How to make your own AI LAB

Over the past six months, I have dedicated countless hours to building my own AI lab at home. In this article, I aim to guide you through the process of setting up your AI lab at home. First, let’s discuss the benefits and drawbacks of having an AI lab at home. Then, we will move on to installation instructions for setting up the lab on your computer.

Benefits of a local AI Lab

Having a local AI lab brings several advantages:

  1. Rapid testing of cutting-edge techniques and models: You can quickly try out new approaches without worrying about expensive cloud services or complex setup processes. This enables you to rapidly iterate and improve your AI applications.
  2. Cost savings: Running an AI lab locally means you only need to consider the costs of hardware and electricity. Since most home computers are already powerful enough to handle AI tasks, the initial investment can be relatively low. Additionally, there are no monthly fees or data storage charges associated with using an external service.
  3. Flexibility: With a local AI lab, you can experiment with different algorithms, libraries, and frameworks without being limited by vendor lock-in or proprietary software restrictions. This gives you greater control over your projects and enables you to tailor solutions to meet specific needs.
  4. Security: Running an AI lab locally allows you to maintain full control over your data and models. Unlike using cloud-based services, you don’t have to worry about security breaches or unauthorized access to sensitive information.
  5. Education: Building and maintaining an AI lab can provide valuable hands-on experience in AI development and deployment. By learning how to design, train, and deploy models, you can gain in-depth knowledge of AI technologies and best practices.

In summary, a local AI lab offers numerous benefits, including rapid prototyping, cost savings, flexibility, security, and education. These advantages make it an attractive option for developers looking to create AI applications and solutions.

Downsides of a local AI Lab

Having your own AI Lab does bring some downsides with it. In my professional and personal experience with these systems, these are as follows:

  1. Upfront cost: Creating your own AI Lab involves significant upfront costs such as buying a motherboard, cables, and licensing costs for the Windows operating system (OS). These costs can be prohibitive for those with limited budgets. However, if you already have a gaming PC, you may have bypassed most of the upfront costs for running your homebrew AI models.
  2. Maintenance responsibility: When you run an AI lab locally, you become responsible for all maintenance tasks. This includes both software and hardware maintenance. If you are new to software and hardware maintenance, it will be an uphill battle for you.
  3. Limited capabilities: Your AI lab might not be able to do everything you want. Balancing your workload becomes crucial when using an AI lab at home. You need to carefully identify which tasks you want to prioritize and focus on.

These are the main downsides of having a local AI Lab. It’s important to consider these factors before deciding whether creating your own lab is the right choice for you.


Hardware and software requirements

To have your own local AI Lab you will need the following:


  1. 16 Gb of memory
  2. An nvidia video with CUDA support with atleast 8Gb of VRAM
  3. 64 Gb of availible storage
  4. Have Git installed
  5. Have python installed
  6. Have anaconda installed
  7. Have CUDA toolkit installed


  1. 32 Gb of memory
  2. An nvidia video with CUDA support with atleast 12Gb of VRAM
  3. 64 Gb of availible storage
  4. Have Git installed
  5. Have python installed
  6. Have anaconda installed
  7. Have CUDA toolkit installed

Installing your local AI lab

To kick off your local AI lab, you should start by identifying a set of tasks you want to perform. For each task, there are hundreds of systems you could use AI for. In this article, we assume you want to either use DALL-E3 image generation systems or your own ChatGPT.


Now that you have installed text generation and/or the Stable Diffusion WebUI, you can play with it without any difficulties or limitations. In future articles, I will provide you with a list of potential sources from which you can obtain additional models to experiment and play with.

If you want to explore the possibilities of AI further, check out my guide on converting any portrait into an art style using the Stable Diffusion WebUI. (link)

If you want tutorials on other topics related to AI or implementing these technologies, please feel free to contact me.

– Niels van der Burg


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How to change your selfie into an oil painting using AI

Example of image style transfer using Stable Diffusion

With image generators like OpenAI’s Dall-E 2 and Midjourney, you can create visually appealing images with just a few words. However, if you have an existing image that you want to transfer a new style onto, it becomes a bit more challenging. Fortunately, with the use of local or private cloud AI, you can easily transfer styles onto any image you have. In this short tutorial, I will show you how to transfer a portrait of a person to a set of art styles.

I highly recommend reading the article from on installing Stable Diffusion WebUI.

