The Diffusion Explainer tool is an interactive webpage that allows users to generate an image from a text prompt. Users have control over various hyperparameters, including the seed and guidance scale, to customize the generated image. The text prompt should describe the desired image in detail to generate high-quality images. By changing the random seed, users can obtain different image representations. Moreover, adjusting the guidance scale can improve the adherence of the image to the text prompt but could limit the image's creativity. While the tool offers flexibility in creating images, it does not allow adjustments to other hyperparameters such as the total number of timesteps, image size, and the type of scheduler.
DragGAN is a novel approach for controlling generative adversarial networks (GANs) in order to synthesize visual content that meets users' needs. It offers precise and flexible controllability over the pose, shape, expression, and layout of generated objects. Unlike existing methods that rely on manual annotations or 3D models, DragGAN enables interactive control by allowing users to "drag" any points of an image to reach desired positions. The approach consists of two main components: feature-based motion supervision and a point tracking approach utilizing discriminative GAN features. By utilizing DragGAN, users can manipulate diverse categories of images, such as animals, cars, humans, and landscapes, with realistic outputs even in challenging scenarios like occluded content and deforming shapes.
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