Stable Diffusion WebUI — User Guide

1. Quick Start

Stable Diffusion WebUI (commonly called "A1111") is a browser-based interface for running Stable Diffusion image generation models locally on your own hardware. Built on Gradio, it provides a full-featured GUI for text-to-image, image-to-image, inpainting, upscaling, and dozens of other image generation workflows — all without cloud dependencies or subscriptions.

Key Features

• Text-to-image generation with advanced prompt syntax (emphasis, scheduling, alternation)
• Image-to-image with inpainting, outpainting, and InstructPix2Pix support
• Multiple model architectures: SD 1.x, SD 2.x, SDXL, SD3, Alt-Diffusion
• Extra networks: LoRA, LyCORIS, textual inversion embeddings, hypernetworks
• Upscaling: ESRGAN, RealESRGAN, SwinIR, ScuNET, DAT, HAT, LDSR, and more
• Face restoration: CodeFormer and GFPGAN
• X/Y/Z plot for systematic parameter comparison
• Extensions system for community-built add-ons
• REST API for programmatic access and automation

Prerequisites

• GPU: NVIDIA GPU with 4 GB+ VRAM (6 GB+ recommended); AMD GPUs supported on Linux
• Python: 3.10.x (3.10.6 recommended; 3.12+ may cause issues)
• Git: installed and on PATH
• Disk: ~10 GB for installation + models (each checkpoint is 2–7 GB)

Quickest Install (Windows)

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
webui-user.bat

The launcher automatically creates a Python virtual environment, installs dependencies, and opens the UI at http://127.0.0.1:7860.

Tip

Place your model checkpoint files (.safetensors or .ckpt) in the models/Stable-diffusion/ folder before launching. The UI will detect them on startup and let you select from a dropdown.

First Image

1. Select a model from the Stable Diffusion checkpoint dropdown at the top
2. Type a prompt: a photo of a mountain landscape at sunset, dramatic lighting, 4k
3. Click Generate (or press Ctrl+Enter)
4. Your image appears in the output gallery within seconds

What to Try Next

• Section 4.1 — master prompt syntax and sampling parameters
• Section 4.2 — transform existing images
• Section 4.5 — add LoRAs for styles and characters
• Section 5 — step-by-step recipes for popular workflows

2. Installation

System Requirements

Important

Python 3.10.x is strongly recommended. Versions 3.12 and above may fail during dependency installation due to breaking changes in setuptools and distutils. If you see Cannot import setuptools.build_meta, downgrade to Python 3.10.6.

Platform-Specific Instructions

Windows

1. Install Python 3.10.6 — check "Add Python to PATH" during install
2. Install Git for Windows
3. Clone and launch:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
webui-user.bat

The batch file creates a venv, installs PyTorch with CUDA support, installs all Python dependencies, and starts the server.

Linux

1. Install prerequisites:

# Ubuntu/Debian
sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0

# Arch
sudo pacman -S wget git python3

2. Clone and launch:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
./webui.sh

The shell script sources webui-user.sh for configuration, creates a venv, installs dependencies, and starts the server.

macOS

1. Install Homebrew, then:

brew install cmake protobuf rust python@3.10 git wget

2. Clone and launch:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
./webui.sh

On macOS, the launcher automatically sources webui-macos-env.sh, which sets --skip-torch-cuda-test, --upcast-sampling, --no-half-vae, and --use-cpu interrogate for Apple Silicon compatibility.

Docker

The repository does not include an official Dockerfile. Community-maintained Docker images are available on Docker Hub and GitHub Container Registry.

Warning

Always verify the source of third-party Docker images before running them. Prefer images with published Dockerfiles you can audit.

Check the project wiki for community Docker setups that bundle NVIDIA Container Toolkit support.

Environment Variables

Customize the installation and launch behavior by setting these variables in webui-user.bat (Windows) or webui-user.sh (Linux/macOS):

Post-Install Verification

After the first launch completes, verify:

1. The console shows Running on local URL: http://127.0.0.1:7860
2. Open http://127.0.0.1:7860 in your browser
3. The checkpoint dropdown at the top lists your model files
4. Generate a test image with a simple prompt

Customizing Launch (webui-user Files)

Windows — edit webui-user.bat:

@echo off
set COMMANDLINE_ARGS=--xformers --autolaunch
set TORCH_COMMAND=pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

Linux/macOS — edit webui-user.sh:

export COMMANDLINE_ARGS="--xformers --autolaunch"
export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121"

3. Configuration

UI Settings Tab

The Settings tab in the web interface exposes hundreds of configurable options organized into sections. Changes take effect after clicking Apply settings (some require Reload UI). Settings are persisted to config.json in the webui root.

