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Advanced GAN¤

Status: Exploratory workflow Device: GPU-optional

This walkthrough uses lower-level Artifex GAN building blocks and a custom training loop to compare several GAN families on MNIST-like image data. It does not instantiate the top-level ConditionalGAN, WGAN, DCGAN, or LSGAN owners end to end, so it is published as exploratory material rather than a canonical runtime-backed tutorial.

Files¤

Run It¤

python examples/generative_models/image/gan/advanced_gan.py
jupyter lab examples/generative_models/image/gan/advanced_gan.ipynb

What This Workflow Actually Uses¤

  • lower-level Artifex GAN building blocks such as ConditionalGenerator, ConditionalDiscriminator, WGANGenerator, WGANDiscriminator, DCGANGenerator, DCGANDiscriminator, LSGANGenerator, and LSGANDiscriminator
  • a custom training loop defined in the example itself
  • local MNIST bootstrap through Hugging Face plus Grain rather than a retained Artifex example data facade
  • Artifex adversarial loss helpers and gradient-penalty utilities

Why It Is Exploratory¤

  • the example compares lower-level component stacks instead of teaching one canonical top-level owner story
  • the data-loading and orchestration logic is owned locally by the example
  • environment-specific dataset bootstrap issues can still affect execution before training begins

Use This When¤

Use this pair if you want to inspect how the lower-level GAN components fit together, adapt the local training loop, or compare family-specific generator and discriminator stacks in one place.

If you want a retained runtime-backed GAN tutorial instead, start with Simple GAN and the broader GAN API and user-guide docs.