Drag gan

Author: g | 2025-04-24

Drag Your GAN offers an innovative and interactive way to experiment with image editing. How does it work? Drag Your Gan leverages StyleGAN2, a state-of-the-art GAN In this paper reading, we dive into Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold. Drag Your GAN introduces a novel

drag gan on custom images

DragGAN (SIGGRAPH'2023)Drag Your GAN: Interactive Point-based Manipulation on the Generative Image ManifoldTask: DragGANAbstractSynthesizing visual content that meets users’ needs often requires flexibleand precise controllability of the pose, shape, expression, and layout of thegenerated objects. Existing approaches gain controllability of generativeadversarial networks (GANs) via manually annotated training data or aprior 3D model, which often lack flexibility, precision, and generality. Inthis work, we study a powerful yet much less explored way of controllingGANs, that is, to "drag" any points of the image to precisely reach targetpoints in a user-interactive manner, as shown in Fig.1. To achieve this, wepropose DragGAN, which consists of two main components: 1) a feature-based motion supervision that drives the handle point to move towardsthe target position, and 2) a new point tracking approach that leveragesthe discriminative generator features to keep localizing the position of thehandle points. Through DragGAN, anyone can deform an image with precisecontrol over where pixels go, thus manipulating the pose, shape, expression,and layout of diverse categories such as animals, cars, humans, landscapes,etc. As these manipulations are performed on the learned generative imagemanifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deformingshapes that consistently follow the object’s rigidity. Both qualitative andquantitative comparisons demonstrate the advantage of DragGAN over priorapproaches in the tasks of image manipulation and point tracking. We alsoshowcase the manipulation of real images through GAN inversion.Results and Models Gradio Demo of DragGAN StyleGAN2-elephants-512 by MMagic ModelDatasetCommentFID50kPrecision50kRecall50kDownloadstylegan2_lion_512x512Internet Lionsself-distilled StyleGAN0.00.00.0modelstylegan2_elphants_512x512Internet Elephantsself-distilled StyleGAN0.00.00.0modelstylegan2_cats_512x512Cat AFHQself-distilled StyleGAN0.00.00.0modelstylegan2_face_512x512FFHQself-distilled StyleGAN0.00.00.0modelstylegan2_horse_256x256LSUN-Horseself-distilled

drag GAN on custom images

Depletion region in the GaN epitaxial layer, effectively depleting the 2DEG. As Vgs increases, the 2DEG beneath the gate gradually recovers, enabling a higher IDS current to flow through the channel. The threshold voltage (Vth) is defined as the Vgs value at which IDS reaches a specified level.Figure 3: Structure of an InnoGaN™ E-Mode GaN HEMT (Source: Innoscience)ROHM SemiconductorROHM offers a range of GaN-based products under the EcoGaN™ brand, designed to optimize performance for lower power consumption, more compact peripheral components, and simplified circuit designs with fewer parts. The EcoGaN™ lineup includes both GaN HEMT devices and GaN-integrated ICs with built-in controllers.A key challenge in the widespread adoption of GaN technology is ensuring the reliability of GaN HEMTs, with the growth of the GaN epitaxial layer playing a crucial role in GaN-on-Si manufacturing. ROHM has been actively developing GaN technology since 2006, leveraging its proprietary expertise in GaN epitaxial layer growth—originally refined for high-reliability LED production—to deliver robust and dependable products.In April 2023, ROHM commenced production of 650V GaN HEMTs, covering a key voltage range in the GaN market. Beyond enhancing GaN HEMT performance, ROHM is also advancing solutions for GaN drive and control, including gate driver and controller ICs. These innovations enable higher switching speeds while minimizing losses, offering user-friendly GaN solutions for more efficient power applications.Key parameters for evaluating GaN power devicesWhen evaluating or selecting a GaN power device, several critical parameters should be considered:VDS,max (drain-source voltage): It is the maximum voltage the device is guaranteed to block

