using zero-mean Gaussian Scale Mixture (GSM) vector Zoran & Weiss (2009); Zhang & Lyu (2014): Lyu al. (2014); Wainwright & Simoncelli 1999 Next,
we analyze an interesting property of the GSM vector.
Lemma 1 A GSM vector x with zero meon has the following probobility density function:
p(x) = / 20x
(2)
and its projection kurtosis is constant with respect to the projection direction w, i.e.,
<(w" x) = 3var
(3)
{
where (z; (Eae) denotes Gaussian distribution with zero mean and covariance matrix zLn,, with positive random variable with density (z(2). {z{2} and [ar= are the meon and variance of latent variable respectively.
Proof. The proof is provided in the Appendix.
This result by Zhang & Lyu (2014) shows that projection kurtosis is constant across projection directions (e.g., wavelet basis), which provides theoretical insights into the KC property, which we will discuss next.
Kurtosis Concentration Property: It has been observed that for natural images, projection kurtosis values across different bandpass-filten filtered channels tend to be close to a constant value. This termed as KC property of natural images [Zhang Lyu (2014); Zoran & Weiss (2009); Lyu et al. (2014); |Bethge| (2006) Lyu & Simoncelli (2009); Wainwright Simoncelli (1999). It can also be interpreted as an implication of Lemma
1. if we consider patches of natural images zero-mean GSM vector (Zoran & Weiss (2009). Wainwright &
Simoncelli [199]] and projection directions correspond to bandpass filters, e.g., DWT.
One intuitive reasoning of the projected KC property is given as follows. It is observed that the distribution (p(2, ot (8)) of different bandpass (DWT) filtered versions of natural images follows a generalized Gaussian
density of the form Zhang & Lyu 2014 Zoran & Weiss 2009
33 exp(
a
(4)
(1I(1/3)
where o, 3 are scaling parameters and T()) is the Gamma function. The kurtosis of this function is given by Zoran & Weiss (2099).
K ((1/3)T(5/3)
T(3/3)2
Empirically, it has been shown that for natural images, 3 takes relatively small values, ranging from 0.5 to Zoran & Weiss (2009). and this kurtosis value tends to be constant Zhang & Lyu (2014): Zoran & Weiss (2009)
Lyu et al. (2014); Wainwright & Simoncelli 1999), independent of a or
We investigate and experimentally verify this property for natural images on large datasets, e.g., FFHQ dataset (Fig. [c)), Dreambooth dataset, seford-flowers dataset (in Appendix). We conclude that this property actually holds for both object datasets (Dreambooth datasot, Oxford flowers), face dataset (FFHQ) with sufficient variations in viewpoint, scale, illumination, color, objects, posc, lighting condition etc. Analysis of kurtosis difference has been shown in Fig. g and also in the Appendix, which clearly shows that the difference of kurtosis values are higher in anffusion-geeerate images compared to natural images in these datasets.
3.2 Kurtosis Concentration (KC) Loss
In this work, we introduce a novel KC loss function for training deep generative models, leveraging the KC property of natural images to improve perceptual quality. Unlike previous approaches Zhang & Lyu (0144)
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Task-specific additional input
Task
Images:
DWT
Cn
Text-to-lmage Dreambooth
Approach specific
Text : "A cat"
Custom diffusion
task loss (Lrask)
C2
Image-to-Imas
Input images
Reconstructed Images
Guided diffusion
k-diffusion
Unconditional
DDPM
Lxc = = -min(x(s))
KC (Lxc)
Reconstruction loss (Lrsom)
Figure 3: Overview of DiffNat. The proposed KC loss can be integrated into any ceffusiio-bbaede approach for various lasks (e.g., text-to-image generation (DreamBooth,, Custom diffusion)), super-resolution image-to-inage generation (Guided diffusion, k-diffusion), unconditional image generation (DDPM)). In addition to the lask-specific losses, and general reconstruction loss, we incorporate the KC loss (Ixe), which operates on the reconstructed images and minimizes the kurtosis deviation (i.e., max(x(c.) min(ss(cH]) across Discrete Wavelet Transform (DWT) filtered version the reconstructed image, Here, Cı, c2 are DWT filtered version of the reconstructed image and k(x) denote kurtosis of z.
that used the KC property for tasks like noise estimation and source separation, our KC loss can be integrated into any diffusion pipeline, and we validate its effectiveness with state-of-the-an diffusion models.
Suppose we need to train finetune diffusion model fo using input training images (ar) with or without conditioning vector The conditioning vector could be text, image, none (in the case of the unconditional diffusion model). Given an initial noise map ~ (O, and a conditioning vector c, the generated image obtained from fe is given by Egen = fe(æ, C, c). Typically, the diffusion model is trained to minimize the 12 distance between the ground truth image (Ir) and the noisy image (Igen) Dhariwal & Nichol (2021) or their corresponding latent. case of Latent Diff'usion Model (LDM) Rombach et al. (2022). Without loss of (Lrecon) between the ground-truth image (z) and
generality, we are referring to that as reconstruction loss the generated image (Zigen,, denoted by, Lyecon Ez,e,el |[-gen - 2
(6)
Note that for diffusion models trained to predict the added noise, we could deterministically obtain the intermediate clean image from the predicted noise and apply the loss to that. Next, we will describe the KC loss. Note, that the KC property holds across different bandpass transformed domains (DCT, DWT, fastICA) and we choose DWT because it is widely used due to its hierarchical structure and energy compaction propertics E Woods & C Gonzalez (2008). Typically, DWT transforms images into LL (low-low), LH (low-high), HL (high-low), HH (high-high) Trequency bands and each of the sub-bards contains several sparse details of the image. E.g., the LL and HH subbands contain a low-pass and high-pass filtered version of the image, respectively Zhang & Lyu (2014). The generated image Egen is then transformed using Discrete Wavelet Transform (DWT) with kernels k1, k2,.., kn producing filtered images Igen,1 Igen.2: Igen,n respectively, such that, Igen, FR. (Fgen). Here, Fj denotes the discrete wavelet, transform with kernel I.
Now, kurtosis values of these Igen, should be constant by the KC property, therefore, we minimize the difference between the maximum and minimum values of the kurtosis 99en, to finetune the model using the loss,
LKC Em,,, (max((((ggenn))
Here, k(x) is kurtosis of x. Note that, this loss (Algorithm. 1 is quite generic and can be applied to both image or latent diffusion models for training. In the case of latent diffusion models, we need to transform the latent to image space (via a pretrained VQVAE), before applying this loss, since this prior holds for image space only. At each timestep (t), we extract the clean image (#x) from the noisy latents (4) (zo (z, o,.4)/ar) and apply KC loss exclusively to the clean image, not the noisy one. In case of applying this loss to any task
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T (DreamBooth, super-resolution, unconditional image generation), the overall loss (L) function would be,
L= Itask Lyecon + L,C,, where Trask is the task-specific loss.
Algorithm 1: Kurtosis Concentration loss
Input: Diffusion model (fo), training images (I), condition vector (c)
Output: KC loss L.c.
