ComfyUI vs Automatic1111 vs Forge: Which to Run

Most comparisons of local Stable Diffusion interfaces are written as taste arguments: nodes versus sliders, power versus friendliness. That framing is not wrong, but it buries the decision that actually costs you time later. The three projects differ far more in maintenance cadence, memory architecture and extension risk than they do in what they can produce, and those are the differences that show up six months after you commit.

This is a comparison of the projects as their own repositories and documentation describe them, not a performance review.

The three-line summary

	ComfyUI	Automatic1111 WebUI	Forge
Interface model	Node graph	Form with tabs	Form with tabs
Latest tagged release	Weekly tags (v0.34.x as of Aug 2026)	v1.10.1, 9 Feb 2025	Based on WebUI 1.10.1
Stated release cadence	Weekly, stable every ~2 weeks	None stated	Syncs upstream every ~90 days
Memory management	Dynamic VRAM, offload, fp8	Baseline WebUI behaviour	Rewritten, GPU weight slider
Quantised Flux support	fp8 flags, GGUF via nodes	Not native	NF4 and GGUF native
Hardware breadth	NVIDIA, AMD, Intel, Apple, NPUs	NVIDIA-centric	CUDA one-click packages
Automation	HTTP API, subgraphs, App Mode	API endpoints	Inherited API

Everything below is the reasoning behind those rows.

Maintenance cadence is the decisive difference

ComfyUI documents its release process in its own README: a weekly release cycle targeting Monday, a new major stable version roughly every two weeks, and three interconnected repositories covering core, the desktop application and the frontend. It also carries a warning that matters operationally: commits outside the stable release tags may be very unstable and break many custom nodes. That is an unusually candid statement, and it tells you exactly how to run the thing in production, which is to track stable tags rather than master.

The AUTOMATIC1111 WebUI presents a different picture. Its most recent tagged release, v1.10.1, is dated 9 February 2025. The repository has seen commits since then, but the release line has not moved in well over a year. This is not a criticism of the project, which is enormously influential and still perfectly capable of generating images. It is a statement about what you should expect when a model released next quarter needs new loader code.

Forge is explicit about the same reality from the outside. Its README states that Forge is currently based on SD-WebUI 1.10.1 and that it will sync with the original WebUI every 90 days “because original SD-WebUI is almost static now”. Forge’s own component status table, the one the author maintains from manual checks, carries last-tested dates from July and August 2024. The repository itself was last pushed to on 31 July 2025.

If your interest in local generation is running the models that already exist, cadence does not matter much. If your interest includes whatever ships next, it is close to the only thing that matters, because a new architecture needs loader support, sampler support and node support before any interface can expose it.

Memory architecture, and why the same card gives different answers

All three projects had to solve the same problem, which is that diffusion model weights have grown far faster than consumer VRAM. They solved it differently.

Forge’s headline contribution was a rewritten GPU memory management system, surfaced to the user as a “GPU Weight” slider plus offload location and offload method controls. The project’s own guidance is blunt about how to use it: the linked performance troubleshooting discussion is titled with the instruction not to set GPU Weight too high, and states that lowering it solves the overwhelming majority of Flux performance problems. Forge also supports quantised Flux weights natively, listing BitsAndBytes NF4 and GGUF Q8_0, Q5_0, Q5_1, Q4_0 and Q4_1 with LoRA support across most of them.

ComfyUI approaches the same problem through the runtime rather than a slider. It offloads models to system RAM after use by default, applies RAM-pressure-based result caching, and exposes a large set of command-line controls: fp8 storage for diffusion model and text encoder weights independently, --reserve-vram to leave capacity for the operating system, --vram-headroom to keep an amount completely free even against other applications, --cpu-vae to move decoding off the GPU, and --novram for the case where nothing else is enough. The trade is control against discoverability, which is the trade the whole interface makes.

The practical consequence is that “which one uses less VRAM” has no fixed answer. A default ComfyUI graph and a well-tuned Forge configuration will land in different places than a tuned ComfyUI graph and a default Forge configuration. Sizing has to be done against a specific configuration, which is what ComfyUI GPU requirements works through, and what the ComfyUI VRAM and execution sizer estimates for a given graph shape.

Extension risk is the same problem in three shapes

Every one of these projects installs community code into a shared Python environment, and every one of them therefore inherits dependency conflicts as a class of failure. The official ComfyUI documentation is direct about the security half of this, warning that malicious plugins could exploit custom nodes and advising users to install only from trusted authors and to understand what a node does before installing it.

Where they differ is in blast radius and recovery. ComfyUI ships a --disable-all-custom-nodes flag with a companion --whitelist-custom-nodes option, which turns a broken environment into a bisectable one: start clean, add back the pack you suspect, observe. That is a real operational advantage over an environment where the only way to isolate a bad extension is to move directories around by hand.

Forge’s extension situation carries an additional wrinkle, which is that it moved to Gradio 4 and maintains a separate extension replacement list because not all WebUI extensions carried over. Compatibility with the enormous WebUI extension ecosystem is partial rather than complete, and the mapping is community-maintained.

Automation, and who each project is actually for

The gap widens once generation stops being interactive. ComfyUI’s stated feature set includes reusable subgraphs, workflow templates, an App Mode that exposes a complex graph behind a simple interface, and a local API for integrating workflows into applications. Workflows serialise to JSON, and complete workflows and seeds can be recovered from generated media. The project also states that the core runs fully offline and downloads nothing unless asked, with --disable-api-nodes available to force all built-in functionality to stay local.

That combination, a serialisable pipeline plus an HTTP interface plus partial re-execution, is why ComfyUI ends up in production image pipelines and the form-based interfaces mostly do not. The node graph is not just a different way to press the same button; it is the reason the workflow is a reusable artefact instead of a set of UI settings. The mechanics of that execution model are covered in how ComfyUI graphs execute and where VRAM goes.

Hardware breadth points the same direction. The ComfyUI documentation lists supported hardware including NVIDIA, AMD through ROCm on Linux, Intel Arc through native PyTorch XPU, Apple Silicon through Metal Performance Shaders, plus Ascend NPU, Cambricon MLU and Iluvatar Corex via vendor PyTorch extensions, and a CPU fallback. Forge’s supported path is a set of CUDA one-click packages for Windows.

Choosing

Choose the WebUI if you want the shortest path to a familiar interface, you are working with SD 1.5 and SDXL, and you do not need whatever ships next. It remains the most widely documented interface, and the volume of tutorials written against it has real value.

Choose Forge if you are on a constrained NVIDIA card on Windows, you want quantised Flux without assembling a graph, and the GPU weight slider is the level of control you actually want. Understand that you are choosing a downstream fork with a stated 90-day sync cadence against a mostly static upstream.

Choose ComfyUI if the output is a pipeline rather than an image, if you need current model support without waiting, or if your hardware is not a recent NVIDIA card. Accept in exchange that the first week is genuinely harder, and that the failure mode you will spend the most time on is memory pressure rather than user interface confusion. When that happens, ComfyUI out of memory errors is the place to start.

There is also no rule that says you pick one. All three read the same checkpoint files, and a shared model directory plus ComfyUI’s extra_model_paths.yaml lets a single collection of weights back more than one interface without duplicating a single gigabyte.