Key user-facing settings categories:

• Saving images: output directory, filename patterns, image format (PNG/JPEG/WEBP), quality, metadata embedding
• Saving paths: per-feature output directories (txt2img, img2img, extras, grids)
• Face restoration: default model, CodeFormer fidelity weight
• Upscaling: default upscaler for hires fix, upscaler order in the UI
• Stable Diffusion: checkpoint, VAE, clip skip, emphasis mode, enable quantization
• Sampler parameters: default sampler, eta, s_churn, s_noise
• UI: theme, quicksettings list, gallery height

Command-Line Flags

Pass flags via COMMANDLINE_ARGS in your webui-user file or directly on the command line.

GPU & Memory

Network

UI

Paths

API & Misc

For the complete flag reference with all 129 flags, see the Admin Guide.

Styles Files

The --styles-file flag specifies one or more CSV files containing saved prompt styles. By default, this is styles.csv in the webui root. You can specify multiple files using glob patterns (e.g., --styles-file "styles/*.csv").

4. Core Usage

4.1 Text-to-Image (txt2img)

The txt2img tab is the primary interface for generating images from text descriptions.

Prompt Syntax

Prompts support several special syntax features:

Emphasis — adjust the weight of specific terms:

(word)           — increase attention by 1.1x
((word))         — increase attention by 1.21x (1.1 * 1.1)
(word:1.5)       — increase attention by 1.5x
[word]           — decrease attention by 1.1x (multiply by 1/1.1)

Prompt scheduling — change prompts during generation:

[from:to:0.5]    — switch from "from" to "to" at 50% of steps
[from:to:10]     — switch at step 10
[to:0.5]         — add "to" starting at 50% of steps
[from::0.5]      — remove "from" after 50% of steps

Alternating — alternate between terms each step:

[cat|dog]        — alternate between cat and dog each step
[cat|dog|bird]   — cycle through three terms

Note

Prompt weights stack multiplicatively. ((word)) is equivalent to (word:1.21). Avoid extreme weights above 2.0 or below 0.3, as they tend to produce artifacts rather than meaningful emphasis changes.

Negative Prompts

The negative prompt field tells the model what to avoid. Common negative prompt terms:

blurry, low quality, deformed, ugly, extra limbs, watermark, text, signature

Negative prompts use the same syntax as positive prompts, including emphasis and scheduling.

Sampling Parameters

Batch Controls

• Batch count: how many batches to run sequentially
• Batch size: how many images to generate in parallel per batch (limited by VRAM)

Hires Fix

Hires fix generates at a lower resolution first, then upscales and re-renders at full resolution. This avoids the composition problems that occur when generating directly at high resolutions with SD 1.x/2.x models.

1. Enable the Hires. fix checkbox
2. Choose an Upscaler (Latent, ESRGAN, etc.)
3. Set Hires steps (0 = same as main steps)
4. Set Denoising strength (0.5–0.7 typical)
5. Set target resolution with Upscale by or explicit dimensions

Tip

For SD 1.5 models, generate at 512x512 with Hires fix upscaling to 1024x1024. This produces much better results than generating directly at 1024x1024, which often creates duplicated subjects or distorted anatomy.

4.2 Image-to-Image (img2img)

The img2img tab transforms an existing image guided by a text prompt.

Basic Workflow

1. Upload or drag an image to the input area
2. Write a prompt describing the desired output
3. Set Denoising strength (0.0 = no change, 1.0 = completely new image; 0.4–0.75 typical)
4. Click Generate

Resize Modes

Inpainting

Inpainting lets you selectively regenerate parts of an image:

1. Switch to the Inpaint sub-tab
2. Upload an image
3. Paint over the area you want to regenerate (white = regenerate, black = keep)
4. Write a prompt for what should appear in the masked area
5. Configure fill settings:
   • Fill: fill masked area with blurred surroundings
   • Original: keep original content as starting point
   • Latent noise: fill with random noise
   • Latent nothing: fill with zero latent

Soft inpainting (available via the built-in extension) provides smoother blending at mask boundaries by using a gradient transition instead of a hard edge.

InstructPix2Pix

When using an InstructPix2Pix model (instruct-pix2pix.yaml config), you can give editing instructions as prompts (e.g., "make it winter", "change the color to red") and the model applies those edits while preserving the overall image structure.

4.3 Upscaling & Extras

The Extras tab provides standalone upscaling and post-processing without regeneration.

Available Upscalers

Usage

1. Go to the Extras tab
2. Upload a single image or use the Batch Process / Batch from Directory sub-tabs
3. Select an upscaler from the Upscaler 1 dropdown
4. Set the Resize factor (e.g., 2x, 4x) or explicit target dimensions
5. Optionally set Upscaler 2 with a visibility blend for combining two upscalers
6. Click Generate

4.4 Face Restoration

Two face restoration models are available as post-processors:

CodeFormer — the recommended option, with an adjustable weight parameter:

• Weight 0 = maximum restoration (sharper faces, may alter features)
• Weight 1 = maximum fidelity (preserves original, less correction)
• Weight 0.5 is a good starting point

GFPGAN — an older alternative, no weight parameter.

Face restoration can be applied:

• Automatically during generation (enable in Settings → Face restoration)
• As a post-processor in the Extras tab
• Via the built-in scripts (postprocessing_codeformer.py, postprocessing_gfpgan.py)

4.5 Using Extra Networks

Extra networks modify the generation process without changing the base model. Access them via the card icon button below the Generate button.

LoRA / LyCORIS

LoRA (Low-Rank Adaptation) and LyCORIS files add trained concepts — characters, styles, objects — on top of a base model.

Setup: Place .safetensors files in models/Lora/.

Usage: Click the LoRA card in the Extra Networks panel, or type directly in the prompt:

<lora:filename:weight>

• filename — the LoRA file name without extension
• weight — strength from 0.0 to 1.0+ (0.7–1.0 typical)

Example:

a portrait of a woman, studio lighting <lora:add_detail:0.8>

Multiple LoRAs can be combined:

landscape photo <lora:add_detail:0.6> <lora:more_lighting:0.4>

Note

LoRA compatibility depends on the base model. A LoRA trained on SD 1.5 will not work with SDXL checkpoints and vice versa. Check the LoRA's documentation for the intended base model.

Textual Inversion Embeddings

Embeddings are small files that encode a learned concept as a prompt token.

Setup: Place .pt or .safetensors files in embeddings/.

Usage: Use the embedding's filename directly in your prompt or negative prompt. The Extra Networks panel shows available embeddings with preview thumbnails.

Hypernetworks

Hypernetworks modify the cross-attention layers. Place files in models/hypernetworks/ and select from the Settings tab or Extra Networks panel.

4.6 Prompt Styles

Styles let you save and reuse prompt/negative-prompt combinations.

Saving a Style

1. Write your prompt and negative prompt
2. Click the save style button (floppy disk icon near the prompt fields)
3. Name the style

Applying a Style

Select one or more styles from the dropdown below the prompt fields. The style's prompt and negative prompt are appended to your current inputs.

Template Syntax

Styles support a {prompt} placeholder. If a style's prompt is:

{prompt}, masterpiece, best quality, highly detailed

Then your typed prompt replaces {prompt}, and the rest is appended.

4.7 Image Interrogation (CLIP / DeepDanbooru)

The Interrogate buttons on the img2img tab reverse-engineer a text prompt from an uploaded image.

• CLIP Interrogate (the "clipboard" button) — produces a natural-language description
• DeepDanbooru — produces anime/booru-style tags

Upload an image to img2img, then click the interrogate button. The generated prompt appears in the prompt field, ready for editing or use.

4.8 Extensions

Extensions add features without modifying the core codebase.