1. E 1
// Sample random noise
2. Zgen = (z,
// Generate image // Wavelet decomposed images
3. Igen. 9.enn,. 39..,3)... (FFg)))))
4. LKc Br,,, ( [max(( (9geen))))) min (nggen,
// Compute the KC loss
Algorithm 2: Parceptual Guidance
Input: Base diffusion model (0B), Diffusion model trained with KC (0p), prompt (c), guidance scale (7) Output: output image (zo)
2T = =0(0.1 for in T, T-1, do
E (It, e, t) = cgn (Ie, + + (cpp - con (x),
if t
then
It-1 (N(()))) Et)
else
To = (21 - /a
end
end return 20
3.3
Perceptual Guidance during Inference
Applying the KC loss during diffusion training enhances sample quality compared to vanilla diffusion. Additionally, we propose novel Perceptual Guidance (PG) mechanism during inference to further improve perceptual quality. Conceptually, this is analogous to classifier-fret guidance, where the diffusson model is evaluated both conditionally and unconditionally at each step, and the difference in their outputs serves a gradient direction toward the condition. However, while classifier-free guidance can indirectly improve percepl.ual quality, it has notable limitations, i.e., (1) it restricted to conditional models, and (2) its primary goal is alignment with the condition, which does not necessarily guarantee improved perceptual quality. In contrast, our PG mochanism generates two outputs with differing perceptual qualitics uusiit two models, one trained with KC loss and the other without and amplisies the intermediate output at each step toward the model with better perceptual quality. This approach offers key advantages (1) it is condition-aguostcc making it more versatile, (2) it can operate alongside traditional classifier-free guidance, complementing its functionality (Tab. 11
Diffusion models typically generate images unconditionally by approximating the true data distribution g(xg) with (oo)). However, many applications require conditioning on labels or text prompts Dhariwal & Nichol (221). From the Bayesian perspective, the conditional score can be expressed as:
V log pl c) = v. log ((r) + V logp(c x))
(8)
Given ca( f) (== -o V., logp(z;)) is a model trained to predict the noise added to sample, the above equation can be approximated as,
éo (21,, c,t) co(o, as Va, log Po(e) xx)
Classifier guidance Dhariwal & Nichol (2021) moets this need by introducing an auxiliary model po xx) and uses the modified score function é, which, while effective, requires training robust classifier for all denoising steps challenging and resource-intensive task.

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To avoid this, classifier-free guidance (CFG) Ho & Salimans (2022) uses single neural network to parameterize
both the conditional and unconditional model. Ho & Salimans (2022) incorporates e directly into the denoising
network, training it to predict both the conditional score eg (2, t) and the unconditional score eœ(2,, t) by
randomly dropping the prompt during training. The resulting conditional gradient,
(ol(E,, - es(xe.
(10)
leads to updated diffusion score (putting Eq. 10 in Eq. 9
(0, co(x),)) + (Fo(o2, co( 1))
(11)
where y is a constant guidance scale.
With similar intuition, PG refines the denoising trajectory in diffusion models by integrating perceptual prior during inference. Instead of calculating class/tet--oonditinaal unconditional scores, calculate scores corresponding to high or low perceplual quality. In PG, we redefine the condition as indicator function based on the presence of KC loss, d' = =kc. We can reformulate Eq. 8 ass
V p0 | = 1kc, c) = Va logp(()) + (Vz log | d' = 1kc c) - V. logp(z))
(12)
We derive the guiding gradient from the difference between models trained with and w/o KC loss (representing conditional and unconditional predictions). Suppose the baseline diffusion model is denoted by OB, and the model trained with KC loss is denoted by 0p (i.e., if c' True, then I 0p, else A = 0g). During perceptual guidance, we guide the diffusion process through the difference of output predicted by 0B and 0p as follows,
0r c, = con (2,, + 7 (Fe, c, c (Ie:
As result, PG introduces a perceptual-opttmmzzed (using KC) denoiser where 7 > enhances perceptual quality. Note that, here e could be a lext prompt or NULL. This process is iterated T times to generate the final sample shown in Algorithm 2 A concurrent work on outo-guidance Karras et al. (2024) improves image quality by guiding diffusion models using a weaker version of itsclf. Instead, PG leverages the difference between models trained with and without KC loss for guidance (Tab. 10
3.4 Intuitive Justification
Why do diffusion-generateed images have higher kurtosis values? Natural images typically exhibit smooth transitions and structured patterns, leading to a pixel intensity distribution with fewer outliers and, consequently, lower kurtosis Zoran & Weiss (2009); Lyu et al. (2014): Wainwright & Simoncelli (1999); Bethge (2006). In contrast, diffusion models gencrate images through iterative refinement pure noise into coherent structures. Due to imperfections in the trained UNet and finite denoising steps, residual high-frequency noise may persist the final output, leading to more extreme pixel intensity values and contributing heavier tails in the distribution Zhang al. (2023). This phenomenon is more effectively characterized in the frequency domain (wavelet transform), which generally correspond to higher kurtosis values Zhang & Lyu (014) (Fig. Dh.
How does KC loss improve image quality? Intuitively, minimizing the KC loss can be seen as a locality-aware smoothing of the data distribution to enhance perceptual quality. For instance, in Fig. [6] the output of GD without KC loss exhibits undesirable abrupt changes near the eye region. Ideally, we should first detect such regions and then apply suitable operations to improve perceptual quality. However, performing a spatially varying refinement. is challenging, and globally applying such filters might be sub-optimal for other regions. It has been observed that the wavelet coefficients of natural images follow Generalized Gaussian density Moulin & Liu (1999): Sharifi & Lcon-Garcia (1995): Mallat (1989), and Kurtosis quantifies the heaviness of the tails and peakedness of distribution compared to the Gaussian distribution Zhang & Lyu (2014): Maier (2021). Therefore, kurtosis across bandpass wavelet filtered versions of the image automatically provides locality of the abrupt changes and minimizing KC loss performs locality-aware smoothing of the data distribution. In Fig. incorporating KC loss enhances the generated eye region.
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Input
DB
DB+KC
DB+KC+PG
teapot
"<V> teapot on top of wood"
<V> backpack
"<V> backpack in the jungle"
Figure 4: Qualitative comparison of with/withan KC loss and PG in DreamBooth (DB). The quality of teapot image (top row) improves while adding KC and PG. In the bottom row, the background of DB generated images looks blurry and unnatural (highlighted in red), while adding KC and PG improves image quality, and the background look more natural.
4 Experiments
We evaluate the efficacy of the proposed loss for four tasks - (1) personalized few-shot finctuning of diffusion model using text guidance, (2) unconditional image generation, (3) image super-resolution, and (4) blind face-restoration.
4.1 Task 1: Personalized few-shot finetuning using text guidance
In this section, address the problem of finetuning the text-to-image diffusion model from few examples for text-guided image generation in subject-driven manner. Specifically, given only few images (e.g., 3-5) of a particular subject without any textual description, our task is to learn the subject-specific details and generate new images of that particular subject in different conditions specified by the text prompt. To evaluate the efficacy of KC loss for this task, we build upon methods, (1) DreamBooth Ruiz et al. 2022)). (2) Custom diffusion Kumari et al. (2022), and (3) AltnDreambooth (AttiDB) Pang et al. (2024). We evaluate all approaches with/wer without KC loss on the DreamBooth dataset with same setup for a fair comparison. We have also compared with another naturalness loss, i.c., LPIPS loss Zhang et al. (2018) loss, decompose the reconstructed images using 27 'Daubechies' filter banks and get the average difference of the kurtosis values as loss function. When adding the proposed KC loss to these approaches, we obtain performance improvements in visual quality, i.e., FID Lucic et al. (2018). MUSIQ score Ke et al. (2021) b. HPSv2 Wu et al. (2023), subject/pree pprompt fidelity metrics (DINO, CLIP-I, CLIP-T) and diieS-diversity Zhang et al. (018)) as shown in Tab. 1 The qualitative results are shown Fig. Tn practice, better perceptual
bascline. For KC
quality often correlates with higher semantic fidelity Dhariwal & Nichol (2021 g Ramesh et al. (2022), as
both improvements are driven by more accurate and coherent image generation that captures the intended high-level semanties of the prompt, verified in Tab. | More training details and ablation are provided in the
Appendix.
