Main

h1. Stable Diffusion Setup

Stable Diffusion <add info here>

h2. Prerequisites

h3. Hardware

* Modern Linux computer with 20 GB disk space 

* NVidia GPU with >= 4 GB VRAM

h3. CUDA

Installation of NVidia Driver and CUDA is not covered here.

h3. Docker

# Ensure Docker prereqs are all installed:

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$ sudo apt update

$ sudo apt install ca-certificates curl gnupg lsb-release

</pre>

# Install GPG key:

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$ sudo mkdir -m 0755 -p /etc/apt/keyrings

$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

</pre>

# Add Docker APT repository configuration:

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echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

</pre>

# Install packages:

<pre>

$ sudo apt install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

</pre>

h3. NVidia Container Toolkit

# Install GPG key:

<pre>

$ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -

</pre>

# Setup APT repo:

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$ sudo bash -c "curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu22.04/nvidia-docker.list > /etc/apt/sources.list.d/nvidia-docker.list"

</pre>

# Install package:

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$ sudo apt update

$ sudo apt -y install nvidia-container-toolkit

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# Restart docker:

<pre>

$ sudo systemctl restart docker

</pre>

h2. Stable Diffusion Docker Container of WebUI

# Go to https://github.com/AbdBarho/stable-diffusion-webui-docker/releases, download the latest source code release (ZIP) under Assets.  Unzip it somewhere.

# Go to the directory where you unzipped the archive, and run

<pre>

sudo docker compose --profile download up --build

</pre>

this will download 12GB of pretrained models. Wait until it is finished, then

<pre>

docker compose --profile auto up --build

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More details are available on the project's wiki: https://github.com/AbdBarho/stable-diffusion-webui-docker/wiki/Setup

# Wait until you see

<pre>

 Running on local URL:  http://0.0.0.0:7860

 To create a public link, set `share=True` in `launch()`.

</pre>

Then go to http://localhost:7860/ in any browser.

h2. Naughty Stuff

# Download one of the better 'Nudifying' models,

** Go to https://civitai.com/models/2661/uber-realistic-porn-merge-urpm (account required) and under Versions, click on 'URPMv1.2-inpainting', then at the right, download

*** "Pruned Model SafeTensor (1.99 GB)"

** "Config"

** Save them to the @data/StableDiffusion@ folder in the Webui docker project you unzipped earlier. The model file should be called @uberRealisticPornMerge_urpmv12-inpainting.safetensors@ and the config file should be named @uberRealisticPornMerge_urpmv12-inpainting.yaml@

# Embeddings​

** Add the following files in the @data/embeddings@: https://gofile.io/d/az0mmz

** These allow you to add @breasts@, @small_tits@ and @Style-Unshaved@ in your prompt, and provide better quality breasts / vaginas. The first one is more generalized, the latter is well.. yes.

** Check the Discord link in the 'Additional Tips', ppl post additional embeddings on there.

# Loading Model​

** In the webui, at the top left, "Stable Diffusion checkpoint", hit the 'Refresh' icon.

** Now you should see the @uberRealisticPornMerge_urpmv12@ model in the list, select it.

# Model Parameters​

** Go to the 'img2img' tab, and then the 'Inpaint' tab.

** In the first textarea (positive prompt), enter

*** <pre>RAW photo of a nude woman, naked​</pre>

** In the second textarea (negative prompt), enter

*** <pre>((clothing), (monochrome:1.3), (deformed, distorted, disfigured:1.3), (hair), jeans, tattoo, wet, water, clothing, shadow, 3d render, cartoon, ((blurry)), duplicate, ((duplicate body parts)), (disfigured), (poorly drawn), ((missing limbs)), logo, signature, text, words, low res, boring, artifacts, bad art, gross, ugly, poor quality, low quality, poorly drawn, bad anatomy, wrong anatomy​</pre>

** If not otherwise mentioned, leave default,

*** Masked content: fill (will just fill in the area without taking in to consideration the original masked 'content', but play around with others too)​

*​** Inpaint area: Only masked​

*** Sampling method: DPM++ SDE Karras (one of the better methods that takes care of using similar skin colors for masked area, etc)​

​*** Sampling steps: start with 20, then increase to 50 for better quality/results when needed. But the higher, the longer it takes. I have mostly good results with 20, but it all depends on the complexity of the source image and the masked area.​

​*** CFG Scale: 7 - 12 (mostly 7)​

​*** Denoise Strength: 0.75 (default, the lower you set this, the more it will look like the original masked area)​

​

h2. Miscellaneous

* Convert PNG files to JPG en masse:

<pre>

ls -1 *.png | parallel --eta convert -quality 85 '{}' '{.}.jpg'

</pre>