Drag your GAN gan dragyourgan AI huggingface - YouTube

To find an equilibrium in the game when: The generator makes data that looks almost identical to the training data. The discriminator can no longer tell the difference between the fake images from the real images.The artist vs. the criticMimicking masterpieces is a great way to learn art — “How Artists Are Copying Masterpieces at World-Renowned Museums.” As a human artist mimicking a masterpiece, I’d find the artwork I like as an inspiration and try to copy it as much as possible: the contours, the colors, the compositions and the brushstrokes, and so on. Then a critic takes a look at the copy and tells me whether it looks like the real masterpiece. Figure 6: An artist copies another painting.GANs training is similar to that process. We can think of the generator as the artist and the discriminator as the critic. Note the difference in this analogy between the human artist and the machine (GANs) artist, though: the generator doesn’t have access or visibility to the masterpiece that it’s trying to copy. Instead, it only relies on the discriminator’s feedback to improve the images it’s generating.Evaluation metricsA good GAN model should have good image quality — for example, not blurry and resembles the training image; and diversity: a good variety of images get generated that approximate the distribution of the training dataset. To evaluate the GAN model, you can visually inspect the generated images during training or by inference with the generator model. If you’d like to evaluate your GANs quantitatively, here are two popular evaluation metrics: Inception Score, which captures both the quality and diversity of the generated images Fréchet Inception Distance which compares the real vs. fake images and doesn’t just evaluate the generated images in isolation GAN variantsSince Ian Goodfellow et al.’s original GANs paper in 2014, there have been many GAN variants. They tend to build upon each other, either to solve a particular training issue or to create new GANs architectures for finer control of the GANs or better images. Here are a few of these variants with breakthroughs that provided the foundation for future GAN advances. This is by all means not a complete list of all the GAN variants.Figure 7: GAN variants timeline (image by the author).DCGAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks) was the first GAN proposal using Convolutional Neural Network (CNN) in its network architecture. Most of the GAN variations today are somewhat based on DCGAN. Thus, DCGAN is most likely your first GAN tutorial, the “Hello-World” of learning GANs. WGAN (Wasserstein GAN) and WGAN-GP (were created to solve GAN training challenges such as mode collapse — when the generator produces the same images or a small subset. Drag Your GAN offers an innovative and interactive way to experiment with image editing. How does it work? Drag Your Gan leverages StyleGAN2, a state-of-the-art GAN

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Deliver higher efficiency, faster switching speeds, and reduced power losses, GaN-based power semiconductors offer significant advantages over traditional silicon-based devices. Their superior electrical properties make these devices highly attractive for applications in consumer electronics, data centers, automotive, renewable energy, RF, and even space systems.Several leading semiconductor manufacturers have developed GaN-based power solutions, each with distinct innovations and approaches to enhance performance and adoption. This article provides an overview of GaN technologies from EPC, Infineon Technologies, Navitas Semiconductor, Innoscience, and ROHM, along with key evaluation parameters and commercially available products.Purpose and scopeThe purpose of this article is to examine the GaN technology landscape by reviewing the solutions offered by five key manufacturers. Each manufacturer has developed GaN-based products with unique architectures, integration levels, and application focuses. This article details the fundamental characteristics of their GaN power devices and provides a comparative analysis of essential parameters to consider when evaluating these components. Additionally, a selection of commercially available devices from each manufacturer is highlighted.Efficient Power Conversion (EPC)Founded in 2007, EPC focuses on advancing power electronics by developing and commercializing GaN-based power devices. EPC is a pioneer in GaN technology, focusing on enhancement-mode GaN (eGaN) FETs and integrated circuits. Their GaN devices are widely used in applications requiring high-speed switching, including DC-DC converters, lidar systems, and Class-D audio amplifiers. 03.21.2025 03.19.2025 03.17.2025 EPC’s proprietary GaN-on-silicon technology delivers superior performance by reducing gate charge, output capacitance, and conduction losses. Their chip-scale packaging (CSP) approach minimizes parasitics, enhancing efficiency and thermal performance.A cross-section of EPC’s