Human evaluation. Since perceptual metrics are not always reliable, we also conducted a human preference study using Amazon Mechanical Turk (AMT) for (1) subject fidelity assessment and (2) image quality ranking. For the subject fidclity assessment, we evaluated the visual similarity of real and generated images, both with and without KC loss. We asked around 5000 visual similarity questions 50 unbiased users (age 20-50, randomized gender, AMT). The average rating was 5.8 (on scale where 0 is "extremely unlikely" and
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Table 1: Comparison on personalized few-shot finetuning task
Method
Image quality
Subject fidelity
Prompt fidelity
Image diversity
FID
MUSIQ
HPPv2
DINO
CLIP-1
CLIP-T
LPIPS-dIv
DB
25.12
0.38
DB
Rai 0t
1PIPS
108
68.35
25.43
0.63
0.80
0.92
0.40
DB
KC
100.08
69.78
25.82
0.08
0.84
0.34
0.42
DB
KC Iose
PC (Qurs)
93.45
70.82
26.04
0.20
0.86
0.35
0.48
CD
0.71
CD
Kumaa zt ml
4022
1PPP88
80:12
70:56
26.33
0.71
0.87
0.37
0.43
CD
KC Ioes
75.68
72.22
26.64
0.73
0.88
0.40
0.44
CD
+ KC Irea
PC [Ouns)
66.27
73.77
27.10
0.27
0.89
0.48
0.46
[aan
AttoDis
Frang di alr
2124
LPIPS
80.06
70:50
26.66
0.73
0.87
0.95
0.43
AttaDB
BCC
(Curs)
73.02
71:10
26.83
0.75
0.59
0.37
0.45
AttoDB
KCC Inees
PG (Ours)
64.78
72.89
27.35
0.28
0.90
0.38
0.47
DDPM
DDPM KC
DDPM KC PG
Figure 5: Comparison of unconditional image generation (DDPM) with without KC loss. Integrating KC loss significantly improve image quality, whereas DDPM generated images have unnatural image artifacts.
"extremely likely"), indicating our proposed loss retains subject fidelity in most cases. We also had 50 unbiased users rank our method against baselines (i.e., "DiffNat", " "DreamBooth", "Custom diffusion", "None is satisfactory"), totaling 1500 questionnaires. The aggregate responses showed that DislNat-generated images significantly outperformed the baselines by large margin (50.4%). Further details provided in the Appendix.
4.2 Task 2: Unconditional image generation
Unconditional image generation opcrates without the need for text or image guidance. aims to learn the training data distribution through generative model (in this case, a diffusion model) and produce samples that resemble the training data distribution. We opted for the well-known unconditional image generation pipcline, the DDPM Ho et al. (2020), to test the efficacy of KC loss. PG is especially effective here because classifier-free guidance cannot be applied.
In DDPM, we directly integrate the KC loss into the image space, demonstrating the flexibility of our proposed loss. We experimented with the Oxford-flowers Nilsback & Zisserman (2006), CelebA-faces Zhang
al. 2020). CelebAHQ Karras et al. (2017), Stanford-Dogs Khosla al. (2011) and Stanford-Can Krause al. 2013) datasets, achieving consistent improvements in image quality, shown in Tab. 2 and Figure
Additionally, PG further enhances image quality, as indicated in Tab. 2 Human evaluation is not feasible for unconditional image generation due to the lack of one-to-one correspondence between training and generated images, but quantitative and qualitative analyses demonstrate the effectiveness of our approach.
Table 2: Comparison of unconditional image generation task
Method
Oxford flowers
Celeb-Laces
CelebAHQ
Stanford-
Stanford-Cars
FID 1
MUSIQ
FID
MUSIQ
FID 1
MUSIQ
FID
MUSIQ
FID
MUSIQ
DDPM
Ho
202..67
190.77
46.05
129.91
50.12
143.71
33.77
DDPM
1o at
AP2D
LPIPS
242.02
20.50
201.55
19:21
197.17
46.15
115.72
50.96
137.22
$3.98
DDPM
237.73
21.13
195.2 25
19.52
190.58
46.83
105.45
51.53
125.85
54.21
DDPM
KC
PG (Ours)
200.12
22.45
188.49
20.82
175.12
48.32
98.77
52.17
115.93
54.85

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Published in Transactions on Machine Learning Research (07 (07/2025)
Input
GD
GD + KC
GD KC + PG
GT
Figure 6: Qualitative comparison of with/without wwihhutt KC loss, PG in guided diffusion (GD). The bottom image (with KC loss) has better eye details (best viewed in color).
Table 3: Comparison of image super-resolution (x4) task
Image quality
Image Diversity
FID
PSNR
SSIM
MUSIQ
LPIPS-div
GD Diariead Nichol 2721
121.25
18.13
0.54
57.31
0.37
KC Xoe (Uuz)
104.19
18.52
0.55
58.01
11:42
GD
KC Joes PG (Ours)
93.45
20.17
0.58
60.13
0.45
10 Ravaat ds mk 1144 LPIPS
82:77
19:42
0.57
59.82
11:00
LD XC 1os (Uui))
83.34
20:25
0.58
61.21
0.42
KC PG (Ouns
62.86
4.3 Task 3: Image super-resolution
Image super-reson resouttesol typically takes the form of a conditional generation task, leveraging a low-resolution image as an additional condition for the diffusion model. In this study, we use two statc-of-the-art diffusion pipelines as baselines for comparison. Guided diffusion (GD) Dhariwal & Nichol (2021) directly takes the low-resolution image as condition and performs the diffuusion operation in the pixel space. Additionally, also explore the latent diffusion model (LD) Karras et al. (022) that operates in the latent space of a pre-trained VQVAE Esser et al. 22021)
Note that, as GD operates in the pixel space, we directly add the proposed KC loss to the output of the denoising UNet. Conversely, for LD, we initially convert the latent embedding to image space using the pre-trained decoder and integrate the KC loss on the output of the decoder. For training, we use the standard FFHQ dataset Karras et al. (2017), which contains 70k high-quality images. We address the task of x2, x4, and x8 super-resolution where the GT images 3000 images from CelebA-Test dataset Karras et al. (2017) under the same x2, x4 and x8-SR setting in Tab. 1 Tab. and Tab. respectively. In the qualitative results shown in Fig. 6 we observe that adding KC loss improves the image quality and finer details, e.g., eye structure, texture, and lighting.
of resolution 256 256. We evaluate randomly sampled
Human evaluation. We conduct a human evaluation of the image super-resolution task to compare GD and LD with the addition of KC loss to each counterpart (DiffNat). The aggregate response of choices (corresponding best quality images w.r.t methods) from 50 unbiased users (age 20-50, randomized gender,
Table 4: Image super-resolution (x2) task
Table 5: Image super-resolution (x8) task
Method
Image quality
Method
Image quality
FID 1 PSNIL
551M
LPIPS-div
MUSIQ
LD RC bea PC (Dua)
Daatee
Kamar
k0 PG (Ums)
1000
50.32
70.13
67.13
71.1
50.0
23.0
21.7
223
21.9
0.68
0.73
6.70
0.15
0.31
60.32
63.75
00.23
62:15
18.91
RC loe PC (Cun)
PC
109.6
105.
156.5
07.3
0.69
0.44
68
01.87
07.81
16
(031
10

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Published in Transactions on Machine Learning Research (07 (07/2025)
Input
IPC
IPC+KC
IPC+KC+PG
GT
Figure 7: Restoration comparison with TPC. KC and PG improves image quality (highlighted in green).
AMT) across 1000 questionnaires, shown in Fig. indicates that DifNNat-generated images have superior quality compared to the GD and LD basclines.
4.4 Task 4: Blind face restoration
We also verify the efficacy of KC loss and PG for blind face restoration task. For blind face restoration task Suin et al. (2024), train on FFHQ dataset with and without KC loss IPC baseline Suin al (2024), and evaluate on a subset of Celeb-A test set with a resolution of 256x256. Average LPIPS, FID, IDS, PSNR, SSTM are reported in Tab. 18 Qualitative results (Fig. 28 also verify that adding KC loss and PG improves the image quality.
Table 7: Loss & guidance ablation on DB (SD-1.5)
Table 8: Loss & guidance ablation on DB (SDXL)
Frequency
150
KC
CPG
PG
FID
MUSIQ
KC
CFG
PG
FID
MIISIQ
100
56
67.11
102.32
70.15
50
BiffNat
GD/LD
None
100,08
19.77
30.75
72332
98.21
70.02
73.93
72.85
Figure Human evaluation for im- age super-resolution task.
