Jovan Chan

Posted on Jun 2 • Originally published at aifoss.dev

comfyui-vs-automatic1111-vs-forge-2026

#opensource #ai #selfhosted #linux

This article was originally published on aifoss.dev

---
title: 'ComfyUI vs Automatic1111 vs Forge 2026: Which UI Wins'
description: 'ComfyUI v0.21.1, Automatic1111 v1.10.1, and Forge compared in 2026. Which Stable Diffusion frontend wins on speed, VRAM, model support, and ease of use?'
pubDate: 'May 18 2026'

tags: ["comfyui", "ai", "stablediffusion", "gpu", "opensource"]

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 Comf

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