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Upon reasonable request.ReferencesChen, K.J., et al.: GaN-on-Si power technology: devices and applications. IEEE Trans. Electron Devices 64, 779–795 (2017)Article Google Scholar Dang, K., et al.: A 58-GHz high-power and high-efficiency rectifier circuit with lateral GaN Schottky diode for wireless power transfer. IEEE Trans. Power Electron. 35, 2247–2252 (2020)Article Google Scholar Aklimi, E., et al.: Hybrid CMOS/GaN 40-MHz maximum 20-V input DC–DC multiphase buck converter. IEEE Trans. Power Electron. 52, 1618–1627 (2017) Google Scholar Jones, E.A., et al.: Review of commercial GaN power devices and GaN-based converter design challenges. IEEE J. Emerg. Sel. Top. Power Electron. 4, 707–719 (2016)Article Google Scholar Weiss, B., et al.: Monolithically-integrated multilevel inverter on lateral GaN-on-Si technology for high-voltage applications. 2015 IEEE Compound Semiconductor Integrated Circuit Symposium (CSICS).1–4 (2015)Li, X., et al.: 200 V enhancement-mode p-GaN HEMTs fabricated on 200 mm GaN-on-SOI with trench isolation for monolithic integration. IEEE Electron Device Lett. 38, 918–921 (2017)Article Google Scholar Wang, B., et al.: Integrated circuit implementation for a GaN HFET driver circuit. IEEE Trans. Ind. Appl. 46, 2056–2067 (2010)Article Google Scholar Bergveld, H.J., et al.: Integration trends in monolithic power ICs: application and technology challenges. 2015 IEEE Bipolar/BiCMOS Circuits and Technology Meeting - BCTM. 51, 1965–1974 (2016)Disney, D., et al.: High-voltage integrated circuits: history, state of the art, and future prospects. IEEE Trans. Electron Devices 64, 659–673 (2017)Article Google Scholar Li, X., et al.: Demonstration of GaN integrated half-bridge with on-chip drivers on 200-mm engineered substrates. IEEE Electron Device Lett. 40, 1499–1502 (2019)Article Google Scholar Reusch, D., et al.: Improving high frequency DC-DC converter performance with monolithic half bridge GaN ICs. IEEE Energy Conversion Congress and Exposition. 381–387 (2015)Jiang, Q., et al.: Substrate-coupled cross-talk effects on an AlGaN/GaN-on-Si smart power-IC platform. IEEE Trans. Electron Devices 61, 3808–3813 (2014)Article Google Scholar Tsai, C., et al.: Smart GaN platform: performance

Drag Your GAN ExplainedEdit Images Via Drag Drop

AbstractIt is necessary to achieve current matching for GaN-based CMOS-like inverters. However, due to the low hole mobility of GaN p-FET devices, the weak output capacity of GaN p-FET devices makes it difficult to obtain current matching with n-FET devices in the off-state, which hinders the development of GaN-based CMOS-like inverters. In this study, a GaN-based CMOS-like device with an AlGaN back barrier layer is designed and its off-state leakage current is compared with that without an AlGaN back-barrier layer. The results show that the 2DEG confinement in the GaN-based n-FET device with an AlGaN back barrier layer can be enhanced and the leakage current is reduced from 10–3 A to 10–6 A in the off-state. This is accomplished without influencing the current of the GaN-based p-FET device in the off-state, resulting in a good current consistency between the n-FET device and the p-FET device in the off-state. The static power consumption is 4.5 µW for GaN-based CMOS-like inverters with an AlGaN back barrier structure when it is operated at Vdd = 5 V. The rise time (tr) and fall time (tf) of the GaN-based CMOS-like inverters are 4 μs and 0.12 μs, respectively. The low noise margin (NML) is 1.90 V and the high noise margin (NMH) is 2.55 V. This work lays a foundation for the development of the future of GaN-based integrated ICs. Access this article Log in via an institution Subscribe and save Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Subscribe now Buy Now Price excludes VAT (USA) Tax calculation will be finalised during checkout. Instant access to the full article PDF. Similar content being viewed by others Data availabilityThe data that support the findings of this study are available from the corresponding author. Drag Your GAN offers an innovative and interactive way to experiment with image editing. How does it work? Drag Your Gan leverages StyleGAN2, a state-of-the-art GAN In this paper reading, we dive into Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold. Drag Your GAN introduces a novel