93.45
70.82
70.18
73.02
Table Face restoration
5 Ablation and analysis
Method
LPIPS-div?
FIDJ
PSNR
S8IM
DifFace
0.20
70.60
22.82
0.61
Ablation of loss and guidance. Here we perform ablation studies of diffusion backbone (SD-1.5, SDXL),
RestaraFormer
0.29
60.98
0.39
21.77
0.53
IPC (WACV"2))
65.42
0.34
45.21
21.10
0.61
KC loss and PG as shown in Tab. 7 g on DreamBooth dataset. We observe that. PG is complementary to classifier-free guidance (CFG) and both KC loss and PG
IPC RC
PG
0.38
38.25
0.65
25.71
0.69
improve image quality as shown in Fig. 4 and Fig. G We have also performed ablations w.r.t transforms (DCT, DWT) and the results provided in the Appendix.
Kurtosis analysis. To verify the efficacy of the proposed KC loss, performed average kurtosis analysis by computing the average kurtosis deviation of
Table 9: Comparison of real
DWT-filtred images from the FFHQ dalaset and plotting the results in Fig. g
syuthetic detection
The analysis showed that images generated with GD had the highest kurtosis deviation (Fig. g (a)), while natural images had the least deviation (Fig. (c)),
Method
Accuracy
DB
93.33%
and adding KC loss reduced the kurtosis deviation (Fig g (b)), thus improving image quality demonstrated both qualitatively and quantitatively.
DB
CD
94.16 %
CD KC lass
92.50%
11

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Published in Transactions on Machine Learning Research (07 (025)
250
250
250
Kurtosis values
Kurtosis values
Kurtosis values
200
200
200
150
150
150
100
100
100
50
50
50
Number of components
Number of components
Number of components
(a) Avg. K of GD generated images
(b) Avg. of images with GD + KC
(c) Avg. K of Natural images
Figure Average kurtosis (x) analysis of guided diffusion (GD) framework trained on FFHQ dataset. From this analysis, it is evident that GD-generated images have higher kurtosis deviation. Integrating KC loss reduces the kurtosis deviation to preserve the naturalness of the generated images. Natural images have more concentrated kurlosis values.
Comparison of real synthetic detection. To analyze the robust.ness of the proposed KC loss, train classifier to distinguish real images from synthetic diffusion models, including those with and without KC loss. The results in Tab. g show that adding KC loss decreased the real vs synthetic classification accuracy, indicating that the generated images with KC loss have higher perceptual quality and appear more natural to both human viewers and machine algorithms.
generated by
Perceptual artifact analysis. Zhang et al. 2023 identified perceptual artifacts in diffusion--enerateed images, which adversely impact image quality, and developed dataset and metric (Perceptual Artifacts Ratio, PAR) automate artifact localization /editing.. Our analysis shows that incorporating KC loss reduces these perceptual artifacts (Fig. 10), evidenced by decrease in average PAR (Table. 12 demonstrating that KC loss inherently enhances image quality by minimising artifacts.
Computational cost. The time and space complexity of CFG and PG for SDXL in A5000 machine (single image inferenco) are presented in Tab. 10 We obscrve that while PG incurs a bit higher compute overhead than CFG, as both require two forward passes, it achieves greater quality improvement (on Dreambooth (DB) task, DB dataset) with minimal memory overhead due to optimized attention in Diffusers.
Comparison with recent SD model. We ablate KC and PG on SOTA models like SDXL (Tab. 11 and FLUX (Tab. 11 for DB task, observing consistent gains. However, the improvements are more pronounced in pixel-space (Tab.
Table 10: Complexity
Metries
w/o guidance
CFG
PG
PG CFG
GPU mem. (MB)
12167
12218
12200
1250I
Inference time
14.9
FID
102.32
05.32
73.33
70.08
models like GD Dhariwal & Nichol 2021))
MUSIQ
70.8
7135
72.88
75.22
Table 11: Ablation on DB task, DB dataset for FLUX SDXL
KG
CFG
FID
MUSIQ
LIQE
Q-align
FID
MUSIQ
LIQE
Q-align
X
X
60.13
15.24
7.20
4.37
112.32
70.18
4.32
1.85
55.43
76.98
701.92
X
76.33
4.73
71.35
2.52
x
/
50.42
76.72
7.97
4.96
89.72
71.87
5.37
2.41
77.56
72.11
5.63
72.92
X
20.21
78.51
8.90
3.82
73.33
72. 88
6.55
3.12
36.96
78.85
0.13
6.04
70.08
73.02
6.82
3.55
Limitations. The proposed PG strategy necessitates two forward passes through the diffusson model to
obtain the guidance direction, which timo-consuming. We aim to address this issue in future work.
6
Conclusion
Table 12: PAR analysis lasks DB, CD, DDPM on Oxford flowers (OF), DDPM on CelebFaces (CF), DDPM on CelebAHQ (CelebHQ), GD on FFHQ, LD on FFHQ has been reported. (in %)
While diffusion models have made significant strides in generating nat- uralistic images, enhancing image quality remains key focus. We in- troduce a novel and generic KC loss, leveraging the KC property of natural images, which minimizes the gap between maximum and minimum
Setting
DB
CD
DDPM (OF)
DDPM (CF)
DDPM (CelebHQ)
GD
LD
w/o KC loes
1.61
0.63
7.09
1.11
0.75
0.38
2.99
6.97
2.63
0.51
1.07
12

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Published in Transactions on Machine Learning Research (07/2025)
(a) GD
(b) GD + KC
(c) LD
(d) LD + KC
Figure 10: Perceptual artifact ratio analysis. Green boundaries localizes perceptual artifacts. Adding KC loss reduces such artifacts
kurtosis values across different DWT-filtered versions of the image. Additionally, we propose condition- agnostic PG stralegy to further improve image quality. Our experiments show that KC loss and PG improve image quality in various generative tasks, including personalized few-shot fine-tuning of text-to-image models, unconditional image generation, image super-resolution, and blind face-restoration. Human evaluations validate the effectiveness of our approach.
7
Acknowledgement
Aniket Roy and Rama Chellappa acknowledge support through a fellowship from JHU + Amazon Initiative for Interactive AT (AT2AT) and a ONR MURT grant N00014-20-1-4
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A
Appendix
In this supplementary material, we will provide the following details.
Training details.
2. Thcorctical justification.
3. KC loss added as regularizer.
4. Additional Ablations.
Failure cases.
6. Kurtosis analysis.
Computational complexity.
8. Convergence analysis.
Qualitative analysis.
10. Experiments on image super-resolutio.
11. Experiments on other tasks.
B Training Details
The training details of finetuning the diffusion model for various tasks are provided here. For personalized few-shot finetuning, we consider two methods Dreambooth Ruiz et al.) (2022) and Custom diffusion Kumari et al. (2022). For fair comparison, applied both the approaches the dataset and setting introduced by Dreambooth. The dataset contains 30 subjects (c.g., backpack, stuffed animal, dogs, cats, sunglassos, cartoons elc) and 25 prompls including 20 re-contextualization prompts and 5 properly modification prompts. DINO, which is the average pairwise cosine similarity between the VIT-S/16 DINO embeddings Caron et al. (2021] of the generated and real images. (2) CLIP-I, i.c., the average pairwise cosine similarity between CLIP Radford et al. (2015) embeddings of the generated and real images. measure the prompt fidelity, we use CLIP-T, which the average cosine similarity between prompt and image CLIP embeddings.
For unconditional image generation, have experimented oxford flowers, CelebAfaces and CelebAHQ datasets. Image quality has been measured by FID and MUSIQ score.