Installing from URL

1. Go to Extensions → Install from URL
2. Paste the git repository URL
3. Click Install
4. Go to Installed tab and click Apply and restart UI

Installing from Index

1. Go to Extensions → Available
2. Click Load from to fetch the extension index
3. Browse or search for extensions
4. Click Install on the desired extension

Managing Extensions

• Enable/Disable: toggle checkboxes on the Installed tab
• Update: click Check for updates then Apply and restart UI
• Remove: delete the extension folder from extensions/

Built-in Extensions

These ship with the webui and can be enabled/disabled:

Important

Extensions run arbitrary code on your system. Only install extensions from sources you trust. Review an extension's repository and community feedback before installing.

5. Common Tasks

5.1 Generate Your First Image

1. Launch the webui and navigate to http://127.0.0.1:7860
2. Select a checkpoint from the top dropdown (e.g., v1-5-pruned.safetensors)
3. In the prompt field, type: a fluffy orange cat sitting on a windowsill, sunlight, soft focus
4. Set Steps to 25, CFG Scale to 7, Size to 512x512
5. Click Generate or press Ctrl+Enter
6. The output appears in the gallery on the right; click to view full-size

5.2 Use LoRA for Style or Character

1. Download a LoRA .safetensors file and place it in models/Lora/
2. Click the Extra Networks card button (below Generate)
3. Navigate to the Lora tab
4. Click the LoRA card to insert it into your prompt
5. Adjust the weight: <lora:my_lora:0.8>
6. Add your subject prompt before the LoRA tag
7. Generate

5.3 Upscale a Generated Image

1. Generate an image or go to the Extras tab
2. Upload the image (or send it from txt2img using the "Send to Extras" button)
3. Select Upscaler 1: RealESRGAN_x4plus (good general choice)
4. Set Resize to 2 (doubles dimensions)
5. Click Generate

5.4 Fix Faces in a Photo

1. Go to Extras tab
2. Upload the image with faces to fix
3. Enable CodeFormer under face restoration
4. Set CodeFormer weight to 0.5 (balance between restoration and fidelity)
5. Click Generate

5.5 Inpaint Part of an Image

1. Go to img2img → Inpaint sub-tab
2. Upload the image
3. Use the brush to paint over the area to regenerate
4. In the prompt, describe what should appear: a red rose
5. Set Denoising strength to 0.75
6. Set Masked content to fill
7. Click Generate

5.6 Create Variations with Seed

1. Generate an image you like
2. Note the Seed value shown below the output image
3. Lock the seed by clicking the recycle icon (or enter it manually)
4. Make small prompt or parameter changes
5. Generate again — the composition stays similar while details change

For slight variations, use Variation seed with a small Variation strength (0.05–0.15).

5.7 Use X/Y/Z Plot for Parameter Comparison

1. In txt2img or img2img, open the Script dropdown at the bottom
2. Select X/Y/Z plot
3. Choose axis types (e.g., X = Sampler, Y = Steps, Z = CFG Scale)
4. Enter values:
   • Sampler: Euler a, DPM++ 2M Karras, DDIM
   • Steps: 10, 20, 30, 50
   • CFG: 5, 7, 10
5. Click Generate — produces a grid comparing every combination

Tip

X/Y/Z plots are the fastest way to find optimal parameters for a new model or LoRA. Start with a sampler comparison, then narrow down steps and CFG scale.

5.8 Run the API Alongside the Web UI

1. Add --api to your COMMANDLINE_ARGS:

export COMMANDLINE_ARGS="--api"

2. Launch the webui normally
3. Access the API docs at http://127.0.0.1:7860/docs (Swagger UI)
4. Make requests programmatically:

import requests
import base64

payload = {
    "prompt": "a beautiful sunset over mountains",
    "steps": 25,
    "width": 512,
    "height": 512
}

response = requests.post("http://127.0.0.1:7860/sdapi/v1/txt2img", json=payload)
image_data = response.json()["images"][0]

with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_data))

5.9 Generate from a List of Prompts

1. Create a text file with one prompt per line:

a red sports car on a mountain road
a blue sailboat on calm waters
a green forest with morning fog

2. In txt2img, select the Prompts from file or textbox script
3. Either paste prompts into the text box or load the file
4. Set your generation parameters
5. Click Generate — each prompt produces one or more images

5.10 Use Outpainting to Extend an Image

1. Go to img2img → Inpaint sub-tab
2. Upload your image
3. Select the Outpainting mk2 script from the Script dropdown
4. Configure the number of pixels to extend on each side
5. Set denoising strength to 0.8–0.9
6. Write a prompt matching the image's subject/style
7. Click Generate — the model fills in the new area coherently

