Waifu-diffusion on Hugging Face vs Drag Your GAN

Explore the frontiers of artificial intelligence with the hakurei/waifu-diffusion project. This text-to-image latent diffusion model, tailored for anime enthusiasts, leverages Stable Diffusion Pipeline and has been fine-tuned with high-quality anime imagery to create masterpieces of digital art. Licensed under CreativeML OpenRAIL-M, it extends the democratization of AI, allowing personal and commercial use with a focus on ethical applications.

Built for creativity and available to all, waifu-diffusion sets new standards for generative art. The project supports both Gradio Web UI and Google Colab for easy and accessible usage. Its potential uses span from personal entertainment to assisting in generative artwork creation, it's perfect for artists, developers, and anime fans alike.

Acknowledging the collaborative efforts of Stability AI and Novel AI, this platform is a testament to the advancement of open-source technology. With regular updates like waifu-diffusion v1.4 and with thousands of downloads monthly, the platform continues to evolve, offering a user-friendly experience, complete with example code, full model overview, and active community involvement.

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.