AUTOMATIC1111 Stable Diffusion WebUI

AUTOMATIC1111 Stable Diffusion WebUI is the original open-source graphical front-end for running Stable Diffusion locally on your own GPU. Released on August 22, 2022, by a developer known only as "AUTOMATIC1111," the project launched one month after Stable Diffusion itself and transformed what had been a command-line research tool into a browser-accessible application. The repository sits at approximately 163,000 GitHub stars as of April 2026, making it one of the most-starred AI projects in existence. It is licensed under AGPL-3.0 and requires no paid subscription, no API credits, and no cloud account. You supply the hardware and the model weights.

The core feature set covers text-to-image generation, image-to-image transformation with adjustable denoising, inpainting and outpainting, LoRA and textual inversion support, ControlNet integration via extension, a batch X/Y/Z parameter plotting tool, face restoration via GFPGAN and CodeFormer, multiple upscalers including ESRGAN, and a REST API for programmatic access. An extension ecosystem of several hundred community plugins extends this further, covering everything from video generation scripts to custom sampler implementations. The interface runs entirely on localhost via the Gradio framework, with no data leaving your machine.

What AUTOMATIC1111 generates in April 2026

A1111 runs Stable Diffusion v1.5, SD 2.x, and SDXL checkpoints. The typical workflow: load a checkpoint, write a positive and negative text prompt, choose a sampler (DPM++ 2M Karras, Euler a, DDIM, and others are built in), set step count and CFG scale, and click Generate. Resolution, seed, batch count, and hi-res fix upscaling are all configurable on the main tab.

The hi-res fix feature is particularly effective for SDv1.5 models: it renders a smaller latent first, runs an upscaler pass, then adds detail at the full resolution. Combined with SDXL refiner support (added in v1.6.0), users running 1024x1024 generations on A1111 can get competitive output quality with 2023-era models.

What A1111 does not generate: FLUX.1 images. Black Forest Labs released FLUX in August 2024 and it became the community's preferred architecture for photorealistic and high-fidelity generation by early 2025. A1111 received no FLUX support from the maintainer. GitHub issue #16311 and discussion #16482, both opened September 2024, documented community pressure for FLUX support. The response was silence. As of April 2026, FLUX on your local machine means using Forge or ComfyUI.

"If Flux is not supported, the Stable Diffusion webui project can be abandoned." -- skyliwq, GitHub discussion #16482, September 10, 2024

"if it does not continue to run the marathon of new technologies, it will end up falling into oblivion." -- psykokwak-com, GitHub discussion #16482, September 27, 2024

Where AUTOMATIC1111 sits versus ComfyUI and Forge

ComfyUI (comfyanonymous/ComfyUI) is architecturally the opposite of A1111. Where A1111 wraps the diffusion pipeline behind a form-based UI with sliders and dropdowns, ComfyUI exposes every pipeline operation as an explicit node on a visual canvas: CLIP text encoding, K-sampler, VAE decode, ControlNet conditioning, upscale nodes. Users wire these together. This means users can branch the pipeline, mix models at the same sampling step, or cache unchanged nodes across runs. ComfyUI generates SDXL 1024x1024 images in approximately 22 seconds versus A1111's 28 seconds on the same hardware (30 steps, benchmarks from Apatero, 2025). More critically, ComfyUI has had native FLUX support since late 2024. The tradeoff is a steep onboarding curve: new users report their first week with ComfyUI is frustrating, while A1111 reaches basic competency in two to three hours.

Stable Diffusion WebUI Forge (lllyasviel/stable-diffusion-webui-forge) is a fork of A1111 by Lvmin Zhang, the creator of ControlNet. Forge's interface is nearly identical to A1111's, so there is no learning curve for existing A1111 users. The difference is the backend: Forge replaces A1111's memory handling with a rewritten system called UNet Patcher that patches PyTorch memory allocation directly. Benchmarks show 30-45% faster generation on 8GB VRAM systems and 60-75% faster on 6GB systems. Forge added FLUX.1 support in August 2024, the same month FLUX launched. The "Never OOM" feature automatically adjusts tiling to prevent out-of-memory crashes that A1111 users regularly encounter on mid-range GPUs. Approximately 80% of A1111 extensions work on Forge without modification.