5.11 SD Upscale (Tiled Upscaling)

For upscaling with Stable Diffusion refinement (better quality than plain upscalers):

1. Go to img2img
2. Upload the image to upscale
3. Select the SD upscale script
4. Choose a tile overlap (e.g., 64 pixels)
5. Choose an upscaler (e.g., RealESRGAN_x4plus)
6. Set scale factor to 2
7. Set denoising strength to 0.2–0.4 (lower = closer to original)
8. Generate

5.12 Checkpoint Merging

Merge two or three models to combine their strengths:

1. Go to the Checkpoint Merger tab
2. Select Primary model (A) and Secondary model (B)
3. Optionally select a Tertiary model (C)
4. Choose interpolation method: Weighted sum or Add difference
5. Set the Multiplier (0 = all A, 1 = all B)
6. Choose output format (safetensors recommended)
7. Click Merge

The merged checkpoint appears in models/Stable-diffusion/ and can be selected like any other model.

6. Troubleshooting

Keyboard Shortcuts

Before diving into issues, know these shortcuts for controlling generation:

Error: RuntimeError: Torch is not able to use GPU

Cause: PyTorch was installed without CUDA support, or NVIDIA drivers are outdated.

Solution:

1. Check your NVIDIA driver version: nvidia-smi
2. Ensure you have CUDA-compatible drivers (525+ for CUDA 12.x)
3. Reinstall PyTorch with CUDA support by setting in webui-user.sh / webui-user.bat:

export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121"

4. Delete the venv folder and relaunch to force reinstall

Prevention: Always install NVIDIA drivers before the webui.

Error: Cannot import setuptools.build_meta

Cause: Python 3.12+ removed distutils, which some dependencies still require.

Solution:

1. Install Python 3.10.6 alongside your current version
2. Update webui-user.sh/webui-user.bat to point python_cmd to Python 3.10
3. Delete the venv folder and relaunch

Prevention: Use Python 3.10.x for this project.

Error: No module named pip

Cause: The Python installation is missing pip, or the venv was corrupted.

Solution:

1. Ensure pip is installed: python -m ensurepip
2. If the venv is corrupted, delete the venv folder and relaunch
3. On Linux, you may need: sudo apt install python3-pip python3-venv

Prevention: Use the official Python installer with pip included.

Error: Python version is too recent

Cause: Dependencies (especially torch, xformers) don't support Python 3.12+.

Solution:

1. Install Python 3.10.6
2. Set python_cmd=python3.10 in webui-user.sh
3. Delete venv and relaunch

Prevention: Stick to Python 3.10.x.

Error: CLIP loading errors

Cause: Corrupted model download or HuggingFace connectivity issue.

Solution:

1. Delete the cached CLIP model (usually in ~/.cache/huggingface/)
2. If behind a firewall, set HF_ENDPOINT to a mirror
3. Relaunch to re-download

Prevention: Ensure stable internet during first launch.

Error: AttributeError: 'ImageDraw' object has no attribute 'multiline_textsize'

Cause: Pillow 10+ removed the deprecated multiline_textsize method.

Solution:

1. Pin Pillow to a compatible version:

pip install Pillow==9.5.0

2. Or update the webui to a version that supports Pillow 10+

Prevention: Let the webui manage its own dependencies via requirements_versions.txt.

Error: RuntimeError: Cannot add middleware after app started

Cause: Extension or API configuration conflict during startup.

Solution:

1. Disable all extensions (rename extensions folder temporarily)
2. If it works, re-enable extensions one by one to find the conflict
3. Update the problematic extension or remove it

Prevention: Keep extensions updated.

Warning

When disabling extensions by renaming the folder, make sure to note the original name. Some extensions create configuration files that reference the folder name.

Out of Memory Errors

Cause: Insufficient GPU VRAM for the requested operation.

Solution:

1. Add --medvram to COMMANDLINE_ARGS (moderate savings)
2. If still OOM, use --lowvram (significant savings, slower)
3. Reduce image dimensions (512x512 instead of 1024x1024)
4. Reduce batch size to 1
5. Add --xformers or --opt-sdp-attention for memory-efficient attention
6. Close other GPU-using applications

Prevention: Monitor VRAM with nvidia-smi and know your GPU's limits.

