ComfyUI vs Automatic1111 vs InvokeAI vs Fooocus - 2026 Comparison
If you are trying to run Stable Diffusion on your own PC, the interface you pick matters more than the checkpoint file you download first. ComfyUI, AUTOMATIC1111 WebUI, InvokeAI, and Fooocus all wrap the same broad family of local models, but they trade off speed, VRAM use, flexibility, and how many clicks you make before an image appears. This comparison is written for 2026 installs on Windows with a NVIDIA GPU in mind, because that is still the least painful path for most people who want interactive latency instead of overnight CPU batches. We are blunt about where each tool shines, where it wastes an evening, and what you should try if you are brand new versus already deep in workflows. We also fold Stable Diffusion WebUI Forge into the Automatic1111 discussion, because Forge is the maintained fork many A1111 guides should be pointing at today - verify CUDA and PyTorch package names on the official Forge README before you download. Nothing here is a cloud lock-in story: your weights live on disk, your prompts stay local, and you are choosing how much control you want over the pipeline.
The Quick Answer
Key Takeaway - 2026
- Fooocus: Fastest path from zero to a good image when you do not want to learn a UI. Fewer expert knobs; tuned defaults.
- ComfyUI: Best when you want node graphs, reproducible JSON workflows, and fine-grained VRAM tradeoffs on the same GPU.
- InvokeAI: Strong middle ground with a more “app-like” feel, canvas tooling, and less bare-metal chaos than rolling everything by hand.
- AUTOMATIC1111 WebUI: Famous and extension-rich, but treat it as the legacy Gradio baseline. Most readers comparing A1111 in 2026 should look at Forge (
lllyasviel/stable-diffusion-webui-forgeon GitHub) for the same general layout with memory-focused improvements and Flux-oriented paths - read the release notes instead of trusting forum posts from 2023. - Or use LocalForge AI if you want Forge-class stacks without spending a night on dependency whack-a-mole - listed last, as one packaged route next to DIY installs.
“Uncensored” in a local setup means you pick the model and the prompt; there is no remote safety classifier in the loop by default. You still owe honest use: follow each model license, respect people’s consent, and do not treat local tools as a workaround for law or platform rules elsewhere.
Comparison table
| Tool | Best for | Learning curve | VRAM feel (same model) | Workflow style |
|---|---|---|---|---|
| ComfyUI | Power users, automation, custom graphs | High (nodes, ports, saved JSON) | Often lower peak VRAM when you optimize the graph | Visual programming: loaders, samplers, saves |
| AUTOMATIC1111 | Classic Gradio tabs, huge extension library | Medium | Heavier when extensions stack | Single-page form plus scripts |
| Forge | Same family as A1111, actively tuned for modern stacks | Medium (very familiar if you know A1111) | Usually better than stock A1111 on the same card | Tabbed Gradio UI, similar muscle memory |
| InvokeAI | Canvas workflows, guided installs | Medium | Depends on model; generally sane defaults | App-like layout beyond one-shot generate |
| Fooocus | Absolute beginners, quick SDXL-class runs | Low | Defaults aim to avoid “bad setting” leaks | Prompt in, image out |
Forge is not in the URL slug, but it belongs in any fair 2026 write-up wherever Automatic1111 is a contender.
ComfyUI: control first, comfort second
ComfyUI is a node editor. You connect blocks - model loader, conditioning, sampler, VAE decode - so the exact VRAM footprint is not a single number; it is a property of your graph. That is the upside: you can remove stages, swap samplers, quantize selectively, and hand someone a JSON file that replays the same result.
The downside is time on the clock. Your first night is wiring, not art direction. The learning curve is real: plan for a few hours of tutorials before you feel productive, not because the UI is “bad,” but because freedom costs attention.
Where ComfyUI wins: batch jobs, custom ControlNet-style rigs, and adopting new architectures when the community posts fresh node packs. Where it loses: you only want “nice portrait, Friday night,” with zero interest in graphs.
AUTOMATIC1111 and why Forge keeps coming up
AUTOMATIC1111’s WebUI is the stack a generation of guides assumed. Extensions, scripts, and old Reddit answers reference its folders and tabs. That familiarity is worth something if you are maintaining a legacy install.
It is still fair to call A1111 heavy once you treat extensions like collectibles. On an 8 GB card, naive settings plus a pile of add-ons is how people learn what “CUDA out of memory” looks like.
Forge targets the same audience - Gradio WebUI habits - but pushes performance and memory work that matters when models grow. Do not take our word for VRAM deltas: run your own A/B on your GPU with the same checkpoint and resolution, because driver and PyTorch builds move numbers.
InvokeAI: polish without full node mode
InvokeAI aims at a cohesive experience: installers that do not assume you live in a terminal, canvas workflows, and internal structure that feels closer to creative software. You still bring checkpoints from the wider ecosystem; you still run locally.
Where InvokeAI wins: you want inpainting and canvas iteration without hand-building Comfy graphs, and you dislike the “single giant browser form” feel. Where it loses: you already standardized on Comfy JSON for everything, or you want the smallest possible open-source surface.
Fooocus: fewer dials, faster first wins
Fooocus optimizes for defaults that do not embarrass you on the first ten images. You type, you generate, you tweak style presets. It is the tool you send to a friend who would uninstall Comfy after twenty minutes.
VRAM notes: treat 4 GB as emergency-only for larger modern checkpoints, 8 GB as a realistic comfort zone for many SDXL-class flows, and 12 GB+ as breathing room when you stop downscaling everything. Numbers are rules of thumb - always confirm against the exact model card you downloaded.
Performance and expectations (honest)
Latency swings by 30 to 300 percent when you change sampler, steps, resolution, and attention backends on the same card. That is why this page avoids fake precision like “always 22 percent faster.” Instead: benchmark one prompt, lock settings, swap only the UI stack, and compare three runs.
Models, licenses, and third-party hubs
Civitai and Hugging Face are third-party catalogs, not guarantees. Read license text on each model page. Some weights allow commercial use; some are research-only; some attach extra rider files. Local generation shifts privacy and control to you - it does not rewrite copyright.
Updates, backups, and folder hygiene
These projects move quickly. Expect 5 to 15 minutes of pull-or-reinstall work on a typical update week, more if Python or CUDA stacks shift. Keep your models directory on a drive with spare tens of gigabytes because checkpoints accumulate fast. If you dual-install Forge and Comfy, symlink a shared checkpoints folder only after you read both apps’ path docs - bad symlinks cause the classic “model missing” mystery that wastes an hour. Export a small archived workflow JSON from Comfy when you get a look you like; for Gradio stacks, screenshot your working settings once you stabilize a recipe.
Who should use what
- Pick Fooocus if you want the shortest path and can accept fewer expert controls.
- Pick InvokeAI if you want app-like polish and canvas workflows without writing node graphs.
- Pick ComfyUI if you will invest learning time to own every step of the pipeline.
- Pick Automatic1111 only if you have a concrete legacy reason; otherwise compare Forge first for the same habit loop with newer maintenance.
- Consider LocalForge AI if you want a packaged Forge-style route and would rather not debug CUDA mismatches manually - one option among several, not the only path.