Open-source alternatives to Midjourney: 3 AI image generation tools compared
Replacing a hosted image generator means owning the GPU, the model files and the update path, and the three tools here differ mainly in how much of the product you assemble yourself. InvokeAI is the one most readers should look at first, because the canvas, the gallery and the node graph arrive in one application under a permissive licence, while ComfyUI is the choice when you want every parameter exposed.
What Midjourney does, and why people look elsewhere
Midjourney is a hosted image generator: prompts submitted through its web app or Discord return images from the vendor's own models.
- subscription-only, with generation limits per tier
- the models cannot be run locally
- generated images are public unless you pay for a higher tier
The candidates at a glance
| Project | Licence | Maintenance | Language | GitHub stars | Install | Best for |
|---|---|---|---|---|---|---|
| Comfy-Org/ComfyUI | GPL-3.0Copyleft: distributing it means sharing source | Commits in the last six monthsLast push October 4, 2026 | Python | 136,088 | See analysis | A technical artist or platform engineer who needs per-node control over diffusion pipelines, reproducible workflows saved as JSON, and a local API to put those workflows into a production pipeline, and who is willing to manage Python dependencies and model files directly. |
| AUTOMATIC1111/stable-diffusion-webui | AGPL-3.0Network copyleft: hosting a modified version means sharing source | No commits for six monthsLast push March 2, 2026 | Python | 165,041 | See analysis | Someone who wants a familiar form-based Stable Diffusion interface with a large extension surface: txt2img, img2img, inpainting, outpainting, prompt matrix, X/Y/Z plots, attention syntax, an Extras tab with upscalers and face restoration, and an API. |
| invoke-ai/InvokeAI | Apache-2.0Permissive: commercial use allowed | Commits in the last six monthsLast push October 11, 2026 | Python | 28,562 | See analysis | Artists and production teams who want a finished workspace rather than a toolkit: a locally hosted React interface with the Unified Canvas, a gallery that keeps prompt metadata with each image, workflow and node management, and a listed set of model families from SD 1.5 through the Flux.2 and Qwen Image lines. |
Each alternative in detail
Comfy-Org/ComfyUIThe most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
- Best for
- A technical artist or platform engineer who needs per-node control over diffusion pipelines, reproducible workflows saved as JSON, and a local API to put those workflows into a production pipeline, and who is willing to manage Python dependencies and model files directly.
- What you give up
- There is no one-click consumer experience: our analysis of ComfyUI says the cost is that you assemble the pipeline yourself, and it tells you to check Python 3.10 or newer, that main.py runs from a clone, and that the workflow JSON you intend to ship loads without a missing custom node. A workflow is only as reproducible as the custom nodes it depends on, and the licence is GPL-3.0, which is the strictest of the three here.
AUTOMATIC1111/stable-diffusion-webuiStable Diffusion web UI
- Best for
- Someone who wants a familiar form-based Stable Diffusion interface with a large extension surface: txt2img, img2img, inpainting, outpainting, prompt matrix, X/Y/Z plots, attention syntax, an Extras tab with upscalers and face restoration, and an API.
- What you give up
- The cadence is the problem. The release history shows v1.10.0 in July 2024 and v1.10.1 in February 2025, our analysis of stable-diffusion-webui describes the last push to master as 2025-02-09 and tells you to treat it as a stable, slow-moving codebase, and the recorded last push for the repository is 2026-03-02. You also pin your own Python, PyTorch, Gradio and transformers versions, and the licence is AGPL-3.0.
invoke-ai/InvokeAIInvokeAI gives artists and production teams a node-based workspace for creating images with Stable Diffusion models.
- Best for
- Artists and production teams who want a finished workspace rather than a toolkit: a locally hosted React interface with the Unified Canvas, a gallery that keeps prompt metadata with each image, workflow and node management, and a listed set of model families from SD 1.5 through the Flux.2 and Qwen Image lines.
- What you give up
- It is a local install with a GPU, on Python 3.11 or 3.12, and our analysis of InvokeAI records no install command for it, so the path comes from the separate launcher installer and the documentation. Three entries in its model list, Nano Banana, GPT Image and Wan, are marked API only and route through an external service rather than a local checkpoint, which matters if the requirement is that nothing leaves the building.
What a reader searching DALL-E is actually comparing
Midjourney is a hosted image generator: prompts submitted through its web app or through Discord come back as images from the vendor's own models. Three documented things send people looking elsewhere. It is subscription-only, with generation limits per tier. The models cannot be run locally. And generated images are public unless you pay for a higher tier. The same three points explain a search for DALL-E, because that product answers the same request from a different vendor.
All three tools here remove the second point completely and change the shape of the first and the third. What you get instead is not a service with different limits but a pipeline you run: you choose the model weights, the hardware and the interface, and the images stay where you generated them because they never leave it.
The honest comparison is therefore not feature for feature. A hosted generator gives you one curated model and a polished front end. These three give you a choice of open models and a front end you configure, and the gap that creates is effort. Midjourney and DALL-E are a prompt box; ComfyUI, AUTOMATIC1111 and InvokeAI are a control panel with a model library behind it.
Scope is worth stating. This page covers three tools that generate images on hardware you control. It does not cover a hosted generator you can only subscribe to, and it does not cover a wrapper whose only job is to forward prompts to someone else's model, because neither changes where the pixels are computed.
Three ways to run the same kind of pipeline
The three differ in how the pipeline is expressed and who edits it.