NaN Images / Black Images

Cause: Numerical instability during generation, often with certain VAEs or half-precision mode.

Solution:

1. Add --no-half-vae to COMMANDLINE_ARGS (most common fix)
2. If still occurring, add --no-half (uses more VRAM)
3. Try a different VAE
4. Try --upcast-sampling

Prevention: Use --no-half-vae by default if you encounter this even once.

Slow Generation Speed

Cause: Missing optimizations or sub-optimal configuration.

Solution:

1. Enable --xformers (NVIDIA GPUs, best speedup)
2. Or use --opt-sdp-attention (PyTorch 2.0+, no extra install)
3. Ensure you're not in --lowvram mode unnecessarily
4. Check that no other processes are using the GPU
5. Use appropriate resolution for your model (512px for SD 1.x, 1024px for SDXL)

Prevention: Always use --xformers or --opt-sdp-attention.

Extensions Won't Install

Cause: Git connectivity issues, Python dependency conflicts, or permissions.

Solution:

1. Check internet connectivity
2. Try installing manually:

cd extensions
git clone <extension-url>

3. Check the console for specific pip errors
4. Some extensions need specific Python packages — install them manually

Prevention: Keep git updated and check extension compatibility before installing.

Model Loading Failures

Cause: Corrupted download, incompatible format, or wrong config.

Solution:

1. Re-download the model file (check file size matches the source)
2. Prefer .safetensors format (safer and faster than .ckpt)
3. Ensure the correct config YAML exists for the model architecture
4. Check console output for specific error messages

Prevention: Always verify file integrity after downloading large models.

Caution

The .ckpt format uses Python pickle, which can execute arbitrary code during loading. Always prefer .safetensors files, which are safe by design. If you must use .ckpt files, do not disable --disable-safe-unpickle.

AMD GPU Issues

Cause: AMD GPUs require ROCm on Linux; Windows support is limited.

Solution:

1. Linux: install ROCm and use --use-cpu for unsupported operations
2. Set TORCH_COMMAND to install the ROCm version of PyTorch:

export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm5.6"

3. Some features (xformers) are NVIDIA-only; use --opt-sdp-attention instead
4. Check the webui wiki for AMD-specific guidance

Prevention: Verify your AMD GPU and ROCm version are in the PyTorch compatibility matrix.

WebUI Won't Start

Cause: Various — dependency issues, port conflicts, corrupted venv.

Solution:

1. Read the console output carefully for the first error
2. Check if port 7860 is already in use: change with --port 7861
3. Delete venv folder and relaunch for a clean install
4. Ensure Python 3.10.x and git are on PATH
5. On Windows, run webui-user.bat from a terminal (not double-click) to see errors

Prevention: Keep a working venv backup before major updates.

7. FAQ

Q: What GPUs are supported?

NVIDIA GPUs with CUDA support are the primary target. Minimum 4 GB VRAM (GTX 1050 Ti class), recommended 8 GB+ (RTX 3060/4060 class). AMD GPUs work on Linux via ROCm. Intel Arc GPUs have experimental support. Apple Silicon (M1/M2/M3) works via MPS backend with the macOS launch script.

Q: How much VRAM do I need?

For SD 1.5 at 512x512: 4 GB minimum (with --medvram), 6 GB comfortable, 8 GB+ for batches and hires fix. For SDXL at 1024x1024: 8 GB minimum, 12 GB+ recommended. The --medvram and --lowvram flags reduce requirements at the cost of speed.

Q: Can I use CPU only?

Yes, add --skip-torch-cuda-test --use-cpu all to COMMANDLINE_ARGS. Generation will be extremely slow (minutes per image instead of seconds), but it works for testing or if you have no compatible GPU.

Q: How do I update the webui?

cd stable-diffusion-webui
git pull

Then relaunch. The startup script handles dependency updates automatically. For a clean update, delete the venv folder before relaunching.

Q: Where are generated images saved?

By default:

• txt2img: outputs/txt2img-images/
• img2img: outputs/img2img-images/
• Extras: outputs/extras-images/
• Grids: outputs/txt2img-grids/ and outputs/img2img-grids/

These paths are configurable in Settings → Saving paths.

Q: How do I add custom models?

