Imported from terminalskills/skills (skills/automatic1111/SKILL.md). Install upstream with npx skills add terminalskills/skills --skill automatic1111. Copyright stays with the author (Apache-2.0).

Automatic1111 (Stable Diffusion WebUI)

Installation

# 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 or SD 1.5)
wget -P models/Stable-diffusion/ \
    "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors"

# Launch (auto-installs dependencies on first run)
./webui.sh --listen --api --xformers
# Visit http://localhost:7860

API: Text to Image

# 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 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

# 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 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

# 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,
}

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

# Place LoRA files in the models directory
# models/Lora/my_style.safetensors

# 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": "<lora:my_style:0.8> 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

# 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

Batch Processing

# 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")

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 <lora:name:weight> 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 Karras, Euler a, DDIM — different speed/quality tradeoffs