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Igan

iGAN is a research implementation of an interactive image-generation interface based on generative adversarial networks. It lets users guide generated images with color, sketch and warp strokes, and provides candidate results and visualizations for exploring what the model can produce.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Igan — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use iGAN when exploring brush-guided GAN image generation or studying how a generative model responds to visual constraints.

Who it is for

  • Researchers exploring interactive image generation and GAN behavior.
  • Developers studying or adapting a brush-based computer-vision demo.
  • Users seeking to guide generated images through local edits rather than text prompts.

Documented uses

  • Generate images guided by the color and shape of brush strokes.
  • Compare candidate outputs that satisfy a set of user edits.
  • Inspect the visual content and limitations of a generative model through interaction.
  • Continue editing by treating a current result as an additional constraint.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The documented use is a local Python setup requiring a GPU stack for real-time operation.

  • This is a research code project to set up locally, rather than a hosted image-generation service.
  • The interface depends on legacy-oriented components including Theano and PyQt4, with Python 2 described in the README.
  • The example model is DCGAN outdoor_64; the README recommends checking real and generated samples to understand the kinds of images the model can produce.
  • The interface includes candidate selection, interpolation controls, restart, save-to-webpage and edit visibility controls.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare and launch the documented iGAN demo

Set up the repository and outdoor_64 model, then launch the interactive interface.

Before you start

  • Python project dependencies listed by the repository: NumPy, OpenCV, Theano, PyQt4, Qdarkstyle and dominate.
  • A GPU with CUDA and cuDNN is specified; the README says a decent GPU is required for real-time operation.
  • Git and a shell for the documented clone, model-download and launch commands.

Steps

  1. Clone the repository with the command below and enter the iGAN directory.

    Documented command
    git clone https://github.com/junyanz/iGAN

  2. Install the listed Python libraries and GPU dependencies, following the repository’s requirements notes.

  3. Download the example model with the command below.

    Documented command
    bash ./models/scripts/download_dcgan_model.sh outdoor_64

  4. Launch with THEANO_FLAGS='device=gpu0, floatX=float32, nvcc.fastmath=True' python iGAN_main.py --model_name outdoor_64.

Expected outcome

The documented launch command opens the iGAN interactive image-generation interface using the outdoor_64 model.

Guide limitations

  • The README describes the code as Python 2 and gives separate Python 3 package notes; it does not establish that the documented environment works with current dependency releases.
  • The GPU example is GTX Titan X with CUDA 7.5 and cuDNN 5; the repository warns remote desktop use may introduce display artifacts and latency.
  • This is documented setup guidance, not a verified execution or compatibility result.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
junyanz/iGAN · Official homepage title · retained in its source language
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