In case of image super-resolution, we experimented with guided diffusion Dhariwal & Nichol (2021 and latent diffusion Karras et al. (2022) pipelines. We use the FFHQ dataset for training, and test on a subset of 1000 images from CelebAHQ test set for x4 super- task. The hyperparameter details are given in Tab. 13
C Theoretical Justification
Here we provide theoretical analysis of the Lemmas mentioned the main paper.
Definition 2 Lipschilz Continuily: A function I is said to be Lipschitz continuuus if there erists a constand L (called the Lipschitz constant) such that for all x and in the domain of f:
[f(x) f(v) - vl
(13)
Definition 3 Maz- Min Difference
Consider the function f
[K1, (0,,,K,,.. kk)
.2, Rn) max(Nx) min(ss)
(14)

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Table 13: Hyperparameters
Hyperparameter
Values
Coefficient of Liveonn
Coefficient of Lprior Coefficient of L,c
1
1
Perceptual guidance scale
1.001
Learning rale
10-5
Batch size (Dreambooth, Custom diffusion)
Batch size (DDPM)
125
Batch size (GD)
16
Batch size (TD)
9
Text-to-image diffusion model
Stable Diffusion-vl Rombach et al. (2022
Number of class prior images (Drcambooth, Custom diffusion ) Number of DWT components
10
25
DWT filter
Daubechics
Definition 4 Lipschitz Condition: We need to show that there erists a constant such that for any two sets of kurlosis values (Ni, K2,..., Rnn) and (M' Kigs..., K')),
[f K2, Ko f(n,, K2..,, M. S L K6s K/
(15)
Lemma 2 KC' loss is differentiable and Lipschitz continuous with Lipschitz constant 2.
Proof. We are taking maximum and minimum across kurtosis values, therefore KC loss is differentiable and so as the combined loss, since differentiability preserves over addition.
Next, we proof the Lipschitz continuity. Note, the function max(s:) is 1-Lipschitz because:
max(k;)) max(x()) I max |K; x41
(16)
Similarly, the function min(k;) is also 1-Lipschitz because:
min(k:) min(k)) S max [x - K/l
(17)
Since both the maximum and minimum functions are 1-Lipschitz, their difference also Lipschitz continuous with constant of 2:
[f(K K2...., kn)) ni
max |xc - n4/1
(18)
For simplicity, if we consider the I norm of the differences, we get:
kn) - ( .i...., Knn <
(19)
Thus, we have shown that the KC loss function, i.e., the difference between the maximum and minimum kurtosis values of constant of 2 when considering the l norm.
coefficients of natural images is Lipschitz continuous with Lipschitz
Lemma 3 Gaussian scale mirture (GSM) vector with zero mean has the following probabilily density function:
p(x) = 1 (2
(20)
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and its projection kurtosis is constant with respect to the projection direction u, t.e.,
<(w">) 3var= (=}
(21)
E1
where { and [ar are the mean and variance of latent variable respectively.
Proof. Marginal distribution of the projection of x non-zero vector w is given by Zhang & Lyu
2014
[ xwwTx=t x=t
(t)
p(
[
xta Di x
Ps Pz(z)dz,
Uuuuaa / (2xz))) (de)(())))))))) expl
)x
2z
J Ni(0,, zw (z)
Note that, the last equality holds from the marginalization property of Gaussian, ie., X NW, 2), then, AX = (Ap,, AEAT).
The variance of w" x,
E((19) = / ppddz (,N =w" EE, w)z Jt
wD Eow f 2p
The fourth order moment of wax,
E((t") pzdz J (',N Tw w2
3(w" Exw)² pd
3(w =3w"E,w Exw) 23
We utilize the property that Ni(0, has a fourth order moment of 3 Finally, the kurtosis becomes,
E1(1)
k(w= x)
- 3
Ettt)2
3E,(z]2
3(E, {x(=)²
Ed/2}²
3v)
Ex(2]²
KC Loss Added as a Regularizer
We would like to highlight that our work, the underlying theoretical framework behind the forward and reverse diffusion processes remains unchanged; rather, we focus on improving the performance of the denoising neural network used to approximate the reverse diffusion trajectory.
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Suppose, we have the imput training images (x) and conditioning vector The conditioning vector could be text (Lext-to-image model), image (inage-to-inage model), or none (in case of the unconditional diffusion model). In the forward process, the noisy versions of image at timestep t is generated as I = arT + Og6, where é I).
In the reverse process, denoised autoencoder (fo) is trained to predict the denoised version of the image gon) at each timestep from the noisy images It, ie., Zt,gen fø e) Typically, the denoised autoencoder (fv) is trained by minimzing the Mean Squared Error between the real image (z) and the
generated denoised version of the image at time step t (It,gen) averaged over timesteps and noise variances denoted by,
Lyecon E,,, [ x
(22)
The KC loss is applied on the generated images (gr), and therefore can be considered as a function (f') of geen as follows:
L Lkc Ef
(23)
Note the function f' is difference between the maximum and minimum values of the DWT filtered version of input Zr,gen-
Therefore, the total loss function can be written as,
Ittoal Exgacee || x012] F
(24)
In our work, the above-mentioned framework remains the same. Instead, the proposed KC loss acts as an additional regularizer to the training of the denoising neural network, which helps it to denoise 2; better (Lemma 2, main paper), ultimately improving the approximation of i.e, Ftgeen at each time step
E
Additional Ablations
In Fig. we visualize some of the DiffNat generated images using various text-prompts. The generated images capture the context of the text-prompt and also retain naturalness. We also provide qualitative comparison w.r.t Dreambooth in Fig. 12
We also provide ablations for using DCT transforms and analyse the performance with respect to other tasks suggests DWT performs better than DCT for different methods
and hods. Experiments in Table. 14
across datasets.
F Failure Cases
We also present some of the failure cases of DifINat in Fig. 13
E.g., our model fails to generate images of A [V] berry bowl with the Eiflel Tower in the background" but actually generates images with "the Eiffel Tower" in the berry bowl. Similarly, the model fails to generate "A cube shaped [V] can", since these object do not appear the training set. The model also fails to generate "A [V] cat on top of purple rug in a forest" and instead generated some version of purple cat.
G Kurtosis Analysis
To verify the efficacy of the proposed KC loss, we perform average kurtosis analysis in this section. we compute the average kurtosis deviation of DWT filtered version of images from the datasct and plot them
Fig. 15 Fig. 16 and Fig. 17 E.g., in case of dreambooth task, we compute the kurtosis statistics of bandpass filtered version of natural images from Dreambooth dataset, images generated by Dreambooth and images
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images
images
[ dog backpack
A[V] dog backpack with a mountain the background
[V] dog backpack with city in the background
A [V] dog backpack on the beach
[V] dog backpack
floating on top of water
A [V] barry bowl
[V] berry bowl with mountain background
A M berry bow with a wheat fiekd the background
A berry bowl on top white rug
A berry bowl on the beach
A [V] cat with Eilfel Tower on top of green grass background with suntlowers around
A [V] cal
AM cat
A [ cat on top of a mirror
A [V] cat flbating on top water
Figure 11: DifINat generaled images. The Lask is learn a unique identifier (*A [V] dog backpack") of the training images and generate variations w.r.t. background, lighting conditions etc. The generated images look natural in different background context, e.g.; "A [V] dog backpack on the beach/ with a city in the background etc". The generated images are of high quality.
Table 14: Comparison of DCT vs DWT
Method
FID score |
MUSIQ score t
DB (Dreambooth dataset)
111.76
68.31
DB + KC (DCT)
106.23
68.72
DB + KC (DWT)
100.08
69.78
CD (Dreambooth dataset)
84.65
70.15
CD + KC (DCT)
80.33
70.67
CD + KC (DWT)
75.68
72.22
DDPM (Oxford flowers)
243.43
20.67
DDPM + KC (DCT)
240.12
20.98
DDPM KC (DWT)
237.73
21.13
GD (FFHQ)
121.23
57.31
GD + KC (DCT)
112.66
58.12
GD + KC (DWT)
103.19
58.69
LD (FFHQ)
95.83
59.57
LD + KC (DCT)
88.52
60.37
LD + KC (DWT)
83.34
61.20
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Real Images
DreamBooth
DiffNat
A M cat on top of green grass with sunflowers around it
A M cat
A [ shiny sneaker with mountain the background
A M shiny sneaker
A []] dog
A [V] dog floating on top of water
Figure 12: Comparison of DreamBooth and Dif DiffNat generated images have better visual quality.
images
Generated images
A [V] berry bowl with the Eiflel Tower background
A []] berry bowl green with sunflowers round
cube shaped M
A on top of purple rug in a fores!
Figure 13: Failure cases of DifINat. Instead of generating "A [V] berry bowl with the Fiffel Tower in the background", our method generates image with the Eiffel Tower in the berry bowl. Also, while generating "A [V] cat on top of a purple rug forest", it generates purple [V] cat, which shows the color bias w.r.t the text-prompt of the model.

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generated by DifiNat (ie., adding KC loss) and plot it in Fig. 15 We observe that the Dreambooth generated (a)) have highest kurtosis deviation. The average deviation is least for natural images (Fig. 15 (c)) and adding KC loss reduces the kurtosis deviation (Fig, 15 (b)). Similar trends can be observed for
images (Fig. 15
DDPM (Fig. 16p. guided diffusion (Fig. 17 as well. Adding KC loss improves image quality has been verified
both qualitatively and quantitatively the paper. This analysis verifies minimizing kurlosis loss improves diffusion image quality.
H
Computational Complexity
Here we analyze the computational complexity of the proposed KC loss. Suppose, given batch of N images. We need to perform DWT of cach images using k different filters. Since, DWT for "faar'/DDauechhi' wavelet can be done in linear time, the complexity of performing DWT with k filters can be done in O(Nk) time. Now, calculating the difference between maximum and minimum kurtosis can be done in linear time, therefore, the computational complexity of calculating KC loss O(Nk). This minimal overhoad of computing KC loss can be observed in the training time analysis provided next. The run time analysis has been provided in Table. 15 Note that the experiments for Dreambooth, Custom diffusion, DDPM have been performed on single A5000 machine with 24GB GPU. We have performed guided diffusion (GD) and latent diffusion (LD) experiments on a server of 8 24GB A5000 GPUs. The experimental results in Table. 15 show that incorporating KC loss induces minimum training overhead
Table 15: Training time analysis
Method
dataset
Training time
DrothBooth Ruiz al.
2022
5-shot finetuning
10 min 21s
DreamBooth
Ruiz of al.
2202
KC loss
5-shot finetuning
11 min 30s
Custom Diffusion
Kumari et.
2022
5-shot finetuning
6m 43s
Custom Diffusion
Kumari et al.
2022
KC loss
5-shot finetuning
7m 11s
DDPM
Ho et al.
2020
CelebAfaces
2d 8h 21m
DDPM
et al.
2020
+ KC loss
CelebAfaces
2d 9h 19m
GD
Dhariwal & Nichol
2021
FFHQ 23h 10m
GD
Dhariwal Nichol
2021
KC loss
FFHQ 1d 1h 29m
LD
Karras et al.
2022
FFHQ
20h 15m
1.I)
Karras et al.
2022
KC loss
FFHQ
22h 40m
Convergence Analysis
The main idea of the difffusion model is to train a UNet, which learns to denoise from random noise to specific image distribution. More denoising steps ensure better denoised version of the image, e.g., DDPM Ho et al. (2020), LDM Karras et al. (2022). In proposition 1 (main paper), we show that minimizing projection kurtosis further denoise input signals. Therefore, KC loss helps in the denoising process and improves the convergence speed. We have shown that adding KC loss improves the loss to converge faster for Dreambooth task in Fig. 14
J Qualitative Analysis
In this section, we provide more qualitative analysis to show that adding KC loss improves image quality. Zoomed view of the generated images are shown to compare w.r.t the basclines in Fig.
19
Fig.
20
Fig.
21
Fig.
22
Fig. 23
Fig. 24
Fig. 25
Fig 26
Details are provided in the caption.
Experiments on Image Super-resolution
In this section, we provide more experimental results for image super- super-resolution task. This includes quantitative results and human evaluation.
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Loss curve convergence
Dreambooth KC loss
Dreambooth
0.6
0.5
0.4
0.3
0.2
0.1
Steps
0
20
40
60
80
100
120
140
Figure 14: Loss curve convergence of Dreambooth.
250
250
250
Kurlosis values
Kurtosis values
Kurtosis values
200
200
200
150
150
150
100
100
100
50
50
50
0
Number of components
Number of components
Number of components
(a) Average kurtosis of Dreambooth (b) Avg. kurtosis (trained with (c) images
Average kurtosis of Natural im- ages
Dreambooth + KC loss)
Figure 15: Average kurtosis analysis of Dreambooth, DiffNat and natural images over the dataset used in Dreambooth. From this analysis, it evident that Dreambooth generated images have higher kurtosis deviation. Integrating KC loss reduces the kurtosis deviation to preserve the naturalness of the generated images. Natural images have more concentrated kurtosis values.
250
250
250
Kurlosis values
Kurtosis values
Kurtosis values
200
200
200
150
150
150
100
100
100
50
50
50
Number of components
Number of components
Number of components
(a) Average kurtosis of DDPM im- (b) Average kurtosis of images (c) ages
Average kurtosis Natural im- ages
trained with DDPM + KC loss
Figure 16: Average kurtosis analysis of DDPM framework trained Oxford flowers dalaset. From this analysis, it is evident that DDPM generated images have higher kurtosis deviation. Integrating KC loss roduces the kurtosis deviation to preserve the naturalncss the generated images. Natural images have more concentrated kurtosis values.

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250
250
Kurtosis values
Kurtosis values
250
200
200
Kurtosis values
200
150
150
150
100
100
100
50
50
50
0
Number of components
Number of components
Number of components
(a) Avg. kurtosis of GD generated (b) Avg. kurtosis of images trained images
with GD + KC loss
(c) Avg. kurtosis of Natural images
Figure 17: Average kurtosis analysis of guided diffusion (GD) framework trained on FFHQ dataset. From this analysis, it is evident that GD generated images have higher kurtosis deviation. Integrating KC loss reduces the kurtosis deviation to preserve the naturalness of the generated images. Natural images have more rateddtrated kurlosis values.
Table 16: Comparison of image super-resolution (x2) task
Method
Image quality
score
PSNR
f
MUSIQ score 1
Dhariwal Nichol
2021
100.2
19.4
58.
CD
RC los Ours)
80.9
20.2
0.66
59.91
I.D. Karras et 2022
82.45
21.2
0.64
60.23
KC Toss
70.12
22.3
0.70
62.15
K.1 Quantitative Results
In addition to the super resolution task (x4) shown in the main paper, we conduct experiments for x2 and x8 tasks as well in the same setting. The ground-truth images are of size 256 X 256. Therefore, x2 task performs image super-resolution from 128 X 128 + 256 X 256 and x8 task performs image super-resolution from 32 X 32 - 256 X 256 and the corresponding experiments are shown in Table 16 and Table 17 respectively. For training, we use standard FFHQ dataset Karras al. (2017), and evaluation performed on CelebA-Test dataset Karras al. (2017). We observe that adding KC loss improves image quality quantitatively both for guided diffusion (GD) and latent diffusion (LDD). Qualitative results are shown in Fig. Fig. 24
23
Fig. 25
and
Fig. 26 Next, we also perform human study to validate our approach.
