Leonardo.Ai vs Drag Your GAN

Leonardo.Ai turns text prompts into images and video inside a browser or mobile app built for creative production work. You pick from platform models like Lucid Origin, Phoenix 1.0, and Veo 3, or train a personal model from your own reference images, then generate art, edit shots, upscale resolution, and animate stills without leaving the workspace.

Most image generators sell tokens and stop there. Leonardo stacks image generation, video motion tools, a universal upscaler, and precision editing in one subscription, with Premium and Ultimate plans offering unlimited relaxed generation on select models after fast tokens run out. That slower queue keeps you creating without buying more credits, which matters when you are iterating dozens of concept frames for a client deck.

The platform targets artists, designers, marketers, and game studios that need style-consistent output at volume. Case studies on the site cite Ducati fan campaigns, Gamepack cutting production costs by 50%, and Springbok speeding motion asset delivery. Team plans add shared token pools, and a developer API offers pay-as-you-go credits starting at $5.

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.