ComfyUI vs stable-diffusion-webui: a node engine against a form-based front end
ComfyUI builds each generation as a reusable graph you can call from an API; stable-diffusion-webui puts the same models behind fixed tabs and form fields. They are not competitors so much as two layers of the same stack, and the choice turns on whether you need to reproduce and automate a pipeline or just operate one interactively.
At a glance
| Project | Comfy-Org/ComfyUI | AUTOMATIC1111/stable-diffusion-webui |
|---|---|---|
| Licence | GPL-3.0Copyleft: distributing it means sharing source | AGPL-3.0Network copyleft: hosting a modified version means sharing source |
| Maintenance | Commits in the last six monthsLast push September 25, 2026 | No commits for six monthsLast push March 2, 2026 |
| Language | Python | Python |
| GitHub stars | 134,951 | 165,150 |
| Read more | Our analysisGitHub | Our analysisGitHub |
Which one to choose
Choose ComfyUI if you need to reproduce a generation exactly from a saved graph, run it headless through the local API, or chain models and post-processing steps that a fixed form cannot express. It is also the better fit when you want video, audio, 3D or multimodal text nodes in the same workspace as image work.
Choose stable-diffusion-webui if you want a browser form with txt2img, img2img, inpainting, upscaling and training in one place, and you value the long list of community extensions more than a programmable graph. It is the easier starting point for a single operator on one workstation.
Two architectures: a graph you execute versus a form you fill in
ComfyUI's README describes a visual node graph for building and reusing workflows without code, with reusable subgraphs, workflow templates and App Mode. The unit of work is the graph itself: nodes for loading checkpoints, separate diffusion models, VAEs, text encoders, LoRAs, ControlNets, adapters and upscalers, connected into a directed structure that the engine executes. Because the graph is saved, the pipeline is the artifact, not the image. The README also states that workflows can be saved and loaded as JSON, and that complete workflows and seeds can be recovered from supported generated media, which is how a result can be traced back to the graph that produced it.
stable-diffusion-webui takes the opposite route. Its README describes a web interface for Stable Diffusion implemented with Gradio, with original txt2img and img2img modes, plus outpainting, inpainting, upscaling and a training tab. The unit of work is a form submission: a prompt, a sampler, a seed, a set of sliders. Features such as the X/Y/Z plot, prompt matrix and styles exist to vary those fields systematically, but the pipeline between them is fixed by the application. You can change parameters; you cannot rewire the order in which models are applied unless an extension exposes it.
That difference explains most of the rest. Anything stateful and repeatable tends to land in ComfyUI's column, because a graph is an explicit program. Anything exploratory and single-shot tends to land in stable-diffusion-webui's column, because a form is faster to fill than a graph is to build. Neither is a defect. They are answers to different questions.
What each one can actually run
ComfyUI's README lists native support across image generation, image editing, video generation, audio and video, audio generation, 3D and vision, and text generation, naming models such as Stable Diffusion 1.5, SDXL, SD3.5, Flux.1, Flux.2, Qwen Image, Wan 2.1 and 2.2, LTX-Video, HunyuanVideo, Hunyuan3D 2.1, ACE-Step and Stable Audio. The README also notes partner nodes that reach closed models such as Nano Banana, Seedance and Hunyuan3D, and that those paid API nodes can be disabled with --disable-api-nodes to stay fully offline. The breadth here is the point: the graph is a general execution engine, and the model list is what it currently knows how to load.
stable-diffusion-webui's README is narrower and older in scope. It lists Stable Diffusion 2.0 support and Alt-Diffusion support, both with wiki instructions, and its feature list centers on the original txt2img and img2img modes. It documents training for hypernetworks and embeddings, LoRAs, textual inversion, checkpoint merging, and a set of upscalers including GFPGAN, CodeFormer, RealESRGAN, ESRGAN, SwinIR, Swin2SR and LDSR. There is no video generation, no 3D, no audio pipeline in the README's feature list.
For a reader whose work is still images on Stable Diffusion 1.5 or SDXL checkpoints, this gap does not matter. For a reader who wants one tool that also handles video or 3D, the README of stable-diffusion-webui does not document those capabilities at all, and that silence should be read as a scope limit rather than a missing paragraph.
Getting each one running
ComfyUI's README offers three local paths: a desktop application for Windows and macOS described as the easiest way to get started, a Windows portable package that tracks the latest commits and is completely portable, and a manual install that supports all operating systems and GPU types, naming NVIDIA, AMD, Intel, Apple Silicon and Ascend. A paid Comfy Cloud option is listed for users without local hardware. The manual install is the only route that covers every GPU family, and it is also the route that asks the most of you.
stable-diffusion-webui's README advertises a one click install and run script but states plainly that you still must install Python and git yourself. Its own feature list does not name supported GPU vendors; the wiki is where dependency and platform details live, and the README points to external wikis for AMD and Intel paths. The README does mention 4GB video card support, with reports of 2GB working, which is a concrete low-end floor that ComfyUI's README does not state in comparable terms.
So the setup story is not simply "one is easy and one is hard." ComfyUI ships a packaged desktop build and a portable archive, which removes most of the manual steps for Windows and macOS users, but its full hardware matrix lives behind the manual install. stable-diffusion-webui automates the Python environment once Python and git exist, and its documented low-VRAM floor is lower. A user on a small NVIDIA card may find the older tool starts more readily; a user on Apple Silicon or Ascend has a documented ComfyUI path and no equivalent statement in the stable-diffusion-webui README.
