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Interfaces for browsing and selecting the best output from a batch of generative model results.
Distinct from Image Selection Inputs: Existing candidates focus on UI input wrappers or AI region selection, not candidate selection from generated thumbnails.
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iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based interface for interactively creating and editing imagery across categories such as landscapes, architecture, and fashion using pre-trained models. The system enables precise control over visual output through latent space exploration, interpolation, and projection. Users can guide the generative process using an interactive editor featuring sketching, coloring, and warping brushes to refine specific regions or shapes in real-time. The project supports both automated scripted gene
Allows users to browse and switch between multiple generated thumbnails to select the best result.