junyanz/iGAN
IGAN
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 generation based on edge or color maps and manual refinement through seed-based candidate selection. It also includes diagnostic tools for debugging generative models by visualizing and probing their output space.
Features
- Generative Adversarial Image Synthesis - Implements a framework for high-resolution synthetic image synthesis using generative adversarial networks.
- Latent Vector Refinement - Updates the underlying latent representation in real-time based on user-drawn masks or brush strokes.
- Constraint-Based Image Synthesis - Provides the ability to produce synthetic images that match specific edge or color maps by optimizing latent vectors.
- Image-to-Latent Projections - Converts existing images into latent vectors using optimization or networks to enable generative manipulation.
- Latent Space Manipulations - Modifies latent representations of images to enable precise control over visual transitions and attribute interpolation.
- Generative Image Models - Uses pre-trained generative models to produce synthetic imagery across categories like landscapes, architecture, and fashion.
- Real-Time Image Generation - Enables the creation of synthetic images by guiding the generative process through manual input and real-time edits.
- Synthetic Image Generators - Provides a framework for producing model-based images across categories like landscapes and architecture using pre-trained networks.
- Semantic Image Synthesis - Produces synthetic images that adhere to specific semantic requirements like color or edge maps via automated scripts.
- Mask-Guided Image Editors - Offers a system for refining generated images using sketching, coloring, and warping brushes to guide the model.
- Generative Image Refinement - Provides coloring, sketching, and warping brushes to refine specific regions or shapes of generated images.
- Latent Space Interpolations - Calculates linear and spherical paths between latent vectors to create smooth visual transitions between images.
- Generative AI Interfaces - Provides a web-based tool for interactively creating and editing synthetic images using Generative Adversarial Networks.
- Scripted Generation - Supports producing multiple images based on specific color and edge maps through automated scripts.
- Latent Space Sampling - Generates a grid of varied image thumbnails by sampling different points around a specific region of the latent space.
- Generative Model Debuggers - Provides a visualization tool for analyzing the capabilities and limitations of a generative model through iterative output testing.
- Interpolation Visualizers - Provides a mechanism to visualize a sequence of transitions between a random image and a user edit via playback controls.
- Generated Result Selection - Allows users to browse and switch between multiple generated thumbnails to select the best result.
- Latent Space Explorers - Provides an interface for navigating and interpolating between latent vectors to visualize transformations in generative models.
- Generative Adversarial Networks - Interactive visual manipulation on the natural image manifold.
- Image Editing and Manipulation - Interactive visual manipulation on natural image manifolds.
- Image Generation - Interactive generative manipulation on natural image manifolds.
- Image Synthesis - Enables interactive visual manipulation on image manifolds.
