Lightweight Diffusion Network for Real-Time Style Transfer on Mobile Devices with Joint Image-Text Interaction

Convolutional Neural Networks (CNNs) have achieved remarkable progress in arbitrary artistic style transfer. However, the model size of existing state-of-the-art (SOTA) style transfer algorithms is immense, leading to enormous computational costs and memory demand. It makes real-time and high resolution hard for GPUs with limited memory and limits the application on mobile devices. This paper proposes a novel arbitrary artistic style transfer algorithm, KBStyle, whose model size is only 200 KB. Firstly, we design a style transfer network where the style encoder, content encoder, and corresponding decoder are custom designed to guarantee low computational cost and high shape retention. Besides, the weighted style loss function is presented to improve the performance of style migration. Then, we propose a novel knowledge distillation method (Symmetric Knowledge Distillation, SKD) for encoder-decoder-based style transfer models, which redefines the knowledge and symmetrically compresses the encoder and decoder. With the SKD, the proposed style transfer network is further compressed by 14 times to achieve the KBStyle. Experimental results demonstrate that the proposed SKD method achieves comparable results with other SOTA knowledge distillation algorithms for style transfer. Besides, the proposed KBStyle achieves high-quality stylized images. And the inference time of the KBStyle on an Nvidia TITAN RTX GPU is only 20 ms when the resolutions of the content image and style image are both 2k-resolution (2048×1080). Moreover, the 200 KB model size of KBStyle is much smaller than the SOTA models and facilitates style transfer on mobile devices.

Image style transfer is a key research area in computer vision. Despite significant progress, challenges such as mode collapse, over-stylization, and insufficient style transfer persist, impacting image quality and stability. To address these issues, we introduce StyDiff, a novel framework that combines diffusion models and Adaptive Instance Normalization (AdaIN) to achieve high-quality and flexible style transfer. Specifically, StyDiff uses the AdaIN module to precisely blend content and style features, mitigating problems of over-stylization and incomplete style transfer. The diffusion model optimizes image generation through a stepwise denoising process, ensuring consistency between content and style while significantly reducing artifacts. Additionally, a multi-component loss function is designed to further enhance the balance between content and style. Experimental results demonstrate that StyDiff outperforms existing methods across key metrics such as SSIM, GM, and LPIPS, producing images with superior style consistency, content retention, and detail preservation. This approach offers a more stable and efficient solution for style transfer tasks, with promising potential for widespread application.

Image style transfer is an emerging image processing technique that generates an image while preserving the structural content of the original image and incorporating the visual style of another image. This technology is widely applied in digital art, photography, animation, and visual media. Traditional methods of style transfer rely on manual modeling based on texture synthesis and physical rendering. This paper provides a systematic and comprehensive overview of the development of image style transfer technology, thoroughly reviewing the technical evolution from early optimization-based methods to modern deep learning frameworks. It begins by introducing traditional approaches based on texture synthesis and manual rendering models; then focuses on convolutional neural network (CNN)-based methods, represented by Gatys et al.'s neural style transfer approach; followed by an in-depth analysis of generative adversarial network (GAN)-based style transfer models, including representative works such as Pix2Pix, CycleGAN, NICE-GAN, and StyleGAN. These methods have demonstrated excellent performance in paired, unpaired, and controllable style transfer tasks. Additionally, the paper discusses the latest research advancements in diffusion models, particularly high-fidelity controllable image generation methods like latent space diffusion models and Stable Diffusion. Furthermore, it systematically summarizes commonly used network architectures, loss functions, evaluation metrics, and benchmark datasets in style transfer. Finally, it analyzes the main challenges and future research directions of current technologies from perspectives such as content consistency, semantic alignment, computational efficiency, and objective evaluation. This paper can provide researchers and practitioners in the field of image style transfer with structured references.

