Creating Logos with Stable Diffusion: A Step-by-Step Guide

8 min readSep 11, 2023

In this article, we’ll delve into the world of AI-generated logos using Stable Diffusion. We’ll explore a workflow that utilizes ControlNet to create high-quality logos from scratch. This article basics of image generation using Stable Diffusion version 1.5: there are many different control mechanisms that are used to alter the underlying neural network which are well documented and too large in scope to cover here.

Step 1: Installing Stable Diffusion

The first step is to install Stable Diffusion on your machine. You can find detailed instructions on how to do so here: Installation Guide.

Due to the memory and processing intensive nature of diffusion models, the performance and capability of the models require a fairly modern Radeon or NVidia GPU with at least 8 GB of free VRAM and 10–20 GB of free space on a local drive, preferably SSD.

Step 2: Setting Up the Environment

Once you have Stable Diffusion installed, you need to set up your environment. This includes installing Python and setting up a virtual environment. For detailed instructions, refer to the official documentation: Environment Setup.

Step 3: Generating Images

Now that your environment is set up, you can start generating images. To do this, you’ll use the stable-diffusion command-line tool. The basic syntax is as follows:

stable-diffusion generate 
  --seed 42 
  --n_iter 20 
  --n_samples 1 
  --scale 1
  --noise_scale 0.5 
  --text "a lineart style professional logo, a cat sitting under a tree." 
  -- output_file "my_image.png"

Replace “my_image.png” with the name of the file you want to save your image to.

The –seed value is just a random number that is used to create the initial gaussian noise that is used to refine into the final image, changing this value will result in the generation of significantly different outputs.

Step 3.1: Using pre-trained checkpoints other than the default

Stable diffusion is shipped with a generative AI model that is provided as a baseline checkpoint. In this demonstration we use the popular stable diffusion 1.5 checkpoint, however there are many fine-tuned checkpoints that have been refined by the open source community to generate images that are different in style and often times, specific capabilities.

For example, if the base SD 1.5 model does not produce quality images of fabric, a user can fine-tune the model with hundreds or thousands of images of fabrics that are annotated with meta-data, allowing for output images that are able to produce more realistic and more variable fabrics. Or if a specific artist is absent from the original training dataset, a user can provide numerous examples of that artist’s work which will allow the model to generate outputs that are similar in style to that artist.

There are a number of different mechanisms to accomplish this, but two mechanisms are the most popular for altering the baseline neural model and will result in the capability to blend and generate variations that were not found in the training data. These two styles are using pre-trained checkpoints that replace the SD1.5 model, and LORA’s which are loaded as an addition to a pre-trained checkpoint.

It should be noted that the LORA is specific to a checkpoint as it will modify the weights of the base-checkpoint model at a certain point within the generative pipeline. For example, if a LORA was generated using a “Super-resolution 35mm black and white film” checkpoint, it may not function well on the baseline SD1.5 model (but experiment and see what unique and unforeseen outputs result!)

Step 4: Using ControlNet

ControlNet is a tool that allows you to control the generation process by providing a sketch or outline of what you want the AI to generate. To use ControlNet, you’ll need to install it separately. You can find instructions on how to do so here: ControlNet Installation.

Once installed, you can use ControlNet by specifying the — guidance flag when running the stable-diffusion command. For example:

stable-diffusion generate 
  --seed 42 
  --n_iter 20 
  --n_samples 1 
  --scale 1
  --noise_scale 0.5
  --guidance 
  --guidance_image <path_to_my_guide_image/Image.png> 
  --model canny
  --text "a lineart style professional logo, a cat sitting under a tree." 
  --output_file "my_image.png"

If you wish to further refine the style of the generated image, you can utilize a LORA model. To accomplish this, you need to run the stable-diffusion command with the –loras <path_to_LORA_file>

For example, if the LORA was named “LOGO-Style”, this would be the command:

stable-diffusion generate 
  --seed 42 
  --n_iter 20 
  --n_samples 1 
  --scale 1
  --noise_scale 0.5
  --guidance 
  --guidance_image <path_to_my_guide_image/Image.png> 
  --model canny
  --loras <path_to_my_LORA/Logo-style>
  --text "a lineart style professional logo, a cat sitting under a tree." 
  --output_file "my_image.png"

Please note that the values: “<path_to_my_guide_image/Image.png>” and “<path_to_my_LORA/Logo-style>“ should be replaced with the actual path to the files.

Step 5: Fine-Tuning Your Logo

After generating your logo, you may want to fine-tune it to get the exact look you’re going for. This can be done by adjusting the parameters in the stable-diffusion command. For example, increasing the — n_iter value will increase the number of iterations the model uses to generate the image, which can lead to more detailed results.

Step 6: Saving and Exporting Your Logo

Finally, once you’re happy with your logo, you’ll need to save it and export it in a format that can be used for your website or branding materials. Stable Diffusion saves images in PNG format by default, but you can also export them as SVG files for scalable vector graphics by utilizing the –format svg flag:

stable-diffusion generate 
  --seed 42 
  --n_iter 20 
  --n_samples 1 
  --scale 1
  --noise_scale 0.5
  --guidance 
  --guidance_image <path_to_my_guide_image/Image.png> 
  --model canny
  --loras <path_to_my_LORA/Logo-style>
  --text "a lineart style professional logo, a cat sitting under a tree." 
  --output_file "my_image.svg"
  --format svg

And that’s it! With this workflow, you can create unique and professional-looking logos using Stable Diffusion and ControlNet.

Appendix:

ControlNet resources:

HuggingFace checkpoints:

https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main

-For this demonstration download:

https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth

and;

https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.yaml

Github Repo:

https://github.com/lllyasviel/ControlNet-v1-1-nightly

Stable Diffusion Checkpoints:

Good overall SD1.5 fine-tuned checkpoint:

https://civitai.com/models/4823/deliberate

LORA checkpoints for logos:

https://civitai.com/search/models?sortBy=models_v2&query=logo

A large archive of SD models:

https://88stacks.com/models/tags

Just as a note:

There are a LOT of models out there that are specifically designed to generate NSFW content, so be aware of your surroundings when browsing through these archives.

Sample Logos:

Here are a few sample logos that I was able to quickly generate using SD 1.5 and a couple of different LORA fine-tunes. As a pro-tip, If you’re looking for a more complex and stylistic logo, I find that architectural LORA’s produce excellent results.

Depending on your hardware, you can produce thousands of different variations in an hour or two.

Control Net Source images: