ComfyUI vs InvokeAI: Node Graphs for Builders vs a Canvas for Artists
Both are open-source, Python-based, node-driven front ends for diffusion models, but ComfyUI exposes the whole pipeline as a graph you assemble, while InvokeAI wraps nodes in a hosted web UI with a Unified Canvas for inpainting and outpainting. They can be complementary: ComfyUI is the more general engine, InvokeAI the more finished studio.
At a glance
| Project | Comfy-Org/ComfyUI | invoke-ai/InvokeAI |
|---|---|---|
| Licence | GPL-3.0Copyleft: distributing it means sharing source | Apache-2.0Permissive: commercial use allowed |
| Maintenance | Commits in the last six monthsLast push September 25, 2026 | Commits in the last six monthsLast push September 27, 2026 |
| Language | Python | Python |
| GitHub stars | 134,951 | 28,304 |
| Read more | Our analysisGitHub | Our analysisGitHub |
Which one to choose
Choose ComfyUI if you need per-node control over diffusion pipelines, want the widest native model coverage including video, audio, 3D and text, or need to expose a workflow through an HTTP API and App Mode. It suits engineers who can manage Python 3.10+ dependencies and model files themselves.
Choose InvokeAI if you are an artist or a small production team that wants a locally hosted web UI with a Unified Canvas for inpainting, outpainting and brush work, plus a node workflow system behind it. It suits people who want a finished workspace rather than a graph they must build.
Two node graphs, two different centres of gravity
ComfyUI describes itself as a modular diffusion model GUI, API and backend with a graph/nodes interface. Its README frames the project as an engine for visual professionals who demand control over every model and every parameter, and the graph is the product: you place nodes, wire them, and save the result as JSON. The README lists reusable subgraphs, workflow templates, App Mode and a local API as features of that same graph. InvokeAI also ships a node-based architecture, and its README calls the workflow system a way to combine node-based workflows with the ease of a UI. The difference is where the default experience sits. In ComfyUI the graph is the interface. In InvokeAI the graph is one feature of a locally hosted web server and React UI, alongside the Unified Canvas, board and gallery management, and model manager. If you strip both projects to their core loop, ComfyUI asks you to think in execution order and tensor shapes, while InvokeAI asks you to think in images on a canvas and reach for nodes when the canvas is not enough.
Model coverage: breadth against curation
ComfyUI's README lists native support for a very wide set of models across modalities: image generation (SD 1.5, SDXL, SD3.5, Flux.1, Flux.2, Qwen Image, Z-Image, Hunyuan Image 2.1, HiDream, Lumina Image 2.0, Chroma and others), image editing (Flux Kontext, Qwen Image Edit, OmniGen2), video (Wan 2.1 and 2.2, LTX-Video 2 and 2.3, HunyuanVideo 1.5, CogVideoX, Mochi), audio and audio-video (ACE-Step 1.5, Stable Audio 3, MiniMax H3), 3D and vision (Hunyuan3D 2.1, TripoSplat, SeedVR2, SUPIR, Depth Anything 3, SAM 3 and 3.1), and text generation (Gemma 3 and 4, Qwen3, Qwen3.5, Qwen3-VL). It also documents loading checkpoints or separate diffusion models, VAEs, text encoders, LoRAs, ControlNets, adapters and upscalers, and built-in tools for inpainting, outpainting, model merging, upscaling, frame interpolation, segmentation and depth estimation. InvokeAI's README lists a narrower but still substantial model set, concentrated on image work: SD 1.5, SD 2.0, SDXL, SD 3.5 Medium and Large, CogView 4, Flux.1 Dev, Schnell, Kontext, Krea, Redux and Fill, Flux.2 Dev and Klein variants, Z-Image Turbo and Base, Krea 2 Turbo and Raw, Anima, Qwen Image and Qwen Image Edit, Ideogram 4, ERNIE-Image and ERNIE-Image-Turbo. It notes that Nano Banana, GPT Image and Wan are API only. InvokeAI also supports ckpt, diffusers and some gguf formats, plus upscaling, embeddings, a model manager and SAM/SAM2 segmentation. The practical reading: ComfyUI is the broader engine across media types; InvokeAI is deliberately narrower and deeper on image editing and canvas workflows.
Getting each one running
ComfyUI documents four paths. A desktop application for Windows and macOS, described as the easiest way to start. A Windows portable package that the README says gets the latest commits and is completely portable. A manual install that supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend). And Comfy Cloud, described as the official paid cloud version for people who cannot afford local hardware. The README states that core runs fully offline and does not download anything unless you request it, and that you can pass --disable-api-nodes to turn off the optional paid Comfy API nodes. InvokeAI's README points to a separate launcher repository and says to download the launcher to get started, then directs users to an FAQ for common installation problems. Its README does not document a portable package or a first-party cloud tier. The verification burden differs. With ComfyUI you should confirm your Python is 3.10 or newer, that you can run main.py from a clone, and that the workflow JSON you intend to ship loads without a missing custom node. With InvokeAI you should confirm the launcher supports your operating system and check the FAQ for known installation issues. Neither README documents rollback procedures.
