Automatic1111 Review 2026: Should You Still Use It?
Automatic1111 (AUTOMATIC1111/stable-diffusion-webui) launched the accessible Stable Diffusion era. It introduced the settings-panel approach that every competitor since has either copied or reacted against. In 2026, it is still the most-referenced UI in tutorial content and the tool most people install first.
It is no longer the best choice for most use cases.
This review is honest about what A1111 still does well, where it has fallen behind, and what the migration path looks like for the large number of people who are still running it.
What Automatic1111 is
A1111 is a browser-based Stable Diffusion frontend built on Gradio. You run a Python server locally, open a browser, and get a settings panel with tabs for txt2img, img2img, inpainting, extras, and more. It introduced a plugin/extension system that generated a large community ecosystem, and for several years it was the most feature-rich image generation UI available.
License: AGPL-3.0. Repository: github.com/AUTOMATIC1111/stable-diffusion-webui.
Current state in 2026
A1111’s main development has slowed significantly. The project is maintained but not aggressively developed, and the most active contributors have largely moved to Forge (a direct fork) or ComfyUI. The extension ecosystem is large but aging — many extensions were written for earlier A1111 versions and have not been updated for current Python or model compatibility.
Two direct successors exist:
- Forge — a fork of A1111 that keeps the same UI and extensions but adds substantially better VRAM management and faster generation
- ComfyUI — a complete architectural departure (node graph instead of settings panel) with better performance, better model support, and higher complexity
Hardware requirements
| Model | A1111 VRAM usage | Forge VRAM usage | ComfyUI VRAM usage |
|---|---|---|---|
| SD 1.5 | 4–6 GB | 3.5–5 GB | 3–4 GB |
| SDXL | 7.5 GB | 5.5 GB | 4.5 GB |
| SDXL + ControlNet | 10–12 GB | 8 GB | 6–7 GB |
| Flux FP8 | Not supported | 12 GB | 12 GB |
| Flux FP16 | Not supported | 24 GB | 24 GB |
The SDXL number is the critical one: A1111 uses 7.5 GB where ComfyUI uses 4.5 GB and Forge uses roughly 5.5 GB on the same hardware. On an 8 GB card, A1111 can run SDXL but with very limited headroom for higher resolutions or ControlNet. Its successors handle this better with no migration pain. If you are still choosing hardware, an RTX 4060 Ti 16 GB on Amazon gives comfortable SDXL headroom on any of the three UIs.
Flux is the bigger issue. A1111 does not support Flux models at all. Flux.1 [dev] and [schnell] are the dominant high-quality generation models in 2026, and A1111 cannot run them. If Flux is part of your workflow, you cannot stay on A1111.
Installation
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
cd stable-diffusion-webui
# Windows:
webui-user.bat
# Linux/Mac:
bash webui.shA1111 handles its own Python environment setup via the launch script. Drop a .safetensors checkpoint into models/Stable-diffusion/ and refresh the model list. The first launch downloads required components and can take 10–20 minutes.
The extension system is accessible from the Extensions tab. Most common extensions (ControlNet, ADetailer, Ultimate SD Upscale) remain available and functional for SD 1.5 and SDXL workflows.
What A1111 still does well
Extension depth for SD 1.5 and SDXL workflows. Hundreds of extensions exist, many mature and stable. If your workflow is SD 1.5 or SDXL with ControlNet, ADetailer, and a specific set of extensions you rely on, A1111 still runs it without friction.
Tutorial coverage. The overwhelming majority of Stable Diffusion tutorials online were written for A1111. If you are learning from older guides, A1111’s panel layout matches exactly what the tutorial shows. This is genuinely useful for beginners working through existing content.
Familiarity. If you have been using A1111 for two years and have your workflow dialed in, the switching cost is real. For SD 1.5 production work with no need for Flux, staying put is a defensible choice — just know what you are giving up.
Where it falls behind
No Flux support. Flux.1 is the quality leader for text-to-image generation in 2026. You cannot run it on A1111. This is the single biggest practical limitation.
VRAM inefficiency. Using 7.5 GB for SDXL compared to Forge’s 5.5 GB and ComfyUI’s 4.5 GB means meaningfully worse headroom on 8–12 GB cards.
Performance. A1111 is 10–20% slower than ComfyUI on identical hardware for SDXL generation. The gap is larger with complex workflows. On a dedicated GPU session, this compounds quickly across many generations.
Slow update cadence. New model architectures, new ControlNet versions, and new samplers appear in ComfyUI and Forge faster. A1111 often waits weeks or months for the same support, if it arrives at all.
Comparison: A1111 vs Forge vs ComfyUI vs Fooocus
| Automatic1111 | Forge | ComfyUI | Fooocus | |
|---|---|---|---|---|
| Interface | Settings panel | Settings panel (same as A1111) | Node graph | Minimal |
| SDXL VRAM | 7.5 GB | 5.5 GB | 4.5 GB | ~6 GB |
| Flux support | ❌ | ✅ | ✅ | Limited |
| Generation speed | Baseline | +30–75% vs A1111 | +15–20% vs A1111 | Similar to A1111 |
| Extension ecosystem | Very large | A1111-compatible | Separate node ecosystem | Minimal |
| Learning curve | Medium | Medium (identical to A1111) | High | Very low |
| Active development | Slow | Active | Very active | Active |
| License | AGPL-3.0 | AGPL-3.0 | GPL-3.0 | GPL-3.0 |
The Forge row is what makes A1111 hard to justify in 2026. Forge uses the same Python environment, the same extensions, the same model files, and the same panel layout. Switching from A1111 to Forge is a git clone and moving your models folder — typically a 15-minute operation. You get 30–75% faster generation, 2 GB less VRAM usage on SDXL, and Flux support.
The only reason to stay on A1111 instead of Forge is if a specific extension you depend on broke on Forge and has not been fixed — which does happen, but is increasingly rare.
When NOT to use Automatic1111
You want to run Flux models. A1111 cannot. Use Forge or ComfyUI.
You are starting fresh. If you have never used any of these tools, start with Forge. Identical learning investment, better performance, and a longer useful lifespan.
You are on a GPU with less than 10 GB VRAM and need SDXL. The VRAM efficiency gap is meaningful at 8 GB. Forge or ComfyUI will give you noticeably more headroom.
You need complex, repeatable pipelines. ComfyUI’s node-based approach is purpose-built for this; A1111’s settings panel is not.
The migration question
If you are currently on A1111:
- Move to Forge if you want the same interface with better performance and Flux support. Time cost: 15–30 minutes.
- Move to ComfyUI if you want maximum control, pipeline composability, and first access to new models. Time cost: several hours of learning.
- Stay on A1111 only if you have a specific extension dependency that has not been ported and your workflow does not require Flux.
Verdict
Automatic1111 is not bad software. It was foundational and its extension ecosystem is a genuine asset. But its successors have passed it on every technical dimension that matters in 2026: VRAM efficiency, generation speed, and model coverage.
If you are evaluating tools from scratch, do not start here. If you are already here, the path forward is Forge (same interface, free upgrade) or ComfyUI (more learning, higher ceiling). Either is worth the switch.
For a deeper look at what ComfyUI can do that A1111 cannot, see the ComfyUI review.
Accurate against Automatic1111 commit history and Forge benchmarks as of May 2026. Performance numbers sourced from community benchmarks on RTX 3090/4090 hardware.