May 18, 2026

ComfyUI vs Automatic1111 vs Forge 2026: Which UI Wins

By AIFoss · 11 min read

Three frontends still dominate self-hosted image generation in 2026. ComfyUI is the node-based powerhouse that every serious workflow eventually ends up on. Automatic1111 is the one everyone started with — still running on millions of machines. Forge is the fork that took A1111’s interface and fixed the performance.

All three are FOSS, all three run Stable Diffusion and its successors, and none of them is the right answer for everyone. The gap between them has widened significantly over the past year, and the choice has gotten more consequential.

Versions covered: ComfyUI v0.21.1 (released May 13, 2026), Automatic1111 v1.10.1 (released February 9, 2025), Forge (latest tag, February 2026).

The quick answer

Situation	Best pick
Complex pipelines, Flux, video generation, maximum control	ComfyUI
Switching from A1111 without a learning curve	Forge
New to image generation, want a form-based UI	Forge
Existing A1111 extension stack you can’t replace yet	A1111 (short-term)
API-driven programmatic image generation	ComfyUI
8 GB VRAM GPU, want to run SDXL comfortably	Forge or ComfyUI

The honest summary: Forge beats A1111 in every technical dimension and costs nothing to switch to if you’re already there. ComfyUI beats Forge on flexibility and model support but requires real learning investment. If you’re starting fresh in 2026, skip A1111 entirely.

What each tool actually is

ComfyUI is a node-graph image generation UI. You build workflows by connecting nodes — a model loader feeds a sampler, the sampler feeds a VAE decoder, the decoder feeds a save node. There is no prompt box sitting at the top of a settings panel; the positive prompt is a Text Encode node, and its output is a tensor connection. This is not a metaphor for how pipelines work: it is literally how you interact with the tool.

That model has two practical consequences. First, you can implement any diffusion technique without waiting for a developer to add a button — chain multiple models, add ControlNet mid-pipeline, branch into video, run sequential img2img passes automatically. Second, the initial learning curve is real. Version v0.21.1 added Flux 2 partner nodes, a native OpenAI Image node, and improved memory management for large video pipelines. License: GPL-3.0. Stars: ~113,000. Maintained by Comfy-Org, the company that now houses the project with funding from the community.

Automatic1111 (stable-diffusion-webui) is the form-based web UI that popularized local image generation in 2022–2023. You enter a prompt, set steps and CFG scale, choose a sampler, click Generate. The interface is familiar to anyone who started with SD 1.5 or early SDXL. License: AGPL-3.0. The latest release is v1.10.1, which shipped February 9, 2025, with a single bug fix: a CPU upscale issue. There have been no releases in 2026.

Forge is a fork of Automatic1111 by lllyasviel — the same person who created ControlNet. It keeps the form-based A1111 interface but rebuilds the inference pipeline to eliminate the memory overhead and tensor-casting waste that made A1111 slow. License: AGPL-3.0. The upstream repo’s latest tag is from February 2026; two actively maintained community forks, Forge Neo (Haoming02) and reForge (Panchovix), track Forge with more frequent updates and RTX 50-series support. Stars on the main repo: ~12,600, though this understates actual adoption considerably — Forge is the recommended A1111 upgrade path across most major tutorials.

Performance

The performance gap between A1111 and the other two is structural, not incidental. A1111’s architecture predates SDXL and Flux, and the overhead is measurable.

Frontend	Speed vs A1111	VRAM vs A1111 (same config)	Flux native support
Automatic1111 v1.10.1	Baseline	Baseline	Partial (extensions only)
Forge (latest)	2–3× faster	30–50% less	Yes
ComfyUI v0.21.1	Comparable to Forge	30–40% less	Yes

The underlying cause (documented in Forge’s GitHub Discussion #716) is tensor dtype casting. A1111 makes roughly 9,200 .to() calls per generation step; Forge makes approximately 1,985. The compute work is identical — the dispatch overhead is not. Forge eliminates the unnecessary casts. ComfyUI’s graph execution model avoids them by design.

For Flux Dev specifically at 20 steps, 1024×1024, benchmarks on an RTX 4090 report Forge at roughly 4 seconds per iteration and ComfyUI at roughly 6–7 seconds. Forge has a marginal FP16 speed edge on Flux; ComfyUI’s memory management often allows larger batches or higher-resolution runs on the same hardware, which can offset the per-step difference in practice.

For older SD 1.5 workflows, the speed difference between all three tools matters less — it’s when you hit SDXL and Flux that Forge and ComfyUI’s architectural advantages become tangible.

VRAM requirements

Model	A1111	Forge	ComfyUI
SD 1.5 (FP16)	4 GB	3 GB	2–3 GB
SDXL (FP16)	10–12 GB	6–8 GB	6–8 GB
Flux Dev (FP16, full precision)	Not practical	12–16 GB	12–16 GB
Flux Dev (GGUF Q4)	Not supported	6–8 GB	5–6 GB

ComfyUI documents a --lowvram flag and supports CPU offloading, allowing it to run on GPUs with as little as 1 GB VRAM — very slowly, but functional. This matters for CPU-only machines or for experimenting on a laptop where you wouldn’t otherwise run image generation at all.

For Flux at production quality without aggressive quantization, 16 GB VRAM is the correct target. 12 GB handles GGUF Q5/Q6 well in both Forge and ComfyUI. An RTX 4070 Ti (12 GB) runs Flux Dev at GGUF Q5 comfortably; an RTX 4060 (8 GB) is largely limited to SDXL and quantized Flux. For current GPU selection across the RTX 40 and RTX 50 series, runaihome.com’s local AI hardware guides cover the tier tradeoffs in detail. If you need to test Flux without buying hardware, RunPod rents A40 instances by the hour.

