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Lakonlab

LakonLab is an open-source Python/PyTorch research codebase implementing the AsymFlow, pi-Flow and GMFlow papers, with components for experimenting with diffusion and flow-based image generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Lakonlab — 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 LakonLab when your work requires a local PyTorch research codebase for experimenting with its named flow and diffusion model implementations, rather than a hosted image-generation interface.

Who it is for

  • Researchers studying diffusion models, flow matching or few-step image generation.
  • Developers who want to inspect or adapt the official implementations of AsymFlow, pi-Flow and GMFlow.
  • Teams conducting model training and evaluation with distributed PyTorch.

Documented uses

  • Explore and compare implementations of AsymFlow, pi-Flow and GMFlow.
  • Run research experiments involving flow solvers and diffusion models.
  • Train or fine-tune models using distributed training, mixed precision and gradient accumulation.
  • Evaluate generated images using the listed online metrics and external benchmarks.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented installation environment is Linux with PyTorch 2.6 or later. Access to FLUX models requires accepting their Hugging Face conditions and logging in.

  • This is a codebase to install and work with locally, not a hosted generation service.
  • The README documents Linux and PyTorch 2.6+ as prerequisites.
  • Most I/O supports local filesystems and AWS S3; checkpoints can also be loaded from Hugging Face and HTTP/HTTPS URLs.
  • The repository includes distributed-training options, flow SDE schedulers and online evaluation metrics.

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

Install LakonLab in a Python environment

Install the repository and its dependencies in editable mode on a documented Linux and PyTorch setup.

Before you start

  • Linux; the README identifies Ubuntu 20 and above as tested.
  • PyTorch 2.6 or later.
  • A local clone of the LakonLab repository and Python 3.10 for the README's example.

Steps

  1. From the repository root, create and activate a Python 3.10 environment with the command below and source .venv/bin/activate.

    Documented command
    uv venv --python 3.10

  2. Install a suitable PyTorch version. The README gives the command below as an example.

    Documented command
    uv pip install torch==2.10.0 torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cu128

  3. Install LakonLab and its dependencies with the command below.

    Documented command
    uv pip install -e . --no-build-isolation

Expected outcome

LakonLab and its dependencies are installed in editable mode in the active environment.

Guide limitations

  • The README documents Linux and PyTorch 2.6+ as prerequisites; its commands are an installation example, not a compatibility guarantee.
  • FLUX model access separately requires accepting the relevant Hugging Face model conditions and authenticating with hf auth login.
Documentation 1 ↗
Evidence and transparency

Sources & review

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