K.2 Human Evaluation
We conduct. human evaluation of image super-resolution task to compare guided diffusion (GD)/ latent diffusion (LD) and adding KC loss to the corresponding counterpart (DiffNat). We provide 20 examples of natural images and corresponding generated images using GD, LD and our method DiffNNat (i.e., adding KC loss) and asked the following question to amazon mechanical turks: 'which of the generated images is of best visual quality considering factors include image quality and preserving the identity of the original image?' Similar to Dreambooth task, we evaluate this by 50 users, totalling 1000 questionnaires. The available options are { 'DiffNat', "GD/LD', 'None is satisfactory" }. The aggregate response shows that DiffNat generated images are of better image quality compared to the baselines, as shown in Fig. 27 Therefore, we verified the improved image quality quantitatively, qualitatively and through human evaluation as well. Note that, human evaluation is not applicable for unconditional image generation task since there is no one-to-one correspondence between the training images and the generated images. It will be ambiguous for the human observers to compare quality between approaches. Therefore, we abstain ourselves from performing human evaluation for this task. However, the quantitative and qualitative analysis exhibit the efficacy of our approach.
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1400
Frequency
1200
1000
800
600
400
200
2
4
8
Ratings
Figure 18: Subject fidelity assessment by user study, The ratings ranges from "0" being "extremely unlikely" being "extremely likely". We observe from the plot that most of the users find DiffNat preserves subject fidelity. The average rating 5.8, which "moderately likely" to "highly likely".
"A <V> backpack on the beach"
Dreambooth
Dreambooth KC Loss
Figure 19: Qualitative comparison of with withhut KC loss in Dreambooth. The bottom image (with KC loss) shows better image quality and shadows (best viewed in color).
Table 17: Comparison of image super-resolution (x8) task
Method
Image quality
FTD
PSNR
SSIM
MUSIQ
CD
Diariwal Nichol 2021
140.3
17.5
0.52
35.26
GD
KC los (urs)
125.5
18.7
0.56
57.33
Karras
2022
183.2
18.7
58.62
KC (ees(
80.1
19.5
0.67
60.31
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"A <V> berrybowl with mountain in the background"
Dreambooth
Bry perl
Dreambooth + KC Loss
Apper
Figure 20: Qualitative comparison of with/without KC loss in Dreambooth. The bottom image (with KC
loss) shows better image quality and reflections on the bowl full of berries (best viewed in color).
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"A <V> vase on top of a wooden floor"
Custom Diffusion
Custom Diffusion
+ KC Loss
Figure 21: Qualitative comparison of with/without KC loss in Custom diffusion. The bottom image (with
KC loss) shows better image quality in terms of color vividness and contrast (best viewed in color).
"A <V> ducktoy on top of a wooden floor"
Figure 22: Qualitative comparison of with without KC loss in Custom diffusion. The bottom image (with
KC loss) shows better image quality in terms of detail and smoothness (best viewed in color).
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Input
GD
GD + KC
GT
Figure 23: Qualitative comparison of with/without without KC loss in guided diffusion (GD). The bottom image
(with KC loss) has better eye and hair details (best viewed in color).
Input
GD
KC
GT
Figure 24: Qualitative comparison of with/without without KC loss in guided diffusion (GD). The bottom image
(with KC loss) has better eye details and skin smoothness (best viewed color).
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Input
LD
LD + KC
GT
Figure 25: Qualitative comparison of with with/without without KC loss in Latent diffusion (LD). The bottom image (with KC loss) has higher similarity w.r.t the ground truth in terms of left eye and skin color (best viewed in color).
Input
LD KC
Figure 26: Qualitative comparison of with /without KC in Latent diffusion (LD). The bottom image (with KC
loss) has higher similarity w.r.t the ground truth in terms of left eye and skin color (best viewed in color).
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200
Frequency
150
100
50
BiffNat
GD/LD
None
Figure 27: Human evaluation for image super-resolution task. DifNNat performs better than guided diffusion (GD), latent diffusion (LD) in user study as well.
Table 18: Blind Face restoration
Method
LPIPS-divt
FID
IDS
PSNR
SSIM t
DifFace
0.20
70.69
0.48
22.82
0.61
RestoreFormer
60.98
0.39
21.77
0,53
IPC Suin et al. 2004
0.32
55.42
0.54
22.34
0,60
IPC
KC
0.34
43.21
0.61
24.19
0.64
L Experiments on other Tasks
We analyze the effectiveness of KC loss on other visual recognition tasks, e.g., inverse problem like blind face restoration.
L.1 Blind Face Restoration
For blind face restoration task Suin et al. 2024 we train on FFHQ dataset with and without KC loss on IPC baseline Suin et al. (2024) and evaluate on 3000 images on Celeb-A test set with a resolution of 256x256.
Average LPIPS, FID, IDS, PSNR, SSIM are reported Tab. 18 Qualitative results (Fig. 28 also verify that adding KC loss improves image quality.
Input
IPC
IPC + KC
GT
Figure 28: Restoration comparison with IPC
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L.2 Conditional Image Generation with Structural Constraints.
We also experiment with more challenging conditional image generation with structural constraint. In this context, we experiment with Crtl-U Zhang et al. (2024), to verify the efficacy of KC loss and PG strategy. Ctrl-U Zhang et al. (2024) proposes uncertainty-aware reward modcling for conditional image gencration. It estimates reward uncertainty by running two forward passes of the diffusion model at different timesteps. The framework uses this uncertainty adaptively regularize a pixel-wise consistency loss. They have expreimented on ADE20K, COCO-Stufi datasets for segmentation, edge, depth conditions.
We integrate KC loss additionally to the existing losses of Ctrl-U for training and use PG strategy during inference for segmentation mask as condition. We experiment on ADF20K and COCO-stuff dataset and observe improvements in both segmentation performance and image quality. We use the same setting used by Crtl-U. Experimental results are shown Tab. 19 and Fig. 29 Fig. 30
L.3
Comparison with AttnDreambooth
For the personalized few-shot finetuning task, we also compare with recent baseline BrtnDreamBooth (AttnDB) Pang et al. (2024). Tt introduces a novel method to enhance personalized text-to-image synthesis by addressing limitations in existing approaches like Textual Inversion and DreamBooth, which often suffer from overfitting ling or neglecting the larget concept. AunDDremmBooh decomposes the personalization process inlo three distinct training stages: (1) learning embedding alignment integrate new concepts into the model's vocabulary, (2) refining the attention map through fine-tuning cross-attention layers with regularization term that encourages similarity between the atlention maps of the new concept. and its super-category, and (3) acquiring the subject identity by fine-tuning the entire U-Net. We add both KC loss and PG strategy to this, and obtain performance improvement both in image quality, and diversity as shown in Tab. 20 Qualitative
results are shown Fig. 31
Table 19: Performance Comparison w.r.t Ctrl-U on ADE20K and COCO-stuff Datasets
ADE20K
COCO-stuff
Method
mloU
FID +
CLIPscore f
mloU
FID +
CLIPscore
Ctrl-U (ICLR'25) Zhang et al. (2024)
46.49
28.01
32.26
49.91
25.79
31.23
Ctrl-U + KC
46.72
24.32
29.32
50.12
14.08
31.75
Ctrl-U + KC + PG
46.91
20.12
30.12
50.47
11.06
32.18
Table 20: Comparison personalized few-shot finetuning task
Method
Image quality
Subject fidelity
Prompt fidelity
Image diversity
FID
MUSIQ
HPSv2
DINO
CLIP-I
CLIP-T
LPIPS-dIv
DB
Ruix et al
2022
111.76
68.31
25.12
0.65
0.81
0.31
0.38
DB
Ruiz et al
2022
LPIPS
108.23
68.39
25.43
0.65
0.80
0.32
0.10
DB
KC Ioes
Ous
100.08
69.78
25.82
0.68
0.84
0.34
0.42
DB
(Ours)
93.45
0.43
CD
Kumarri
2022
CD
Kumari
2022
LPIPS
80.12
70.56
26.33
0.71
0.87
0.37
0.43
CD)
KC loss (Curs)
75.68
72.22
26.64
0.73
0.88
0.40
0.44
CD
KC lues + PG
(Oux)
68.27
73.77
27.
