mo-di-diffusion on Hugging Face vs Drag Your GAN

mo-di-diffusion is a fine-tuned Stable Diffusion 1.5 checkpoint on Hugging Face that renders characters and scenes in a modern animated-film look. Creator nitrosocke trained it on screenshots from a popular animation studio using DreamBooth with prior-preservation loss and text-encoder training over 9,000 steps. Add the token modern disney style to your prompt to trigger the aesthetic.

General SD 1.5 checkpoints aim for photorealism or broad illustration styles. mo-di-diffusion narrows hard on that glossy character-animation look, which is why sample prompts for game heroes, animals, and landscapes all lean cinematic rather than photographic. The weights load through the standard Diffusers StableDiffusionPipeline, so you can run it locally, in Colab, or inside Hugging Face Spaces without a proprietary API.

Artists, hobbyists, and developers experimenting with character concepts use mo-di-diffusion when they want Disney-adjacent renders without commissioning custom model training. The model card ships Python sample code, links to Gradio demos, and notes ONNX, MPS, and FLAX export options. It is free to download under the CreativeML OpenRAIL-M license with commercial use allowed subject to the license harm restrictions.

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.