BackgroundData heterogeneity due to cross‐scanner variations is a major challenge in multi‐center aging and dementia studies. Brain structural measures for the same participant can vary due to scanner differences (manufacturer, scanner age/technology, signal‐to‐noise ratio, pulse sequence design, contrast, resolution) and result in biomarker variability. In this work, we applied basic image processing and deep learning methods (style transfer (ST) and generative adversarial networks (GANs)) for harmonizing T1‐weighted MRI scans across change in vendors (GE to Siemens) and resolution (1x1x1.2mm to 0.8x0.8x0.8mm) and test whether this harmonization improved compatibility of AD biomarker measures.MethodWe utilized training (n=814) and test (n=113 same participants scanned on two scanners close‐in‐time, typically on the same day, with different resolution) data from cognitively impaired and unimpaired participants scanned on GE and Siemens scanners from Mayo Clinic Study of Aging and Mayo ADRC. The harmonization was conducted to translate GE to Siemens. All scans were affine transformed to the same template space, resampled to 1.5 mm3, and bias corrected. Histogram matching was performed using cumulative density functions. A ResNet18 was trained for the ST’s content and style layers. Two types of GANs, conditional GANs (cGAN) and cycle‐consistent GANs (cycleGAN), were constructed. We then measured regional cortical thickness from each image using FreeSurfer‐7.1.1 and compared the thickness measurements from the original and translated scans using intraclass‐correlation (ICC) and Deming regression.ResultIn the test dataset, ST and cycleGAN showed the best performances when comparing the translated GE to Siemens. With ST, the ICC increased (vs. the original scans) slightly for the temporal meta‐ROI (0.956 from 0.954) and more substantially for the frontal pole ROI (0.706 from 0.594). The cycleGAN showed an increase in ICC for the frontal pole ROI (0.610 from 0.594) but not the meta‐ROI (Fig. 1).ConclusionEach method leveraged a different strength. Histogram matching captures contrast, ST extracts textural information, and GANs learn combination of contrast, edge and texture properties. We found that deep learning is a promising approach to cross‐scanner variability. Our comparisons revealed anatomical differences across methods; thus, disease specific measure should be considered for selection of the scanner harmonization method.

Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to their strong performance in representation learning. However, running a DNN needs tremendous memory resources, which significantly restricts DNN from being applicable on resource-constrained platforms (e.g., IoT, mobile devices, etc.). Lightweight DNNs can accommodate the characteristics of mobile devices, but the hardware resources of mobile or IoT devices are extremely limited, and the resource consumption of lightweight models needs to be further reduced. However, the current neural network compression approaches (i.e., pruning, quantization, knowledge distillation, etc.) works poorly on the lightweight DNNs, which are already simplified. In this paper, we present a novel framework called Smart-DNN, which can efficiently reduce the memory requirements of running DNNs on resource-constrained platforms. Specifically, we slice a neural network into several segments and use SZ error-bounded lossy compression to compress each segment separately while keeping the network structure unchanged. When running a network, we first store the compressed network into memory and then partially decompress the corresponding part layer by layer. According to experimental results on four popular lightweight DNNs (usually used in resource-constrained platforms), Smart-DNN achieves memory saving of 1/10∼1/5, while slightly sacrificing inference accuracy and unchanging the neural network structure with accepted extra runtime overhead.

Generative adversarial network (GAN) has become a hot research topic in the field of image processing. As an unsupervised training model, GAN has been widely used in the field of computer vision, especially in image style transfer. The purpose of the GAN is to make the generator generate a false image, and the discriminator cannot tell whether the input image is the real image or the generated image. Compared with traditional network models, GAN model has these advantages in image style transfer: GAN is composed of two different networks, and the loss function is automatically learned by playing games with each other. GAN belongs to unsupervised training and does not need to annotate the data set, which saves a lot of work. In this paper, improved GAN models related to image style migration are summarized. Firstly, the principle and method of image style transfer based on convolutional neural network are introduced. Secondly, the status, principle and prospect of GAN are introduced, and the causes of gradient disappearance and mode collapse of GAN are analyzed in detail. On this basis, the principles, advantages and disadvantages of CGAN, DCGAN, CycleGAN and StarGAN V2 network models are introduced. Finally, it summarizes the current problems and future research directions of style transfer based on GAN.