The community verdict as of 2026: Forge is a free, zero-friction upgrade from A1111 for users who want the same UI with better performance and FLUX support. ComfyUI is the choice for users who want to build complex, reusable pipelines and are willing to invest the learning time.

Real cost of running AUTOMATIC1111

A1111 itself is free, open source, and self-hosted. The costs are entirely hardware and electricity. Minimum viable setup: an Nvidia GPU with at least 4GB VRAM (generation is possible but slow); practical minimum is 8GB for SDXL workflows. The project runs on Windows, Linux, and macOS (Apple Silicon via MPS backend, though with less community support).

Model weights are the other cost vector. SDv1.5 base checkpoints are freely available on Hugging Face. Fine-tuned checkpoints, LoRA packs, and SDXL models are available on CivitAI, most without charge. Large model files (2-7GB per checkpoint) require local storage. Running multiple checkpoints means a library that can easily reach 50-100GB.

Third-party cloud services like RunDiffusion and ThinkDiffusion offer hosted A1111 environments starting at roughly $0.50-$1.00/hour for GPU access. These are not affiliated with AUTOMATIC1111 but allow users without local hardware to run the same interface. Google Colab notebooks for A1111 remain available but free-tier GPU allocation has become unreliable since 2023.

Where AUTOMATIC1111 reliably fails

FLUX support: absent. This is the clearest capability gap in 2025-2026. If your workflow involves FLUX.1-dev, FLUX.1-schnell, or any FLUX-based fine-tune, A1111 cannot run it. This is not a configuration issue; the architecture is simply not implemented.

Performance on constrained hardware. On 6-8GB VRAM GPUs, A1111 generates SDXL images noticeably slower than Forge and more frequently hits out-of-memory errors. The problem is not the model, it is A1111's memory management. Forge's UNet Patcher handles the same hardware more efficiently. Users who switch to Forge from A1111 typically see immediate generation speed improvements without changing anything else.

Extension breakage on updates. A1111's Python and Gradio dependencies have shifted across versions. The 1.9.x-to-1.10.x transition broke several widely-used extensions. The maintainer moves slowly: over 1,800 open issues and 100 open pull requests have accumulated with limited merge activity. Community contributions rarely land, and bug reports can go unacknowledged for months.

Complex pipeline workflows. A1111's tab-based UI makes it difficult or impossible to construct workflows that branch, mix models mid-generation, or apply conditional logic. What takes a few nodes in ComfyUI requires workarounds or custom scripts in A1111, if it is achievable at all.

Windows installation friction. Despite install scripts, users on Windows regularly encounter CUDA version mismatches, Python path conflicts, and virtual environment errors. The official wiki documents solutions, but resolving them requires comfort with the command line, which contradicts A1111's beginner-friendly reputation.

Who AUTOMATIC1111 is for, and who should pick something else

A1111 is the right tool if: You are new to local Stable Diffusion and want the widest selection of tutorials, guides, and community documentation. The majority of YouTube SD tutorials, CivitAI model cards, and written guides reference A1111's interface specifically. You run SDv1.5 or SDXL checkpoints and do not need FLUX. You want a simple tab-based UI without learning ComfyUI's node paradigm. You do textual inversion training or LoRA dataset preprocessing using the built-in Train tab. You have an existing A1111 installation with extensions and workflows you want to continue using without migration overhead.

Skip A1111 and use Forge if: You want A1111's familiar interface but with better performance and FLUX support. Forge is a direct drop-in with no learning curve and is actively maintained. The switch takes under an hour.

Skip A1111 and use ComfyUI if: You want to work with FLUX natively, build reusable generation pipelines, mix models at inference time, or squeeze maximum performance from your hardware. ComfyUI has the steeper learning curve, but it is now the serious practitioner's standard in the r/StableDiffusion community.

A1111 in 2026 occupies a specific niche: the best-documented, most beginner-accessible entry point to local image generation, with the caveat that users will likely outgrow it within a few months and find Forge or ComfyUI better suited to their evolving needs.