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.

Drag Your GAN lets you reshape a generated image by dragging handle points to target spots on the canvas. Instead of training on labeled pose data or 3D priors, DragGAN moves pixels inside a pretrained GAN's image manifold so edits stay photorealistic even when occluded regions get hallucinated. The project page hosts the SIGGRAPH 2023 paper, demo clips, and links to open source code.

Most GAN editors rely on fixed training annotations or 3D meshes, which limits what you can deform. DragGAN pairs feature-based motion supervision with discriminative point tracking so handle points follow your cursor while staying on the object surface. That makes it useful for nudging animal poses, facial expressions, car angles, and landscape layouts without retraining the generator.

Researchers, graphics students, and computer vision engineers use it to prototype interactive image editing workflows. The Max Planck Institute page distributes the paper PDF and GitHub repo under a non-commercial Creative Commons license, with contact details for the lead author.