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stable-diffusion-webui

stable-diffusion-webui

Use and maintain AUTOMATIC1111 Stable Diffusion WebUI workflows, including launch/configuration, REST API automation, extensions, model assets, LoRA, textual inversion, training, extras, and postprocessing.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill documentation

GitHub

name: stable-diffusion-webui description: "Use and maintain AUTOMATIC1111 Stable Diffusion WebUI workflows, including launch/configuration, REST API automation, extensions, model assets, LoRA, textual inversion, training, extras, and postprocessing." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Stable Diffusion WebUI

Use this repo skill when the task names Stable Diffusion WebUI, AUTOMATIC1111, launch.py, /sdapi/v1/*, Gradio WebUI launch flags, WebUI extensions/scripts, checkpoints/VAEs/Lora/embeddings/upscalers, textual inversion, hypernetworks, Extras, or WebUI postprocessing workflows.

This skill is self-contained guidance distilled from the Stable Diffusion WebUI repository. It does not require the original checkout unless the user is explicitly auditing or modifying a checkout; bundled helper scripts are safe stdlib tools for source inspection, payload construction, extension scaffolding, and layout validation.

Route By Task

User taskRead
Start WebUI/API mode, choose launch flags, configure auth/CORS/TLS/subpath, avoid downloads, diagnose startuplaunch-and-config
Build /sdapi/v1/* calls, create txt2img/img2img payloads, encode images, inspect script metadata, troubleshoot API 400/401/422api-automation
Write or debug WebUI scripts/extensions, callbacks, postprocessing scripts, preload hooks, extension metadata, Gradio component hooksextension-scripting
Place or refresh checkpoints, VAEs, embeddings, hypernetworks, Lora/extra networks, upscalers, hashes, model directoriesassets-and-models
Plan textual inversion or hypernetwork training, preprocessing-for-training, Extras upscaling, face restoration, postprocessingtraining-and-postprocessing

Common Entry Points

  • python launch.py --api --api-auth user:pass --ckpt <checkpoint.safetensors> starts UI plus authenticated API; use --nowebui for API-only mode.
  • GET /sdapi/v1/samplers, /schedulers, /sd-models, /upscalers, /scripts, and /script-info should precede generation payload construction.
  • Checkpoint and VAE placement should be solved before API generation or training; model-list endpoints only show assets discovered by the running server.
  • Custom scripts and extensions should keep WebUI imports inside generated extension files, not inside scaffolding tools.
  • Textual inversion, hypernetwork training, face restoration, and upscaling are long-running/model-backed workflows; validate inputs before running them.

Root References And Tools

  • repo-provenance.md: source snapshot, evidence paths, dirty-state baseline, and refresh cues.
  • repo-routing-metadata.json: structured scenario metadata for DisCo managed routing.
  • overview.md: capability map, repository shape, safety model, and cross-sub-skill workflow patterns.
  • troubleshooting.md: cross-cutting install/import, optional dependency, backend, server, security, and workflow triage.
  • inspect_webui_source.py: optional stdlib helper that summarizes CLI flags and API routes from a WebUI checkout without importing WebUI or loading models.

Example source-audit helper usage when the user is already working in a WebUI checkout:

python scripts/inspect_webui_source.py --repo . --json

Safety Defaults

  • Do not expose --listen, --share, --ngrok, broad CORS, server-stop endpoints, or insecure extension access without explicit auth and trust boundaries.
  • Do not enable --allow-code or --disable-safe-unpickle unless the user accepts arbitrary-code or pickle risk.
  • Do not run launcher modes that install packages, clone repositories, download models, start servers, load checkpoints, or initialize GPU state unless the user explicitly wants that action.
  • Prefer .safetensors assets, explicit model paths, small API smoke payloads, and read-only metadata endpoints while diagnosing.

Boundaries

  • Use this skill for practical WebUI operation, automation, extension work, and asset/training workflow guidance.
  • Use general Diffusers or ComfyUI skills instead when the task is about Python diffusion pipeline APIs or node-graph execution outside Stable Diffusion WebUI.
  • Use a repository-maintenance skill in addition to this one when the user asks to edit WebUI source code, run native tests, or prepare pull-request changes.
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