Stable Diffusion WebUI
Stable Diffusion WebUI is the form-based option among the image generation runtimes covered here, and a common first stop for anyone new to image generation on their own machine. You open a page in your browser, type a prompt, adjust a handful of settings, and press generate. Everything the pipeline does is preset and sensible by default, which is exactly what you want before you know what any of the settings mean.
How it works
The interface is organised as tabs. The main one is text-to-image: a positive prompt box, a negative prompt box, and controls for image size, sampler, step count, guidance strength and seed. Another tab does image-to-image, where you supply a starting picture and let the model rework it. Inpainting lets you mask a region and regenerate only that part. Other tabs handle extras like upscaling and model management.
Checkpoints, LoRA adapters, embeddings and upscalers all live in folders on disk, and the interface picks them up from there. Selecting a different checkpoint is a dropdown. Applying a LoRA is a short piece of syntax in the prompt itself. The diffusion process running underneath is the same one every other app uses, but you drive it through a form rather than assembling it.
Generation history and settings are kept alongside the images, so you can see what produced a given result and reproduce it by reusing the seed and parameters.
Hardware and backends
NVIDIA and CUDA is the default assumption and the best-supported path. Apple Silicon works through Metal, and AMD cards work through ROCm on Linux, with a DirectML route on Windows that tends to lag. As with the other apps, extensions are written and tested against NVIDIA first, so a non-NVIDIA setup occasionally means waiting or patching.
VRAM sets your ceiling on model size and output resolution. The interface has memory-saving options that trade speed for a smaller footprint, which is what makes it workable on modest cards. More memory mostly buys you larger images, bigger batches and newer model families without fiddling. A 24 GB card removes most of the pressure. On a Mac the memory is unified and shared with the rest of the system. See GPUs for local AI for how the common cards compare.
Fit
Choose Stable Diffusion WebUI when you want to make images rather than build a system. It is the shortest path from installed to useful, it matches the way most tutorials and prompt guides are written, and the settings map one to one onto the concepts you need to learn anyway.
Move to ComfyUI when you keep wanting a step the form does not offer, or when you need a repeatable multi-stage pipeline. Prefer InvokeAI if your work is sustained editing of a single composition on a canvas rather than generating many candidates.
Working with it
The usual pattern is exploratory. You write a prompt, generate a small batch at a modest resolution, and look for a composition worth keeping. When one appears, you lock its seed, refine the prompt, then rerun at a higher resolution or push it through an upscale pass. Anything that is nearly right but wrong in one area goes to inpainting.
Extensions add new tabs and controls, and they install from within the interface. They are also the usual cause of a setup that stops starting, so add them one at a time and note what you have. Keep your checkpoints in one place so other apps can share them, which organising model files covers.