--- name: automatic1111 description: >- AUTOMATIC1111 Stable Diffusion WebUI is a self-hosted browser interface and REST API for generating images with Stable Diffusion models on your own GPU. Use this skill to install and launch it, call /sdapi/v1/txt2img and img2img from scripts, inpaint, apply LoRAs, add extensions such as ControlNet, or batch-generate images. license: Apache-2.0 compatibility: 'Python 3.10.6 (3.11 works on Linux), git, an NVIDIA GPU (AMD via ROCm, Apple Silicon via the wiki), Linux/Windows/macOS' metadata: author: terminal-skills version: 1.1.0 category: data-ai repository: https://github.com/AUTOMATIC1111/stable-diffusion-webui tags: "stable-diffusion, image-generation, web-ui, img2img, lora" --- # Automatic1111 (Stable Diffusion WebUI) ## Overview AUTOMATIC1111's stable-diffusion-webui is a Gradio web app that runs Stable Diffusion locally (txt2img, img2img, inpainting, upscaling, LoRA, extensions) and exposes the same features over a REST API under `/sdapi/v1/` when started with `--api`. The latest tagged release is v1.10.1 (July 2024; adds SD 3 support and new schedulers), so treat the project as stable and slow-moving. It runs SD 1.5, SD 2.x, SDXL and SD3 checkpoints; it does not run newer architectures such as Flux. The interactive API docs are at `http://127.0.0.1:7860/docs` once it is running. ## Instructions Install and launch, then drive it from the UI or the API examples below. Keep to these points: 1. Python 3.10.6 is the documented version (newer Python often lacks matching torch wheels); `webui.sh` creates its own venv and installs dependencies on first run. Do not run it as root. 2. Put checkpoints in `models/Stable-diffusion/`, LoRAs in `models/Lora/`, VAEs in `models/VAE/`. Pick the model and resolution together: SD 1.5 is trained at 512x512, SDXL at 1024x1024. 3. Since 1.9 the scheduler is a separate field. Send `"sampler_name": "DPM++ 2M"` with `"scheduler": "Karras"` rather than the old combined name. 4. Set persistent launch flags in `webui-user.sh` (`export COMMANDLINE_ARGS="--api --xformers"`) instead of retyping them. 5. Low VRAM: `--medvram` (or `--medvram-sdxl`), `--lowvram` as a last resort. ## Installation ```bash # install.sh — Clone and launch Stable Diffusion WebUI git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git cd stable-diffusion-webui # Download a model (SDXL base, about 6.9 GB; use only .safetensors files) wget -P models/Stable-diffusion/ \ "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" # Verify against the SHA-256 Hugging Face publishes for this file (shown on its file page) echo "31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b models/Stable-diffusion/sd_xl_base_1.0.safetensors" | sha256sum -c - # Launch (auto-installs dependencies on first run). Binds to 127.0.0.1 only. ./webui.sh --api --xformers # Visit http://127.0.0.1:7860 ``` ## API: Text to Image ```python # txt2img_api.py — Generate images via the built-in REST API import requests import base64 from pathlib import Path API_URL = "http://localhost:7860" payload = { "prompt": "A serene Japanese garden with cherry blossoms, watercolor painting style, detailed", "negative_prompt": "blurry, low quality, distorted, text, watermark", "steps": 30, "cfg_scale": 7.5, "width": 1024, "height": 1024, "sampler_name": "DPM++ 2M", "scheduler": "Karras", "seed": -1, "batch_size": 1, } response = requests.post(f"{API_URL}/sdapi/v1/txt2img", json=payload) data = response.json() for i, img_b64 in enumerate(data["images"]): img_bytes = base64.b64decode(img_b64) Path(f"output_{i}.png").write_bytes(img_bytes) print(f"Saved output_{i}.png") ``` ## API: Image to Image ```python # img2img_api.py — Transform an existing image with a new prompt import requests import base64 from pathlib import Path API_URL = "http://localhost:7860" # Read input image as base64 input_image = base64.b64encode(Path("input.png").read_bytes()).decode() payload = { "init_images": [input_image], "prompt": "Transform into an oil painting, impressionist style", "negative_prompt": "blurry, distorted", "steps": 30, "cfg_scale": 7, "denoising_strength": 0.6, # 0.0 = no change, 1.0 = full regeneration "width": 1024, "height": 1024, "sampler_name": "DPM++ 2M", "scheduler": "Karras", } response = requests.post(f"{API_URL}/sdapi/v1/img2img", json=payload) data = response.json() img_bytes = base64.b64decode(data["images"][0]) Path("output_img2img.png").write_bytes(img_bytes) ``` ## API: Inpainting ```python # inpainting_api.py — Edit specific regions of an image using a mask import requests import base64 from pathlib import Path API_URL = "http://localhost:7860" input_image = base64.b64encode(Path("photo.png").read_bytes()).decode() mask_image = base64.b64encode(Path("mask.png").read_bytes()).decode() # White = edit area payload = { "init_images": [input_image], "mask": mask_image, "prompt": "A golden retriever puppy sitting on the grass", "negative_prompt": "blurry, distorted", "steps": 