Automation, APIs and running many jobs
ComfyUI's README states that it integrates into production pipelines with API endpoints, that the most sophisticated workflows can be exposed through a simple UI via App Mode, and that execution uses asynchronous queueing, partial graph re-execution, smart VRAM and RAM management, model offloading and quantized model support. Each of those is a mechanism, not a slogan. Partial graph re-execution means changing one branch of a graph does not force the whole graph to rerun. Asynchronous queueing means jobs can be submitted and drained rather than driven one at a time from a browser tab. App Mode means the graph can be hidden behind a reduced interface for someone who should not edit it.
stable-diffusion-webui's README does list an API and a batch processing feature that processes a group of files using img2img, plus a generate forever option and interrupt processing at any time. It does not document a queue with partial re-execution, nor a reduced interface mode, nor an offloading strategy described in those terms. For a single operator clicking through prompts, the API and batch mode are enough. For a service that receives requests from another system, the README's description of the web UI as the primary surface is the relevant constraint.
The practical split: ComfyUI is designed so the graph can be treated as a callable artifact. stable-diffusion-webui is designed so a person can sit in front of it. If your workload is a person, the second design is better. If your workload is a queue, the first one is.
Where each one falls short
ComfyUI's cost is the graph. Every workflow is a structure you build or import, and the README's own advice to browse the workflow library for maintained, ready-to-run templates acknowledges that starting from a blank canvas is not the intended entry point. The engine changes often: the releases listed for it run from v0.32.0 on 2026-08-11 through v0.33.1 on 2026-08-13 to v0.34.0 on 2026-08-26, which is a fast cadence, and our earlier analysis notes that patch versions only backport fixes to the current stable release. If you pin a version for production, you should expect to move forward deliberately rather than drift. The README also does not document a rollback procedure, so downgrade behaviour is not something you can plan from the repository text alone. On licensing, ComfyUI is GPL-3.0, and our earlier analysis states that embedding it in proprietary software without opening your code is not an option.
stable-diffusion-webui's cost is its pace and its licence. Its most recent release listed is v1.10.1 on 2025-02-09, and its last push is 2026-03-02, more than six months before today, so it should not be described as actively developed. The feature list is long and the extension ecosystem is real, but the README does not document a video or 3D path, and the one click script still requires Python and git. It is AGPL-3.0, which is a stronger copyleft obligation than GPL-3.0 in the network-service case: if you expose a modified version to users over a network, the AGPL's source-availability terms are the ones that apply. Our earlier analysis flags this as a reason to skip the project if you cannot accept AGPL obligations.
Neither shortcoming is fatal. They are simply the price of each design: ComfyUI charges you in graph complexity and release churn, stable-diffusion-webui charges you in stagnation and a stricter licence.
Licence and maintenance, read from the repositories
ComfyUI is GPL-3.0 and its last push is 2026-09-15, one day before today, with three releases inside the preceding month. That is a project that is being worked on now. The consequence for an adopter is that the stable branch is a moving target and the README's guidance to check the weekly release cycle is operational advice, not marketing. You should verify which release you will run and how you will test an upgrade before you depend on it.
stable-diffusion-webui is AGPL-3.0 with a last push of 2026-03-02, which is more than six months before today. Its latest listed release is v1.10.1 from 2025-02-09. The repository is not archived, but the dates are the dates: this is not a project whose recent history suggests frequent updates. For a hobbyist that is tolerable. For a team that needs a dependency patched on demand, it is a risk you should price in.
The licence difference deserves a plain statement. GPL-3.0 and AGPL-3.0 both require source disclosure when you distribute a modified version. AGPL-3.0 extends that to users interacting with the software over a network. If your plan is to run either tool internally and never distribute it, the practical difference is small. If your plan is to offer a hosted service built on a modified version, the AGPL obligation in stable-diffusion-webui is the one that will shape your architecture.
Choosing for concrete situations
A studio that needs the same shot reproduced next quarter with a different checkpoint should pick ComfyUI. The saved graph plus seed recovery from generated media is the mechanism that makes that possible, and no equivalent is documented in the stable-diffusion-webui README.
A solo artist exploring prompts on a 4GB card should start with stable-diffusion-webui. Its README documents that floor and the one click script, and the X/Y/Z plot and prompt matrix give systematic variation without building a graph.
A team exposing generation as an internal HTTP service should pick ComfyUI. The README documents API endpoints and asynchronous queueing; stable-diffusion-webui documents an API but frames the product as a web UI.
A team that wants to ship a modified version inside a closed product should pick neither as written. GPL-3.0 and AGPL-3.0 both create disclosure obligations, and the AGPL reaches network use.
A user who needs video, audio or 3D in the same tool as image generation should pick ComfyUI, because stable-diffusion-webui's README does not list those capabilities.
A user who depends on a specific community extension for stable-diffusion-webui should stay where that extension works, and treat the last push date as a reason to keep a local fork rather than wait for upstream. The two can also be combined: ComfyUI as the execution engine behind an API, stable-diffusion-webui as the interactive surface for exploration, with models shared between them on disk. That combination is common because the two solve adjacent problems rather than the same one.
Bottom line
Pick ComfyUI when the pipeline must be reproducible, callable and extensible beyond images, and accept the graph learning curve, the fast release cadence and the GPL-3.0 terms. Pick stable-diffusion-webui when a single operator needs a familiar form, a documented 4GB floor and a deep extension list, and accept that its last push was 2026-03-02 and its licence is AGPL-3.0. Before committing to either, verify your GPU appears in the supported list, confirm the models you need are loadable, and check whether your deployment distributes or hosts the software, because that last point decides which licence actually binds you.