Network architecture plays a pivotal role in style transfer. Most existing algorithms use VGG19 as the feature extractor, which incurs a high computational cost. In this work, we conduct an empirical study on the popular network architectures and find that some more efficient networks can replace VGG19 while having comparable style transfer performance. Beyond that, we show that an efficient network can be further accelerated by removing its empty channels via a simple channel pruning method tweaked for style transfer. To prevent the potential performance drop due to using a more lightweight network and obtain better style transfer results, we introduce a more accurate deep feature alignment strategy to improve existing style transfer modules. Taking GoogLeNet as an exemplary efficient network, the pruned GoogLeNet with the improved style transfer module is 2.3 ~ 107.4× faster than the state-of-the-art approaches and can achieve 68.03 FPS on 512×512 images. Extensive experiments demonstrate that VGG19 can be replaced by a more lightweight network with significantly improved efficiency and comparable style transfer quality.

Generative Adversarial Networks (GANs), introduced by Ian Good fellow in 2014, have revolutionized the field of artificial intelligence, particularly in image generation and style transfer. GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously through adversarial processes. GANs have found extensive applications in various domains of image generation, such as creating high-resolution images, generating images from textual descriptions, and augmenting datasets for machine learning tasks. In style transfer, GANs have enabled the seamless merging of different artistic styles with content images, producing visually appealing and artistically sophisticated results. Techniques like CycleGAN and StyleGAN have pushed the boundaries, allowing for unpaired image-to-image translation and fine-grained control over generated images' style and content. This abstract explores the underlying principles of GANs, highlights key advancements, and discusses their transformative impact on image generation and style transfer.

Style transfer describes the rendering of an image's semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style. In this paper, we focus on tackling these challenges and limitations to improve style transfer. We propose adversarial gated networks (Gated-GAN) to transfer multiple styles in a single model. The generative networks have three modules: an encoder, a gated transformer, and a decoder. Different styles can be achieved by passing input images through different branches of the gated transformer. To stabilize training, the encoder and decoder are combined as an auto-encoder to reconstruct the input images. The discriminative networks are used to distinguish whether the input image is a stylized or genuine image. An auxiliary classifier is used to recognize the style categories of transferred images, thereby helping the generative networks generate images in multiple styles. In addition, Gated-GAN makes it possible to explore a new style by investigating styles learned from artists or genres. Our extensive experiments demonstrate the stability and effectiveness of the proposed model for multi-style transfer.

Neural Style Transfer (NST) is a class of software algorithms that allows us to transform scenes, change/edit the environment of a media with the help of a Neural Network. NST finds use in image and video editing software allowing image stylization based on a general model, unlike traditional methods. This made NST a trending topic in the entertainment industry as professional editors/media producers create media faster and offer the general public recreational use. In this paper, the current progress in Neural Style Transfer with all related aspects such as still images and videos is presented critically. The authors looked at the different architectures used and compared their advantages and limitations. Multiple literature reviews focus on the Neural Style Transfer of images and cover Generative Adversarial Networks (GANs) that generate video. As per the authors’ knowledge, this is the only research article that looks at image and video style transfer, particularly mobile devices with high potential usage. This article also reviewed the challenges faced in applying for video neural style transfer in real-time on mobile devices and presents research gaps with future research directions. NST, a fascinating deep learning application, has considerable research and application potential in the coming years.

Generative Adversarial Networks (GANs) have been one of the most successful deep learning architectures in recent years, providing a powerful way to model high-dimensional data such as images, audio, and text data. GANs use two neural networks, generator and discriminator, to generate samples that resemble real data. The generator tries to create realistic looking samples while the discriminator tries to differentiate the generated samples from real ones. Through this adversarial training process, the generator learns to produce high-quality samples indistinguishable from the real ones.Different optimization algorithms have been utilized in GAN research, including different types of loss functions and regularization techniques, to improve the performance of GANs. Some of the most significant recent developments in GANs include M-DCGAN, which stands for multi-scale deep convolutional generative adversarial network, designed for image dataset augmentation; StackGAN, which is a text-to-image generation technique designed to produce high-resolution images with fine details and BigGAN, a scaled-up version of GAN that has shown improved performance in generating high-fidelity images.Moreover, the potential applications of GANs are vast and cross-disciplinary. They have been applied in various fields such as image and video synthesis, data augmentation, image translation, and style transfer. GANs also show promise in extending their use to healthcare, finance, and creative art fields. Despite their significant advancements and promising applications, GANs face several challenges such as mode collapse, vanishing gradients, and instability, which need to be addressed to achieve better performance and broader applicability.In conclusion, this review gives insights into the current state-of-the-art in GAN research, discussing its core ideas, structure, optimization techniques, applications, and challenges faced. This knowledge aims to help researchers and practitioners alike to understand the current GAN models' strengths and weaknesses and guide future GAN developments. As GANs continue to evolve, they have the potential to transform the way we understand and generate complex datasets across various fields.

