Free alternatives to Leonardo AI

The canonical local workflow engine, complete with saved graphs, queues and every knob Leonardo hides.

Versus paying: It has deeper parameter control, but no Leonardo-style hosted asset library, team workspace, polished model-training flow, or instant cloud capacity.

Setup: Windows or macOS desktop installer, Windows portable archive, or manual Python install on Windows, macOS, or Linux

Watch for: A compatible GPU, enough VRAM and disk for multi-gigabyte models, and a working model/workflow must be chosen before useful output appears

124,207 GitHub stars (checked Aug 6, 2026)

Local models, histories, canvases and retained settings on Apple hardware; no team asset library.

Versus paying: It keeps models and history locally, but lacks Leonardo's shared team library, browser access, and managed training and compute.

Setup: Install the signed app from Apple's App Store on macOS, iPhone, or iPad

Watch for: Apple hardware is required, and the first model download is large; older devices can be slow or memory-limited

A simpler local queue with custom models and reproducible settings; fewer workflow toys, fewer invoices.

Versus paying: It is simpler locally, but its gallery, canvas, model management, and collaboration are much less polished than Leonardo's hosted workspace.

Setup: Run the Windows installer, or extract the Linux/macOS package and start it with ./start.sh

Watch for: Windows may show SmartScreen, macOS/Linux use a terminal launcher, and the first runtime plus model download can consume roughly tens of gigabytes

Organized boards, rich metadata, masks and reusable workflows for local and API-backed image models.

Versus paying: It has excellent boards and workflows, but team collaboration, hosted asset sharing, and Leonardo's turnkey training and generation infrastructure are not included.

Setup: Install through Invoke's launcher on Windows, macOS, or Linux, or deploy the documented Docker setup

Watch for: The launcher is straightforward, but the user still needs a compatible GPU, a large model download, and sometimes a gated-model login or provider API key

27,776 GitHub stars (checked Aug 6, 2026)

A native workflow app for local and BYOK image models, with projects and results kept on your machine.

Versus paying: It retains local workflows and results, but the user supplies compute and providers and gets no Leonardo-style shared asset catalog or managed training.

Setup: Install the macOS .dmg, signed Windows .exe, or Linux AppImage; Docker Compose is available for self-hosting

Watch for: No useful model or provider is bundled: the user must add an API key or download a multi-gigabyte local model, and some local backends add a large Python/Conda environment

450 GitHub stars (checked Aug 6, 2026)

A clean Generate tab, history, grids and the full workflow graph; parameters survive every rerun.

Versus paying: It saves settings and history, but its asset organization and collaboration are thinner than Leonardo's cloud workspace.

Setup: Run the Windows installer script, the Linux launch script, or install .NET/Python/Homebrew prerequisites and run launch-macos.sh on macOS; Docker is optional

Watch for: The backend and models still have to be downloaded, and non-Windows installs require command-line prerequisites such as .NET, Python, and Git

4,423 GitHub stars (checked Aug 6, 2026)