Leonardo AI alternatives: 6 free & open-source picks
Curated, verified, and actually free. No trials, no crippled tiers. Star counts and last-activity dates checked by hand, not scraped and forgotten.
The canonical local workflow engine, complete with saved graphs, queues and every knob Leonardo hides.
licenseGPL-3.0
runsyour machine or their cloud
installWindows or macOS desktop installer, Windows portable archive, or manual Python install on Windows, macOS, or Linux
enginesLocal and downloadable image, video, audio, 3D, and text models including Stable Diffusion/SDXL/SD3.5, FLUX, Wan, LTX-Video, HunyuanVideo, CogVideoX, and Mochi; optional API nodes connect to hosted providers
dataModels, inputs, outputs, and workflow JSON live in the local ComfyUI data tree; generated media can embed workflow and seed metadata, while API nodes transmit selected inputs to their provider
the catch vs Leonardo AIIt has deeper parameter control, but no Leonardo-style hosted asset library, team workspace, polished model-training flow, or instant cloud capacity.
setupA compatible GPU, enough VRAM and disk for multi-gigabyte models, and a working model/workflow must be chosen before useful output appears
facts verified 2026-08-10
Organized boards, rich metadata, masks and reusable workflows for local and API-backed image models.
licenseApache-2.0
runsyour machine or their cloud
installInstall through Invoke's launcher on Windows, macOS, or Linux, or deploy the documented Docker setup
enginesLocal models including Stable Diffusion, SDXL, SD3.5, FLUX, CogView, Z-Image, Krea, Anima, Qwen Image, Ideogram, and segmentation models; selected hosted models such as GPT Image, Gemini/Nano Banana, and Wan use API credentials
dataModels, images, boards, canvas state, prompts, metadata, and a SQLite database live in the local Invoke data directory; API-backed nodes send only the selected prompt and image context to their provider
the catch vs Leonardo AIIt has excellent boards and workflows, but team collaboration, hosted asset sharing, and Leonardo's turnkey training and generation infrastructure are not included.
setupThe 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
facts verified 2026-08-10
A clean Generate tab, history, grids and the full workflow graph; parameters survive every rerun.
licenseMIT
runsyour server
installRun the Windows installer script, the Linux launch script, or install .NET/Python/Homebrew prerequisites and run launch-macos.sh on macOS; Docker is optional
enginesUses ComfyUI as its main backend for Stable Diffusion, SDXL, FLUX, Wan, LTX, MiniMax, ACE-Step, and other image/video/audio workflows; an Automatic1111-compatible backend is optional
dataConfiguration, users, models, workflows, generated media, and history live in the local SwarmUI installation and configured model/output directories
the catch vs Leonardo AIIt saves settings and history, but its asset organization and collaboration are thinner than Leonardo's cloud workspace.
setupThe backend and models still have to be downloaded, and non-Windows installs require command-line prerequisites such as .NET, Python, and Git
facts verified 2026-08-10
A native workflow app for local and BYOK image models, with projects and results kept on your machine.
licenseAGPL-3.0
runsyour machine or their cloud
installInstall the macOS .dmg, signed Windows .exe, or Linux AppImage; Docker Compose is available for self-hosting
enginesLocal Ollama, llama.cpp/GGUF, MLX, Nunchaku, Hugging Face, and diffusion/media models, plus BYOK providers such as OpenAI, Anthropic, Gemini, FAL, Replicate, KIE, ElevenLabs, and Hugging Face
dataWorkflows, projects, assets, files, provider settings, and vector indexes live locally in YAML, SQLite/SQLite-vec, and application storage; optional S3 or Supabase storage is supported, and cloud nodes transmit their inputs
the catch vs Leonardo AIIt retains local workflows and results, but the user supplies compute and providers and gets no Leonardo-style shared asset catalog or managed training.
setupNo 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
facts verified 2026-08-10
Local models, histories, canvases and retained settings on Apple hardware; no team asset library.
licenseGPL-3.0
runsyour machine or their cloud
installInstall the signed app from Apple's App Store on macOS, iPhone, or iPad
enginesRuns supported Stable Diffusion, SDXL, FLUX, and video models locally, including imported checkpoints and LoRAs; optional cloud compute can offload generation
dataProjects, models, history, and generated media live in the app sandbox; on macOS model files are under the app container, while cloud offload sends prompts and media for processing
the catch vs Leonardo AIIt keeps models and history locally, but lacks Leonardo's shared team library, browser access, and managed training and compute.
setupApple hardware is required, and the first model download is large; older devices can be slow or memory-limited
facts verified 2026-08-10
A simpler local queue with custom models and reproducible settings; fewer workflow toys, fewer invoices.
licenseproprietary, free
runson your machine
installRun the Windows installer, or extract the Linux/macOS package and start it with ./start.sh
enginesLocal diffusion models including Stable Diffusion variants, FLUX, Z-Image, ERNIE, Lumina, Anima, and supported quantized/custom checkpoints
dataModels, settings, prompts, and generated images remain in the local installation and output directories; the app contacts package/model hosts for dependencies and downloads but says it does not send prompts or images for analytics
the catch vs Leonardo AIIt is simpler locally, but its gallery, canvas, model management, and collaboration are much less polished than Leonardo's hosted workspace.
setupWindows may show SmartScreen, macOS/Linux use a terminal launcher, and the first runtime plus model download can consume roughly tens of gigabytes
facts verified 2026-08-10
last updated 2026-08-10 · no votes, no pay-to-list · just what's real
| tool | license | running it | platforms | stars | active |
|---|---|---|---|---|---|
| ComfyUI | GPL-3.0 | one-click install | web, macos, windows, linux, self-hosted | 124,207 | 2026-08 |
| InvokeAI | Apache-2.0 | one-click install | web, macos, windows, linux, self-hosted | 27,776 | 2026-08 |
| SwarmUI | MIT | one-click install | web, macos, windows, linux, self-hosted | 4,423 | 2026-08 |
| NodeTool | AGPL-3.0 | one-click install | web, macos, windows, linux, self-hosted, cli | 450 | 2026-08 |
| Draw Things | GPL-3.0 | one-click install | macos, ios | — | — |
| Easy Diffusion | proprietary, free | one-click install | web, macos, windows, linux, self-hosted | — | — |
People still pay for Leonardo AI because the product value is the model and compute fleet, not the prompt box around it. The recurring cost buys GPU procurement, model licensing, safety filters, queueing, storage, and rapid model replacement, not just the visible interface. If none of that applies to you, any pick above saves $144 a year.
Is there a free alternative to Leonardo AI?
Yes: ComfyUI, InvokeAI, SwarmUI and 3 more. Every tool on this page is either open source or genuinely free to use long-term: no trials, no crippled tiers.
Should I just build my own Leonardo AI?
Our verdict is NO TANTO. A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Leonardo AI, organize local or API-backed image-generation workflows and retain parameters. The hard boundary is proprietary models, hosted gpu capacity, training tools, and asset ecosystem, plus frontier models, compute, and data. If you'd rather build, the exact one-shot prompt is free at puedovibecodearlo.app/leonardo-ai.