30, "cfg_scale": 7, "denoising_strength": 0.75, "inpainting_fill": 1, # 0=fill, 1=original, 2=latent noise, 3=latent nothing "mask_blur": 4, "width": 1024, "height": 1024, } # img2img honours width/height; match them to the input image to avoid a crop or stretch response = requests.post(f"{API_URL}/sdapi/v1/img2img", json=payload) img_bytes = base64.b64decode(response.json()["images"][0]) Path("inpainted.png").write_bytes(img_bytes) ``` ## Using LoRA Models ```bash # Place LoRA files in the models directory # models/Lora/my_style.safetensors ``` ```python # lora_usage.py — Apply LoRA weights in prompts via the API import requests import base64 from pathlib import Path API_URL = "http://localhost:7860" payload = { "prompt": " A portrait in my custom style, detailed, high quality", "negative_prompt": "blurry, low quality", "steps": 30, "cfg_scale": 7, "width": 1024, "height": 1024, } response = requests.post(f"{API_URL}/sdapi/v1/txt2img", json=payload) img_bytes = base64.b64decode(response.json()["images"][0]) Path("lora_output.png").write_bytes(img_bytes) ``` ## Extensions ```bash # Install popular extensions via git clone into the extensions directory cd stable-diffusion-webui/extensions # ControlNet — Guided generation with edge/depth/pose git clone https://github.com/Mikubill/sd-webui-controlnet.git # Adetailer — Automatic face/hand detail improvement git clone https://github.com/Bing-su/adetailer.git # Regional Prompter — Different prompts for different image regions git clone https://github.com/hako-mikan/sd-webui-regional-prompter.git # Restart WebUI to load extensions ``` Or use Extensions > Install from URL in the UI. Each extension runs arbitrary Python inside the WebUI process, so install only ones you trust. ControlNet needs separate model files in `extensions/sd-webui-controlnet/models/`. ## Batch Processing ```python # batch_generate.py — Generate multiple images with different prompts import requests import base64 from pathlib import Path API_URL = "http://localhost:7860" prompts = [ "A cyberpunk city at night, neon lights, rain", "A cozy cabin in the mountains, snow, warm light", "An underwater coral reef, tropical fish, sunlight", ] for i, prompt in enumerate(prompts): response = requests.post(f"{API_URL}/sdapi/v1/txt2img", json={ "prompt": prompt, "negative_prompt": "blurry, low quality", "steps": 25, "cfg_scale": 7, "width": 1024, "height": 1024, }) img_bytes = base64.b64decode(response.json()["images"][0]) Path(f"batch_{i}.png").write_bytes(img_bytes) print(f"Generated batch_{i}.png") ``` ## Examples The scripts above are complete examples. Typical requests: "generate a 1024x1024 watercolor garden with SDXL" is the txt2img script (it writes `output_0.png`); "restyle photo.png as an oil painting at 0.6 strength" is the img2img script; "replace the dog in photo.png using mask.png" is the inpainting script. Before scripting, confirm the server answers with `curl -s http://127.0.0.1:7860/sdapi/v1/sd-models`, which lists the installed checkpoints. Switch checkpoints per request with `"override_settings": {"sd_model_checkpoint": "sd_xl_base_1.0.safetensors"}`. ## Key Concepts - **txt2img**: Generate images from text prompts — the core feature - **img2img**: Transform existing images using prompts and denoising strength - **Inpainting**: Edit specific masked regions while preserving the rest - **LoRA**: Apply fine-tuned style adapters via `` in prompts - **Extensions**: Plugin system for ControlNet, Adetailer, regional prompting, and more - **API**: Full REST API at `/sdapi/v1/` — automate everything the UI can do - **Samplers**: DPM++ 2M (with the Karras scheduler), Euler a, DDIM — different speed/quality tradeoffs ## Guidelines - `--listen` binds to 0.0.0.0 and exposes the UI and API to your network. Leave it off on shared or public networks; if you need remote access add `--api-auth user:password` and `--gradio-auth user:password`, and put it behind a VPN or an SSH tunnel. Avoid `--share`, which publishes a public gradio.live URL, and `--enable-insecure-extension-access`. - `--allow-code` lets the UI run arbitrary Python; never enable it on a reachable server. - The API call blocks until generation finishes and returns base64 PNGs; set a long `timeout` in `requests.post` for big batches or SDXL on a slow GPU. - A seed of `-1` is random; save the seed from the `info` field in the response to reproduce an image. - `"width"`/`"height"` far from the model's trained size produce duplicated subjects; use hires fix or img2img upscaling instead. - For newer models (Flux and similar) use a maintained UI such as ComfyUI or Forge; this project's last release is from 2024. --- Source attribution: TerminalSkills Original project: https://github.com/TerminalSkills/skills/blob/3bfbf7f76459d89422eddc0e5f572ad0e6c393c9/skills/automatic1111/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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