Place model files in the appropriate directory:

• Checkpoints (.safetensors, .ckpt): models/Stable-diffusion/
• VAEs: models/VAE/
• LoRAs: models/Lora/
• Embeddings: embeddings/
• Upscalers (ESRGAN etc.): models/ESRGAN/

Click the refresh button next to the model dropdown to detect new files without restarting.

Q: What's the difference between samplers?

Samplers control the denoising algorithm. Key distinctions:

• Euler / Euler a: fast, good baseline. "a" = ancestral (adds randomness each step)
• DPM++ 2M Karras: excellent quality-to-speed ratio, widely recommended
• DPM++ SDE Karras: good for detailed images, slightly slower
• DDIM: deterministic, good for img2img and low step counts
• UniPC: fast convergence, good results in fewer steps
• LCM: designed for LCM-LoRA, produces results in 4–8 steps

Ancestral samplers (marked with "a") never fully converge — increasing steps continues changing the image. Non-ancestral samplers converge to a final result.

Q: How do I use SDXL?

1. Download an SDXL checkpoint (e.g., sd_xl_base_1.0.safetensors) and place it in models/Stable-diffusion/
2. Optionally download the SDXL refiner model
3. Select the SDXL checkpoint from the dropdown
4. Set resolution to 1024x1024 (SDXL's native resolution)
5. Generate as usual — the webui automatically detects the model architecture

SDXL requires more VRAM than SD 1.5 (8 GB+ recommended).

Q: Can I run it on a server or cloud?

Yes. Use --listen to bind to all interfaces and --port to set the port. For security, add --gradio-auth user:password. For public access, use --share (Gradio tunnel) or --ngrok with a token. For API-only access, use --nowebui --api.

Q: How do I use the API?

Launch with --api flag. The API is documented at http://127.0.0.1:7860/docs. Key endpoints:

• POST /sdapi/v1/txt2img — text-to-image
• POST /sdapi/v1/img2img — image-to-image
• GET /sdapi/v1/sd-models — list models
• POST /sdapi/v1/options — change settings
• GET /sdapi/v1/progress — check generation progress

Use --api-auth user:pass for API authentication.

Q: What is --xformers?

The --xformers flag enables the xformers library for memory-efficient cross-attention. It reduces VRAM usage and increases speed on NVIDIA GPUs. If xformers fails to install, use --opt-sdp-attention as an alternative (requires PyTorch 2.0+).

Q: How do I disable the NSFW safety checker?

The webui does not include a built-in NSFW filter by default. If an extension adds one, disable it from the Extensions tab. The --disable-safe-unpickle flag is unrelated — it controls pickle file safety checks, not content filtering.

Q: Can I use multiple GPUs?

The webui does not natively support multi-GPU generation. Each instance uses one GPU. To use a specific GPU, set the CUDA_VISIBLE_DEVICES environment variable:

export CUDA_VISIBLE_DEVICES=1  # use second GPU

For parallel processing, run multiple instances on different ports and GPUs.

Q: How do I roll back to an older version?

cd stable-diffusion-webui
git log --oneline -20    # find the commit you want
git checkout <commit-hash>

Delete the venv folder and relaunch to ensure dependencies match the older version. Back up your config.json and ui-config.json before rolling back.

Q: What is the config.json file?

config.json in the webui root stores all settings configured through the UI's Settings tab. It is created automatically on first settings save. You can edit it directly (with the webui stopped) or back it up before making changes. ui-config.json stores UI element defaults (dropdown selections, slider ranges).

Appendix: Reference Tables

Available Samplers

K-diffusion samplers: Euler, Euler a, LMS, Heun, DPM2, DPM2 a, DPM++ 2S a, DPM++ 2M, DPM++ SDE, DPM++ 2M SDE, DPM++ 3M SDE, DPM fast, DPM adaptive, LMS Karras, DPM2 Karras, DPM2 a Karras, DPM++ 2S a Karras, DPM++ 2M Karras, DPM++ SDE Karras, DPM++ 2M SDE Karras, DPM++ 3M SDE Karras, DPM++ 2M SDE Exponential, DPM++ 3M SDE Exponential, Restart

CompVis samplers: DDIM, PLMS, UniPC

LCM sampler: LCM

Supported Model Architectures

Built-in Scripts