0.77
0.89
0.43
0.46
AstnDB
Pang et al.
2024
80.59
70.23
26.42
0.72
0.85
0.35
0.11
AttuDB
Pang al.
2024
+ LPIPS
80.06
70.50
26.66
0.73
0.87
0.35
0.43
AttnDB
(Urss)
73.02
26.83
0.45
AstnDB
KC
PG (Ours)
64.78
72.89
27.35
0.78
0.90
0.38
0.47
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Input
condition
Cenmuntt-eeenerrttte images-
Crtl-U
Crtl-U
KC
Crtl-U
KC
PG
Figure 29: Qualitative comparison with Ctrl-U
Input
condition
Generated images
Crtl-U
Crtl-U
KC
Crtl-U
KC
+PG
Figure 30: Qualitative comparison with Ctrl-U
17

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Published in Transactions on Machine Learning Research (07 (07/2025)
Input
AttnDB
AttnDB+KC
AttnDB+KC+PG
<V> ducktoy
"<V> ducktoy on a cobblestone street"
</> cat
"<V> cat on wooden floor"
Figure 31: Qualitative comparison with AthoDreamBooth
18


Reviews:
[{"review_id": "UhcowCLyDL", "reviewer": "Reviewer_eXc5", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "S1: The proposed KC loss is self-contained and does not rely on external priors for calculation, making it relatively generic and versatile.\n\nS2: The experimental evaluation is conducted across multiple tasks, effectively demonstrating the applicability and effectiveness of the proposed methods.\n\nW1: The proposed KC loss maintains a quasi-constant value but does not explicitly regulate the absolute values of KC. I wonder how the actual KC values impact or reflect the image quality of the generated outputs. It would be beneficial to report the KC values and their standard deviations for both baseline methods and the proposed methods for better understanding.\n\nW2: The KC calculation likely introduces additional computational overhead during training and fine-tuning of diffusion models (DMs). Although the authors report the overhead of PG in Table 10, they do not mention the overhead for KC, which should be detailed for both training and fine-tuning scenarios.\n\nW3: If KC is calculated purely based on the generated images themselves, it seems that it could be broadly applicable to other generative models beyond DMs. This contrasts with PG, which appears more specific to DMs. Could the authors clarify this distinction?\n\nW4: The reported FID scores seem relatively high across different tasks for the baselines. For example, the FID for unconditional generation on CelebA-HQ with DDPM seems higher than expected based on my own experience working with similar models. Could the authors clarify the details of FID calculation, such as the number of images used and the exact implementation setup?\n\nW5: For the qualitative visualizations in the few-shot fine-tuning task (Task 1), it would be helpful to include the corresponding text prompts alongside the image examples in both the main paper and the appendix. It would also strengthen the presentation to showcase some examples where the same input image is used with varying text prompts, as the text conditions are also known to have impacts on the image quality for T2I tasks.\n\nOther weaknesses: There are some minor formatting inconsistencies in the paper title and section titles such as 3.2, 3.3, and 3.4. Some titles have each word capitalized, while others do not. It would be best to ensure consistent formatting throughout the paper.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This work proposes to improve the quality of synthesized images for diffusion-based generative models by exploiting the Kurtosis concentration property of images. Accordingly, the authors propose a KC loss that aims to minimize the variants of KC value so that the KC metric stays constant. They also propose a corresponding sampling modification, namely Perceptual Guidance (PG), to further improve the sampling quality. Experiments are conducted on four tasks, covering both unconditional and conditional (text-to-image) generations.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please see the detailed requests from my listed weaknesses above.", "broader_impact_concerns": "A Broader Impact Statement would probably be helpful despite the methodology focus, as this is a work on the generative models, and may produce unlicensed and potentially harmful content.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "fxBj0hwcPc", "reviewer": "Reviewer_hnFs", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The paper is exceptionally well-written. The ideas are clearly explained, the structure is logical, and the language is fluent and easy to follow, making the technical concepts accessible.\n2. The paper includes an analysis of kurtosis values of generated images and perceptual artifacts, providing insight into how the proposed KC loss might be working to improve image quality by altering the statistical properties of the output.\n\nWeaknesses:\n1. While the paper presents a broad range of experiments across different tasks and baselines, the experimental evaluation appears insufficient in demonstrating the method's performance relative to the latest state-of-the-art methods specifically designed for these tasks. Without comparisons to the most recent and competitive approaches published since the baselines used, it is difficult to definitively assess the magnitude of the improvement achieved.\n2. The paper mentions and builds upon existing knowledge regarding the kurtosis concentration property and its use in image processing. However, the novelty and unique contribution of applying this property specifically as a differentiable training loss for diffusion models aimed at image quality enhancement needs clearer delineation and justification. This is particularly important when considering potentially related prior works that have utilized KC or similar higher-order statistics in other image generation or manipulation contexts. Consequently, it is challenging to fully evaluate the genuine technical contribution and added value provided by the proposed method relative to the existing body of work leveraging KC for various image-related problems.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes DiffNat, a framework aimed at improving the perceptual quality of images generated by diffusion models. The core idea is to leverage the kurtosis concentration (KC) property of natural images by introducing a novel KC loss during training and a Perceptual Guidance (PG) strategy during inference. The KC loss minimizes the difference between the maximum and minimum kurtosis values computed from Discrete Wavelet Transform (DWT) filtered versions of the generated images. The PG strategy combines a model trained with the KC loss and a base model to guide the sampling process towards perceptually improved outputs. The method is evaluated on diverse tasks including personalized few-shot finetuning, unconditional image generation, image super-resolution, and blind face restoration, using both quantitative metrics and user studies.\n\n\n-----------------------------------Thanks for the response----------------------------------------------\n\nThis is a straightforward training trick for diffusion models, and I do not see new insights from it. The improvement in generation quality is not evident based on qualitative comparisons.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Add more recent baselines in experiments.", "broader_impact_concerns": "n/a", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "E0dDmD4arJ", "reviewer": "Reviewer_gVFx", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "S:\n* The paper is well-written and the formulation of the KC loss is grounded in well-established properties of natural images.\n* The proposed loss and inference-time strategy are general-purpose and shown to improve performance across diverse image synthesis tasks.\n* The paper presents detailed experiments, demonstrating consistent improvements in image quality.\n\nW:\n* While the KC property is well-motivated theoretically, its empirical applicability is primarily demonstrated on traditional natural image datasets. It is unclear how this property generalizes to domains that deviate from natural images. The authors could clarify how \"naturalness\" is defined and whether this metric aligns with human perception.\n* By enforcing kurtosis regularity, the method could bias the model towards producing certain samples, potentially suppressing diversity. The authors could quantify this trade-off.\n* The use of FID for few-shot personalized generation (e.g., DreamBooth) is not standard, as the reference distribution is ill-defined. The paper could provide justification or include additional standard metrics.\n* Although the authors claim that KC loss can be applied to “any diffusion-based pipeline,” recent conditional generative models with structural constraints (e.g., spatial priors) may not trivially benefit from KC loss. Works such as: UniScene (CVPR 2025), Ctrl-u (ICLR 2025) and SCP-Diff (ECCV 2024) could serve as benchmarks. More empirical validation on these structured conditional setups would strengthen the claim of universality.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces DiffNat, a framework that enhances the image quality of diffusion-based generative models by leveraging the Kurtosis Concentration (KC) property of natural images. The authors propose a novel KC loss that minimizes the kurtosis spread across band-pass filtered versions (e.g., via DWT) of generated images and introduce a condition-agnostic Perceptual Guidance (PG) mechanism to further improve perceptual quality during inference. The proposed method is evaluated across four tasks: (1) personalized few-shot finetuning, (2) unconditional generation, (3) image super-resolution, and (4) blind face restoration. The results demonstrate improvements in FID, MUSIQ, and human perceptual assessments.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please consider addressing the points raised in the weakness part.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)