AUTOMATIC1111 vs ComfyUI
AUTOMATIC1111 and ComfyUI both run Stable Diffusion locally—they’re not “cloud vs local,” they’re form UI vs node graph. This page breaks down the real workflow differences so you pick the one you’ll actually keep using.
Feature Comparison
| Feature | AUTOMATIC1111 | ComfyUI |
|---|---|---|
| Runs Locally | Yes | Yes |
| Open Source | Yes | Yes |
| NSFW Allowed | Yes | Yes |
| Type | Local / Offline | Local / Offline |
Quick Verdict — March 2026
Pick AUTOMATIC1111 if you want a tabbed web UI, the biggest extension catalog, and the shortest path from install → prompt → image. Pick ComfyUI if you want a node graph you can save as JSON, reuse forever, and extend with custom nodes when new models and techniques land.
One line: AUTOMATIC1111 = fast iteration inside one app screen. ComfyUI = reproducible pipelines you can share like code.
Side-by-side spec table
| AUTOMATIC1111 (SD Web UI) | ComfyUI | |
|---|---|---|
| UI type | Classic web UI: tabs, fields, sliders | Node graph in the browser: wires between ops |
| Setup (typical) | Clone repo → webui-user.bat / webui.sh (or community installers) |
Portable build, manual venv, or Desktop beta—pick what matches your OS |
| VRAM | Depends on model + resolution; --medvram / --lowvram are first-line mitigations on tight GPUs |
Depends on model + how many nodes stay resident; big graphs cost more VRAM |
| Model support | SD 1.5 / SDXL / community checkpoints + LoRAs via the usual folders | Same model families; often where new techniques show up as nodes first |
| Best for | Prompt → generate → inpaint loops; extension-heavy workflows | Saved workflows, batch logic, multi-stage and video-style pipelines |
Where AUTOMATIC1111 wins
- Extension ecosystem: Huge library of one-click extensions—ControlNet, extra samplers, tooling—wired into the same UI you already learned.
- Familiar controls: Prompt, negative prompt, steps, CFG, sampler—same vocabulary as most tutorials online.
- Fast “tweak and regenerate”: Change a slider, hit generate—no rearranging a graph unless you want to.
- Inpainting / img2img loop: Strong fit when you’re refining one canvas without rebuilding a node network each time.
Where ComfyUI wins
- Workflow is a file: Export/import JSON, stash versions in git, share exact pipelines—your graph is the recipe.
- Composable pipelines: Branching, reroutes, and reusable subgraphs beat “one giant screen of settings” when the process gets long.
- Custom nodes: New techniques often land as node packs; you add what you need instead of waiting for a single monolithic release.
- Automation-friendly: Queue behavior and headless patterns fit “run this graph on 500 inputs” better than clicking through tabs.
Setup compared
AUTOMATIC1111: Install Python/Git (versions per project docs), clone stable-diffusion-webui, run the launcher script, let dependencies pull on first boot, drop checkpoints into the models folder. It’s the path most walkthroughs assume—expect troubleshooting GPU drivers and VRAM flags at least once on a new machine.
ComfyUI: Install route varies (portable vs venv). You’ll pick a startup script, point model folders, then learn the canvas—the first hour is graph topology, not prompts. ComfyUI Manager (community) is widely used for custom nodes—treat updates like any dependency stack: update when you need a fix, not randomly mid-project.
Hardware & performance
- Both care more about which checkpoint and resolution you pick than about the brand name of the UI.
- AUTOMATIC1111: Official troubleshooting docs reference low-VRAM modes (
--medvram,--lowvram) when you’re on smaller GPUs; SDXL-class models at high res is where people hit OOM first—plan headroom. - ComfyUI: Heavy graphs (multiple models, ControlNets, upscalers) stack memory pressure—watch VRAM as you add nodes, not just at the checkpoint loader.
- Speed claims vary by GPU, driver, and sampler—if someone quotes a single “% faster,” it’s usually a cherry-picked run. Benchmark your card with your workflow if it matters.
Who should use what
| AUTOMATIC1111 if you… | ComfyUI if you… |
|---|---|
| Want the shortest path to “prompt in, image out” with minimal graph thinking | Want saved workflows you can version, diff, and hand to teammates |
| Rely on extensions and community scripts inside one UI | Want node packs and custom ops when new models drop |
| Prefer tutorials that use tab vocabulary (txt2img, img2img, extras) | Prefer wiring loaders, samplers, and VAE decode explicitly |
| Mostly edit one image at a time in a tight loop | Run multi-step or repeatable pipelines (batch, video-style graphs, complex post) |
How to run it without the rabbit hole: install Forge or ComfyUI yourself, or use LocalForge AI if you want a pre-wired local stack with less manual setup.
About AUTOMATIC1111
The original Stable Diffusion web UI with 145k+ GitHub stars. Full-featured image generation frontend with extensions, LoRA support, and img2img.
Full AUTOMATIC1111 profile →About ComfyUI
Node-based Stable Diffusion frontend for power users. Visual workflow editor with full pipeline control and native Flux support.
Full ComfyUI profile →