Installation

ComfyUI

git clone https://github.com/comfyanonymous/ComfyUI
cd ComfyUI
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
python main.py

ComfyUI also ships a Desktop installer (Windows and macOS) that bundles Python and handles dependencies. For most users, the Desktop build is the right starting point — it avoids the pip environment issues that make the manual install awkward on Windows. First launch opens the browser UI automatically.

Forge

Forge uses the same one-click installer as A1111 (webui-user.bat on Windows, webui.sh on Linux/macOS). If you’re already on A1111, the migration is: clone the Forge repo, point it at your existing models directory, run. Most A1111 extensions work without modification.

If you want the most actively maintained version, check Forge Neo at github.com/Haoming02/stable-diffusion-webui-forge-neo, which adds CUDA 13 and RTX 50-series support on top of the upstream Forge base.

A1111

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
cd stable-diffusion-webui
./webui.sh      # Linux/macOS
# or webui-user.bat on Windows

A1111 has the most documentation of the three — years of community guides, Reddit threads, and YouTube tutorials. The caveat: a significant portion of that documentation covers problems that no longer exist in Forge or ComfyUI. Debugging A1111-specific behavior in 2026 increasingly leads nowhere.

Model and extension ecosystem

A1111’s extension registry is the largest of the three, built up over years of community development. ControlNet, ADetailer, Regional Prompter, and hundreds of others exist here. The important caveat: Forge is A1111-compatible, so nearly all A1111 extensions run in Forge without modification. The “better extension ecosystem” argument for staying on A1111 rather than Forge is mostly gone.

ComfyUI runs a separate ecosystem of custom nodes, managed through ComfyUI Manager (a near-mandatory first install). The node ecosystem has grown rapidly — and for newer model architectures, ComfyUI typically gets support first, sometimes weeks before A1111 or Forge. The reason is structural: a node developer can ship a working implementation by dropping files into the custom_nodes directory without coordinating with the UI maintainer. New video models, new Flux variants, and experimental architectures appear in ComfyUI faster than anywhere else.

The practical tradeoff: A1111’s extensions add buttons and tabs — they’re shallow but accessible. ComfyUI’s custom nodes can fundamentally reroute the inference pipeline — more powerful, but they require understanding the graph.

Maintenance status

ComfyUI ships frequently. v0.21.1 was May 13, 2026; there were multiple releases in the preceding weeks. Comfy-Org has employed developers and is working on ComfyUI 2.0 as a longer-term backend rewrite. The project’s trajectory is clearly upward.

Forge (upstream lllyasviel) has slowed, with the last tagged release in February 2026. The practical answer for new installs is to use Forge Neo or reForge rather than the main repo — both have more frequent commits, better GPU compatibility, and active maintainers. For anything long-term, treating Forge as a stable but slowly-evolving UI is the right mental model.

A1111 last released in February 2025. GitHub discussions through late 2025 ask whether the project is actively maintained, with no clear response from the author. The codebase functions and the community keeps it working, but there is no active development roadmap.

This matters when new models ship. When a new architecture lands — a new Flux variant, a new video model — ComfyUI has a working community workflow within days. Forge gets it in weeks, usually via a community extension. A1111 may get it in months, or may not — Flux support in A1111 still requires a third-party extension rather than being built in.

When NOT to use each

ComfyUI is the wrong tool if you want to generate images inside 30 minutes of setup time. The node interface is genuinely confusing at first — connecting 8 nodes to accomplish what A1111 does in one form is not an advantage for casual use. If your workflow is “write a prompt, iterate on seeds, occasionally change samplers,” ComfyUI adds complexity without adding value. Fooocus or Forge are better starting points; you can migrate to ComfyUI when your workflows outgrow them.

A1111 is hard to recommend for new setups in 2026 in any form. It’s slower than Forge on the same hardware, uses more VRAM, lacks native Flux support, and its upstream development is stalled. The only defensible reason to stay on A1111 is an existing setup with extensions that haven’t yet been confirmed compatible with Forge — and that’s a temporary situation. Run A1111 if you must; plan to move.

Forge has a specific limitation worth flagging for production deployments: the main upstream repo is in maintenance mode, not active development. If you’re building something that requires auditable security patches from an active upstream, ComfyUI is the safer long-term choice. Forge is excellent for personal workstations and creative workflows where you control the update cadence. It’s less appropriate for anything where you need to trust the software supply chain.

The verdict

Start with Forge if you’re coming from A1111 or want a form-based interface. You get the familiar layout, a significant performance improvement, and native Flux support without learning a new paradigm. The migration from A1111 costs an afternoon.

Move to ComfyUI when your workflows hit the ceiling of what a form-based interface can do — multi-model chains, video pipelines, custom sampling schedules, anything experimental. That ceiling arrives quickly for anyone serious about image generation. ComfyUI is also where new model support lands first, so if staying current matters, it’s the right long-term home.

Don’t start on A1111. Nothing in its feature set justifies the performance gap and stalled development at this point. Forge is the same interface done correctly.

For a deeper look at ComfyUI’s node system, custom node ecosystem, and workflow examples, the ComfyUI review covers them in detail. For A1111’s specific strengths — its img2img tools, regional prompting, and the extension stack built around it over three years — the Automatic1111 review gives a full picture of what you’d be leaving behind.

Sources

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