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Alae

ALAE is a Python research implementation of adversarial latent autoencoders, which learn an encoder-generator mapping for image generation and representation learning.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Alae — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use ALAE when you want to examine or run the research implementation locally and have access to the documented CUDA-capable GPU and software prerequisites.

Who it is for

  • Researchers studying generative modeling or unsupervised representation learning
  • Developers exploring image generation, reconstruction or manipulation in computer vision

Documented uses

  • Explore an adversarial latent autoencoder research implementation.
  • Run an interactive demo with pretrained models and a selected dataset configuration.
  • Study face-image generation, reconstruction and manipulation, including the StyleALAE approach described in the paper.
Before you choose

Requirements & limits

  • This is a repository-based implementation, so the interactive demo runs in a local environment rather than through a hosted service.
  • The demo requires a CUDA-capable GPU, PyTorch 1.3.1 or later, and CUDA/cuDNN drivers.
  • The repository lists a PyTorch 1.4.0 badge and includes a requirements file.
  • FFHQ is the demo's default configuration; other configurations may require new principal direction vectors for attributes.

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

Set up and launch the interactive demo

Install the listed Python dependencies, download pretrained models, and start the documented interactive demo.

Before you start

  • A CUDA-capable GPU
  • PyTorch 1.3.1 or later
  • CUDA/cuDNN drivers
  • A Python environment for installing the repository's requirements

Steps

  1. From the repository root, install the packages listed in requirements.txt with pip.

  2. Download pretrained models by running python training_artifacts/download_all.py.

  3. From the repository root, launch the demo with python interactive_demo.py. The default configuration is for FFHQ.

Expected outcome

The interactive demo starts using the default FFHQ configuration, subject to the documented prerequisites.

Guide limitations

  • For configurations other than FFHQ, the README says new principal direction vectors for attributes are needed.
  • The repository instructs users to run scripts from its root directory.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
podgorskiy/ALAE · Official homepage title · retained in its source language
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