The design and cracking of text-based CAPTCHAs are important topics in computer security. This study proposes a method for the style transfer of text-based CAPTCHAs using Generative Adversarial Networks (GANs). First, a curated dataset was used, combining a text-based CAPTCHA library and image collections from four artistic styles—Van Gogh, Monet, Cézanne, and Ukiyo-e—which were used to generate style-based text CAPTCHA samples. Subsequently, a universal style transfer model, along with trained CycleGAN models for both single- and double-style transfers, were employed to generate style-enhanced text-based CAPTCHAs. Traditional methods for evaluating the anti-recognition capability of text-based CAPTCHAs primarily focus on recognition success rates. This study introduces topological feature analysis as a new method for evaluating text-based CAPTCHAs. Initially, the recognition success rates of the three methods across four styles were evaluated using Muggle-OCR. Subsequently, the graph diameter was employed to quantify the differences between text-based CAPTCHA images before and after style transfer. The experimental results demonstrate that the recognition rates of style-enhanced text-based CAPTCHAs are consistently lower than those of the original CAPTCHA, suggesting that style transfer enhances anti-recognition capability. Topological feature analysis indicates that style transfer results in a more compact topological structure, further validating the effectiveness of the GAN-based twice-transfer method in enhancing CAPTCHA complexity and anti-recognition capability.

Stain transfer using Generative Adversarial Networks and disentangled features

GAN (Generative Adversarial Network) is widely used in image generation, renowned for its ability to produce high-fidelity details and sharp edges through adversarial training. Unlike Variational Autoencoders (VAEs), which often generate blurrier outputs, GANs excel in visual realism by leveraging a dual-network architecture—a generator and a discriminator—engaged in a competitive learning process. Furthermore, GANs synthesize images in a single forward pass, making them significantly faster than iterative approaches like Diffusion Models, which rely on multi-step denoising. This efficiency enables real-time applications, a critical advantage in fields such as AI-assisted art creation. This essay begins by outlining the foundational concepts of GANs, including their adversarial training mechanism. Next, it explores their methodology, emphasizing key architectures and training techniques that enhance stability and output quality. A comparative analysis with VAEs and Diffusion Models follows, highlighting GANs' superior perceptual quality while acknowledging challenges such as mode collapse and training instability. Finally, the discussion shifts to GANs' transformative role in AI painting, where they facilitate style transfer, photorealistic artwork generation, and interactive digital art tools. By examining these aspects, this essay underscores GANs' unique contributions to generative AI while addressing their limitations and future potential.

Abstract: Generative Adversarial Networks (GANs) is a groundbreaking artificial intelligence technology that transforms generative modeling through the implementation of a novel adversarial training framework. GANs are made up of two neural networks, the generator and the discriminator, which compete in a minimax game in order to generate counterfeit samples of data and distinguish between real and fake data. This adversarial training method results in the creation of highly proficient generative models capable of producing data that is identical to real-world samples. GANs have been shown to have significant outcomes in a variety of programs, consisting of synthesizing images, style transfer, medical visualization, and natural language processing. However, GANs encounter difficulties with problems like mode collapse and operational irregularities. Presently, research is aimed at tackling these problems and strengthening GAN frameworks and training approaches. GANs’ adaptability and perspective contribute to an intriguing technology for a wide range of sectors and artistic activities, with consequences for artificial intelligence growth and generative modeling breakthroughs. GANs incorporate the potential of competitive training with their capacity to generate extremely realistic data in a variety of domains. We will begin by looking at the fundamental concepts, and underlying principles that reinforce GANs, and their general architecture, investigate their different possible uses, and discuss the obstacles and potential developments in this instantly transforming field.