ArtBot vs Drag Your GAN

ArtBot is a free browser client for image generation with Stable Diffusion through the AI Horde, a distributed network of volunteer GPUs. You open the site, write a prompt, pick from 154 community-hosted models, and get images back without installing software or creating an account. Everything runs in the browser, and your generations stay stored locally in IndexedDB rather than on ArtBot servers.

Most image generation tools either need a local GPU or bill you for cloud compute. ArtBot routes work through AI Horde instead, so you trade hardware costs for queue time. That makes it unusually practical if you want ControlNet, img2img, inpainting, and live paint in one free interface but do not want to run Automatic1111 on your own machine. The trade-off is speed and reliability depend on how many workers are online, not a fixed SLA.

It fits hobbyists experimenting with prompts, artists who want Horde access without juggling CLI tools, and contributors who run GPU workers to earn kudos for faster queue priority. Dave Schumaker built it as an open source side project, and the about page reports more than 40 million images generated through the service.

In the realm of synthesizing visual content to meet users' needs, achieving precise control over pose, shape, expression, and layout of generated objects is essential. Traditional approaches to controlling generative adversarial networks (GANs) have relied on manual annotations during training or prior 3D models, often lacking the flexibility, precision, and versatility required for diverse applications.

In our research, we explore an innovative and relatively uncharted method for GAN control – the ability to "drag" specific image points to precisely reach user-defined target points in an interactive manner (as illustrated in Fig.1). This approach has led to the development of DragGAN, a novel framework comprising two core components:

Feature-Based Motion Supervision: This component guides handle points within the image toward their intended target positions through feature-based motion supervision.

Point Tracking: Leveraging discriminative GAN features, our new point tracking technique continuously localizes the position of handle points.

DragGAN empowers users to deform images with remarkable precision, enabling manipulation of the pose, shape, expression, and layout across diverse categories such as animals, cars, humans, landscapes, and more. These manipulations take place within the learned generative image manifold of a GAN, resulting in realistic outputs, even in complex scenarios like generating occluded content and deforming shapes while adhering to the object's rigidity.

Our comprehensive evaluations, encompassing both qualitative and quantitative comparisons, highlight DragGAN's superiority over existing methods in tasks related to image manipulation and point tracking. Additionally, we demonstrate its capabilities in manipulating real-world images through GAN inversion, showcasing its potential for various practical applications in the realm of visual content synthesis and control.