Operations, scaling and integration
ComfyUI's README claims efficient local execution through asynchronous queueing, partial graph re-execution, smart VRAM and RAM management, model offloading and support for quantized models. Those are the mechanisms that matter when you run long jobs or share one GPU across several users. The same README states that the most sophisticated workflows can be exposed through a simple UI using App Mode, and that the project integrates into production pipelines through API endpoints. That combination, a graph plus a local HTTP API plus a simplified front end, is why ComfyUI tends to end up behind internal tools. InvokeAI's README describes a locally hosted web server and React UI, a board and gallery system with rich image metadata for recalling prompts and settings, and drag-and-drop of images onto image-based UI elements. It does not describe asynchronous queueing, partial graph re-execution, model offloading or quantized-model support. It does list upscaling tools, an embedding manager and a model manager. If your requirement is a shared service with an API contract, ComfyUI documents that surface. If your requirement is a workspace where several artists iterate on images, InvokeAI documents that surface. The two are not competing on the same axis.
Where each falls short
ComfyUI's cost is assembly. The graph gives you control over every model and parameter, and in exchange you build the pipeline, choose the checkpoints, and keep custom nodes working across updates. The README does not document rollback, so a workflow that depends on a specific node version is a risk you manage outside the project. The breadth of model support also means breadth of model files to source and store. ComfyUI is a poor fit if you want a one-click consumer tool, or if you cannot check GPU memory headroom before loading a large checkpoint. InvokeAI's constraints run the other way. Its README is explicit that some models are API only (Nano Banana, GPT Image, Wan), so a fully offline, all-models-local setup is not what the project promises. Its documented model list is image-centric; the README does not present video, audio, 3D or text generation as first-class native capabilities the way ComfyUI's does. The README also leans on a separate launcher and an FAQ for installation, which means troubleshooting starts outside the main repository. If you need an HTTP API contract for a production pipeline, the InvokeAI README does not document one.
Licence and maintenance implications
ComfyUI is GPL-3.0. InvokeAI is Apache-2.0. That difference matters if you plan to embed either project inside a proprietary product or ship a modified build. GPL-3.0 imposes copyleft obligations on distributed derivatives, so a team that wants to keep its own changes private should read the licence carefully before building on ComfyUI. Apache-2.0 is permissive and includes an explicit patent grant, which is usually easier to clear inside a commercial organisation. InvokeAI's README calls the project free to use under a commercially friendly licence and notes that it serves as the foundation for multiple commercial products. On maintenance, both repositories show a last push of 2026-09-15, and neither is archived, so both were receiving commits in the days before this page. ComfyUI's recent releases include v0.34.0 on 2026-08-26, v0.33.1 on 2026-08-13 and v0.32.0 on 2026-08-11. InvokeAI's recent releases include v6.14.0 on 2026-08-25, v6.14.0-rc2 on 2026-08-16 and v6.13.8 on 2026-08-13. Release cadence on both sides is frequent enough that pinning a version and reading the changelog before upgrading is the practical habit, not an optional one. InvokeAI's README asks users to sponsor development, which is worth noting if you depend on the project long term.
Choosing for a concrete scenario
A studio building an internal image pipeline with a fixed API contract: ComfyUI, because the README documents API endpoints, App Mode and asynchronous queueing, and because the graph can be versioned as JSON. A solo artist who wants to paint, inpaint and iterate on a canvas without wiring nodes first: InvokeAI, because the Unified Canvas and gallery are the default surface and nodes are available when needed. A team that needs video, audio, 3D and text generation in one tool: ComfyUI, since its README lists those modalities as native, while InvokeAI's README lists them only as API-only entries or not at all. A company that must keep its modifications proprietary: InvokeAI, because Apache-2.0 is easier to clear than GPL-3.0. A workshop teaching how diffusion pipelines are assembled: ComfyUI, because the graph is the lesson. A production team that wants a curated, image-focused workspace with commercial-friendly licensing and a launcher: InvokeAI. And a team that wants both: run ComfyUI as the engine and API, and InvokeAI as the artist-facing workspace, accepting that the two keep separate model directories and separate workflow formats.
Bottom line
Pick ComfyUI when the pipeline is the product: broad native model coverage, an API surface, App Mode, and a graph you can save and version, at the cost of assembling it yourself and living with GPL-3.0. Pick InvokeAI when the workspace is the product: a locally hosted UI, a Unified Canvas for inpainting and outpainting, gallery and model management, and Apache-2.0 terms, at the cost of narrower native model coverage and API-only entries for some models. Before committing, verify first that your GPU has the memory headroom for the specific checkpoints you intend to load, that the install path you chose (desktop, portable, manual or launcher) works on your operating system, and that the workflow JSON or canvas project you build loads without a missing custom node or model file.