ComfyUI is a node graph. Every step is a node, workflows are saved as JSON, and the README describes reusable subgraphs, workflow templates, App Mode for exposing a complex graph through a simple interface, and a local API for integrating workflows into applications. It also lists asynchronous queueing, partial graph re-execution, smart VRAM and RAM management, model offloading and support for quantized models, which are the features that decide whether a batch job finishes. Its model list is organised by task rather than by family, with separate sections for image generation, image editing, video, audio, 3D and text. The README states that the core runs fully offline and downloads nothing unless you ask it to, with a flag to disable the optional paid API nodes.
AUTOMATIC1111 is a form. It is a Gradio interface for Stable Diffusion with the classic feature set: txt2img, img2img, inpainting, outpainting, the prompt matrix, the X/Y/Z plot, attention weighting syntax, negative prompts, styles, and an Extras tab holding upscalers and face restoration tools. Its extension system is the point of it, and the README notes that running arbitrary Python code from the interface needs a command-line flag.
InvokeAI sits between the two. It runs a local web server with a React interface, and its README describes a Unified Canvas with inpainting, outpainting and brush tools, a gallery that keeps rich metadata with each image, and workflow and node management on top. Its model list is explicit, and the last three entries are labelled API only.
Solo, small team, compliance, embedding
Solo. InvokeAI is the least assembly: the README's route is to download a launcher installer, and the analysis describes a Python 3.11 or 3.12 environment with a GPU. ComfyUI is close behind with a desktop application for Windows and macOS, and AUTOMATIC1111 has a one-click script that still expects Python and git already installed.
Small team. The question is whether the team wants a shared workspace or a shared pipeline. A shared workspace is InvokeAI, with its gallery and boards. A shared pipeline is ComfyUI, where a workflow is a file: our analysis of ComfyUI asks you to verify that the workflow JSON you intend to ship loads without a missing custom node, which is the check that keeps a team from being blocked by somebody's laptop.
Company with compliance needs. Every one of the three computes locally, so the question becomes what leaves the site anyway. ComfyUI's README states the core downloads nothing unless requested and offers a flag to disable the paid API nodes. InvokeAI's list marks Nano Banana, GPT Image and Wan as API only, so those entries are an outbound call by design. AUTOMATIC1111 has no equivalent, which is simpler, and it is the one with the strictest licence.
Developer embedding it. ComfyUI's README describes a local API for integrating workflows into applications and API endpoints for production pipelines, with App Mode as the way to hide the graph. InvokeAI's workflows are designed to be shared between users. AUTOMATIC1111 exposes an API and custom scripts, which is how most of its automation actually happens.
What self-hosting each one costs
The install commands describe the operational shape of each project.
ComfyUI: clone, create a virtual environment, install from requirements.txt. The cost is the dependency tree and the model files, and the analysis asks you to check Python 3.10 or newer and that you can run main.py from a clone before you build anything on it. The README also names the hardware paths: manual install covers NVIDIA, AMD, Intel, Apple Silicon and Ascend, and the desktop application covers Windows and macOS.
AUTOMATIC1111: clone, then run a batch script on Windows. The README is candid that you still have to install Python and git, and the analysis tells you to pin your own Python and PyTorch versions and to check that the Gradio and transformers versions in requirements.txt resolve against your installed torch. That last check is the one that decides whether your afternoon goes well.
InvokeAI: download the launcher. Our analysis of InvokeAI records no install command for it, and says to verify that your GPU and driver combination is covered by the installation documentation. The separate launcher is also a component to track: it is distributed as its own release, so an install is two things moving independently.
The cost all three share is VRAM. Our analysis of ComfyUI tells you not to adopt it if you cannot check GPU memory headroom before loading a large checkpoint, and that is the honest constraint on this page: none of these tools makes a big model fit on a small card.
ComfyUI Cloud and the paid API nodes in its README exist for people who cannot afford local hardware, which tells you the maintainers consider local hardware a real constraint rather than a default.
Licences, and the model underneath
The three licences are genuinely different, and the difference matters in one specific case.
InvokeAI is Apache-2.0, which is the most permissive of the three and is what its README means by a commercially-friendly license. AUTOMATIC1111 is AGPL-3.0, and our analysis of stable-diffusion-webui tells you to confirm those obligations are acceptable for how you intend to distribute anything built on top of it. ComfyUI is GPL-3.0, which is the middle position: it obliges you when you distribute, and it is the licence most often met by people shipping a modified build.
The obligation that bites in practice is hosting a modified instance for other people. With AGPL-3.0, a modification you serve over a network has to be published under the same terms. If your plan is to run one of these internally and never hand it to a third party, the difference is theoretical. If you plan to offer it as a service, it is not.
Underneath all three sits a licence the application does not control. Each of these tools is a harness, and the weights you download have their own terms, which differ by family and by what you intend to do with the output. That is a separate check from the one you do on GitHub, and it is the one most likely to surprise a team that assumed an Apache or GPL repository meant the images were free to use.
One more boundary is worth naming. ComfyUI's partner nodes and paid API nodes, and InvokeAI's API-only model entries, are paths to closed models. Using them is convenient and it means the request leaves your machine, which is a decision about data rather than about open source.
Bottom line
Start with InvokeAI if you want a finished workspace you can hand to an artist, with the canvas, the gallery and the workflow editor in one application and an Apache-2.0 licence that will not complicate a distribution plan. Choose ComfyUI when reproducibility and API integration matter more than a polished surface, and budget for the JSON workflows and custom nodes that make it maintainable. AUTOMATIC1111 remains a reasonable choice for a Stable Diffusion front end with a familiar form and a large extension surface, provided you accept a slow-moving codebase and pin your own dependencies. Before committing, check the VRAM headroom for the model you intend to load, confirm the licence of the weights as well as the licence of the tool, and decide who runs the upgrade when a new model family appears.