Improving the quality of AI-generated images using ControlNet

If you are familiar with Stable Diffusion WebUI, you know that it generates high-quality images. However, sometimes the results may not be exactly what you want or expect. That’s where ControlNet comes into play. With ControlNet, you can fine-tune the generation process and achieve even better results.

What is ControlNet?

ControlNet is an extension for Stable Diffusion WebUI that enables you to control specific aspects of the image generation process. It works by extracting certain features (e.g., contours, depth, or base image) from source images and transferring them onto the generated images. This helps the AI model focus on generating specific details or characteristics of the desired image.

How to use ControlNet?

To use ControlNet, follow these steps:

1. Install the ControlNet extension: Go to the Extension tab in Stable Diffusion WebUI and select “Load from.” Find the “sd-webui-controlnet” extension and click “Install.”

2. Download the required models: Tile and Lineart. Place these models in the extensions/sd-webui-controlnet/models folder inside the Stable Diffusion WebUI directory.

3. Enable multi-ControlNet: Go to Settings > ControlNet and increase the value of the Multi ControlNet slider to 2. This will allow you to run two ControlNet models simultaneously.

4. Generate an image: Choose an appropriate prompt and generate an image.

5. Configure ControlNet: For each ControlNet model, specify the source image path and the type of feature extraction (contours, depth, or base image).

6. Adjust the balance between stability and fidelity: The Balance parameter determines the tradeoff between maintaining the original image structure and achieving detailed features. Lower values prioritize structure, while higher values emphasize detail. You can adjust this parameter in the individual ControlNet configurations.

Transferring style

To transfer the style onto your image of choice, you need to go to the tab labeled ‘img2img’. Drag your desired image into the canvas, then set Denoising to 0.50.

Next, set Resize Mode to ‘Crop and Resize’. Then, go to ControlNet and click on the radio button labeled ‘Tile’. Enabling this model will turn on the ControlNet Tile Model.

Go to ControlNet Unit 1 and select LineArt as your Image Style. Select ‘LineArt_Realistic’ from the dropdown menu for Preprocessor, which will set the preprocessor to create realistic representations of contours instead of creating lineart over everything. Enable this model.

Finally, go back to the top and use this prompt to create an oil painting of yourself: For this generation, I got this image back:

AI style transfer without taking the entire image as input.

In my case, it got close, but it changed me to much. It turned me 40 years older and it added details that did not exist.

This is where the human work comes in. Now, all you have to do is tweak the denoise you changed earlier to try and get the result you want.

In my case and maybe in your case to, this did not work. If that is the case, keep on reading.

The classic old tale of more data

In my case, the failure was due to reducing the amount of information in the source image. We reduced a 2000×2000 pixel image to a 512×512 pixel image, destroying half of the total data. Of course it is going to have difficulties running it then! But how to we fix this?

All we need to do is make sure the AI can see all the pixels. The ‘SD Upscale’ extension allows us to use each pixel of the base image as a reference rather than requiring additional memory.

To install it, go to Extensions, Installed, and find ‘Ultimate-Upscale-for-automatic1111‘. Download and restart the server.

Repeat the previous steps. When done, go to Resize to and choose Resize by. Set it to 1. Then go to the bottom and choose Script and select ‘Ultimate SD Upscale’. Finally, click Generate. This process may take longer, so give it some time. After approximately one minute, I got the following image back.

Improved version of the image

After a few seconds of image generation, I get the following image back. Just as the doctor ordered. Experimenting with different styles and controlnets can create near endless possibilities for artistic expression. Don’t hesitate to try it out!

Use cases of this AI workflow

Here are some ideas of what you could do with this AI workflow to create a unique experience for yourself or your clients.

1. Personalized photos: You can use this AI workflow to create unique and memorable images of yourself or loved ones by incorporating various artistic styles into your portrait. A company I work with does wonderful work with this (

2. Marketing materials: Whether it’s an advertisement, brochure, or website design, incorporating distinctive visual elements can help make your brand stand out from the competition.

3. Artistic expression: This technology allows artists to explore new creative avenues by combining their own artwork with that of others in unexpected ways.

4. Education: Students can learn about different art movements and eras through hands-on experimentation with this AI workflow.

5. Historical preservation: By digitizing old photographs and paintings using this technology, we can ensure that these valuable pieces of history don’t get lost over time.

These are just a few examples of how this AI workflow can be applied across different industries and disciplines. As with any emerging technology, there will undoubtedly be even more innovative uses as developers continue to push the boundaries of what’s possible.

If you are interested in the possibilities of image generation for your company, feel free to contact me for a quick demo!

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