DreamShaper on CivitAI vs Drag Your GAN

Discover the power of DreamShaper - V∞, an innovative AI generative model built for artists and AI enthusiasts alike. With its latest version release, DreamShaper offers a suite of tools designed to push the envelope of photorealism and art creation. Whether you aim for lifelike landscapes, intricate 3D models, or photorealistic inpainting, DreamShaper is your go-to solution for high-quality AI-generated art. Thanks to its ability to handle Character LoRA and a robust capability for NSFW content without sacrificing versatility, it's a powerhouse for creativity.

DreamShaper thrives on versatility, aiming to make "a better Stable Diffusion" by providing a model capable of everything from art to anime. Despite catering to art and illustrations being the primary goal, the diverse set of functionalities means being good at everything comes as a bonus. Available across several GPU-accelerated platforms, such as Mage.space and Sinkin.ai, and offering downloadable checkpoints, DreamShaper is both accessible and powerful.

Above all, the community spirit shines through Lykon's message: by showcasing improvements like enhanced LoRA support and realism, this model continues the journey of providing an open-source alternative to proprietary services. Additionally, DreamShaper encourages engagement and support through social features like Patreon, ensuring a community-driven development process.

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.