Stable Diffusion WebUI

The most popular web interface for Stable Diffusion on CLORE.AI GPUs.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Server Requirements

Parameter	Minimum	Recommended
RAM	16GB	32GB+
VRAM	8GB	12GB+
Network	500Mbps	1Gbps+
Startup Time	10-20 minutes	-

Startup Time: First launch installs Python dependencies and downloads the base model (10-20 minutes depending on network speed). HTTP 502 during this time is normal.

Why SD WebUI?

• Feature-rich - txt2img, img2img, inpainting, outpainting

• Extensions - Huge ecosystem of plugins

• User-friendly - Intuitive web interface

• Well documented - Large community support

Quick Deploy on CLORE.AI

Docker Image:

universonic/stable-diffusion-webui:latest

Ports:

22/tcp
8080/http

Command:

./webui.sh --listen --xformers

Verify It's Working

After deployment, find your http_pub URL in My Orders:

# Check if UI is accessible (may take 10-20 min on first run)
curl https://your-http-pub.clorecloud.net/

If you get HTTP 502 for more than 20 minutes, check:

1. Server has 16GB+ RAM

2. Server has 8GB+ VRAM

3. Network speed is adequate for downloading dependencies

Accessing Your Service

When deployed on CLORE.AI, access SD WebUI via the http_pub URL:

• Web UI: https://your-http-pub.clorecloud.net/

• API (if enabled): https://your-http-pub.clorecloud.net/sdapi/v1/

All localhost:7860 examples below work when connected via SSH. For external access, replace with your https://your-http-pub.clorecloud.net/ URL.

Installation

Using Docker (Recommended)

docker run -d --gpus all \
    -p 8080:8080 \
    -v sd-webui-data:/app/stable-diffusion-webui \
    universonic/stable-diffusion-webui:latest

Manual Installation

# Install dependencies
sudo apt install python3.10 python3.10-venv git wget

# Clone repository
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# Run (auto-installs everything)
./webui.sh --listen --xformers

Directory Structure

stable-diffusion-webui/
├── models/
│   ├── Stable-diffusion/   # Main models (.safetensors)
│   ├── Lora/               # LoRA models
│   ├── VAE/                # VAE models
│   ├── ControlNet/         # ControlNet models
│   └── ESRGAN/             # Upscalers
├── embeddings/             # Textual inversions
├── extensions/             # Installed extensions
├── outputs/                # Generated images
└── scripts/                # Custom scripts

Download Models

Checkpoints

cd models/Stable-diffusion

# SD 1.5
wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.safetensors

# SDXL
wget https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors

# Realistic Vision (Photorealistic)
wget "https://civitai.com/api/download/models/245598" -O realisticVision_v60B1.safetensors

# DreamShaper (Artistic)
wget "https://civitai.com/api/download/models/351306" -O dreamshaper_8.safetensors

VAE (Better Colors)

cd models/VAE

# SD 1.5 VAE
wget https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors

# SDXL VAE
wget https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors

Basic Usage

txt2img (Text to Image)

1. Select model from dropdown

2. Enter positive prompt: what you want

3. Enter negative prompt: what to avoid

4. Set dimensions (512x512 for SD1.5, 1024x1024 for SDXL)

5. Click Generate

img2img (Image to Image)

1. Go to img2img tab

2. Upload source image

3. Enter prompt describing desired changes

4. Adjust Denoising strength (0.3-0.8)

5. Generate

Inpainting

1. Go to img2img → Inpaint

2. Upload image

3. Draw mask over area to change

4. Enter prompt for masked area

5. Generate

Essential Settings

Generation Settings

Setting	SD 1.5	SDXL
Width	512	1024
Height	512	1024
Steps	20-30	20-40
CFG Scale	7	5-7
Sampler	DPM++ 2M Karras	DPM++ 2M Karras

Command Line Arguments

./webui.sh \
    --listen \              # Allow external access
    --port 7860 \           # Port number
    --xformers \            # Memory optimization
    --enable-insecure-extension-access \  # Allow extensions
    --api \                 # Enable API
    --no-half-vae           # Fix black images

For Low VRAM

./webui.sh \
    --listen \
    --medvram \           # 6-8GB VRAM
    # or
    --lowvram \           # 4GB VRAM
    --xformers

Popular Extensions

Must Have

Extension	Purpose
ControlNet	Guided generation
ADetailer	Auto face/hand fix
Ultimate SD Upscale	Better upscaling
sd-webui-segment-anything	Segmentation
Regional Prompter	Multi-region prompts

Install Extensions

1. Go to Extensions tab

2. Click Available

3. Click Load from

4. Search and Install

5. Apply and Restart UI

Or manually:

cd extensions
git clone https://github.com/Mikubill/sd-webui-controlnet.git

ControlNet

Installation

# Install extension
cd extensions
git clone https://github.com/Mikubill/sd-webui-controlnet.git

# Download models
cd ../models/ControlNet
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny.pth
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose.pth
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth

Usage

1. Expand ControlNet section

2. Upload control image

3. Select preprocessor (Canny, OpenPose, etc.)

4. Select model matching preprocessor

5. Generate

API Usage

Enable with --api flag, then:

import requests
import base64

# For external access, use your http_pub URL:
API_URL = "https://your-http-pub.clorecloud.net"
# Or via SSH: API_URL = "http://localhost:7860"

def txt2img(prompt, negative="", steps=20, width=512, height=512):
    response = requests.post(
        f"{API_URL}/sdapi/v1/txt2img",
        json={
            "prompt": prompt,
            "negative_prompt": negative,
            "steps": steps,
            "width": width,
            "height": height,
        }
    )
    return base64.b64decode(response.json()["images"][0])

# Generate and save
image_data = txt2img("A beautiful sunset over mountains")
with open("output.png", "wb") as f:
    f.write(image_data)

img2img API

import base64

def img2img(prompt, image_path, denoising=0.5):
    with open(image_path, "rb") as f:
        image_b64 = base64.b64encode(f.read()).decode()

    response = requests.post(
        f"{API_URL}/sdapi/v1/img2img",
        json={
            "prompt": prompt,
            "init_images": [image_b64],
            "denoising_strength": denoising,
            "steps": 30,
        }
    )
    return base64.b64decode(response.json()["images"][0])

Prompt Writing

Basic Structure

[subject], [style], [details], [lighting], [quality tags]

Example Prompts

# Portrait
portrait of a young woman, professional photography,
soft lighting, shallow depth of field, 8k uhd,
highly detailed, photorealistic

# Landscape
majestic mountain landscape at sunset, dramatic clouds,
golden hour lighting, national geographic style,
8k wallpaper, highly detailed

# Anime
1girl, silver hair, blue eyes, school uniform,
cherry blossoms, spring, masterpiece, best quality

Negative Prompts

# General
lowres, bad anatomy, bad hands, text, error,
missing fingers, cropped, worst quality, low quality,
jpeg artifacts, signature, watermark, blurry

# For photorealistic
cartoon, anime, illustration, painting, drawing,
3d render, cgi

Performance Tips

1. Enable xFormers - Significant speed boost

2. Use VAE - Better colors

3. Batch size 1 - For limited VRAM

4. Hires Fix - Generate small, then upscale

5. ADetailer - Auto-fix faces

GPU Requirements

Model	Minimum VRAM	Recommended VRAM	Min RAM
SD 1.5	4GB	8GB	16GB
SD 2.1	6GB	8GB	16GB
SDXL	8GB	12GB	16GB
With ControlNet	+2GB	+4GB	16GB

GPU Presets

RTX 3060 12GB (Budget)

# Launch command
./webui.sh --medvram --xformers

# Recommended settings.json:
# - SD 1.5: 512x512, batch 4, 20-30 steps
# - SDXL: 768x768, batch 1, 20 steps
# - Enable VAE tiling in Settings
# - Use ADetailer for faces

Best models: SD 1.5, DreamShaper, RealisticVision

RTX 3090 24GB (Optimal)

# Launch command
./webui.sh --xformers

# Recommended settings:
# - SD 1.5: 512x512, batch 8, 30 steps
# - SDXL: 1024x1024, batch 2, 30 steps
# - ControlNet + SD 1.5 works great
# - Hires fix to 2x works

Best models: SDXL, Juggernaut, RealVisXL

RTX 4090 24GB (Performance)

# Launch command
./webui.sh --xformers --opt-sdp-attention

# Recommended settings:
# - SD 1.5: 512x512, batch 16, 30 steps
# - SDXL: 1024x1024, batch 4, 40 steps
# - Multiple ControlNets simultaneously
# - Hires fix to 4x

Best models: SDXL, Pony Diffusion, Any high-res model

A100 40GB/80GB (Production)

# Launch command
./webui.sh --opt-sdp-attention --no-half-vae

# Recommended settings:
# - SDXL: 1024x1024, batch 8+, 50 steps
# - Multiple ControlNet + IP-Adapter
# - Hires fix to 4096x4096
# - AnimateDiff for video

Best for: Batch generation, complex workflows, video

Cost Estimate

Typical CLORE.AI marketplace rates:

GPU	CLORE/day	Approx USD/hr	SD1.5 Speed	SDXL Speed
RTX 3060 12GB	~80	~$0.02	~4 sec	~12 sec
RTX 3090 24GB	~150	~$0.03	~2 sec	~6 sec
RTX 4090 24GB	~200	~$0.04	~1 sec	~3 sec
A100 40GB	~400	~$0.08	~0.5 sec	~2 sec

Prices vary by provider. Check CLORE.AI Marketplace for current rates.

Troubleshooting

HTTP 502 for a long time

1. Check RAM: Server must have 16GB+ RAM

2. Check VRAM: Must have 8GB+ for SDXL

3. Dependencies installing: First run takes 10-20 min

4. Slow network: Low bandwidth causes longer startup

Black Images

# Add to launch args
--no-half-vae

Out of Memory

# Use optimization
--medvram
--xformers

Extensions Not Loading

--enable-insecure-extension-access

Slow Generation

1. Enable xFormers

2. Reduce image size

3. Lower steps (20 is often enough)

Next Steps

• ComfyUI - More advanced workflows

• FLUX.1 - Latest model

• ControlNet - Guided generation

• LoRA Training - Custom models