How to train your own LoRa network! beginners step by step guide with KOHYA / Stable Diffusion

First off, this tutorial is based on designers trial and error best practice knowledge using Kohya, basic knowledge and several Youtube tutorials. As the Kohya training toolset has quiet a lot of parameters, we stick to presets in hope for the documentation to become more user friendly in the future.

#1 installation and setup

To train your own LoRa network, you need a training environment. This is provided by Kohya_ss by bmaltais as open source and free! Follow the install instructions on his/her github page:

#2 collect images

To train your LoRa, you need training data. We use simple 512x512px images for simplicity purpose. The more precise, sharp, well contrasted images you use, the better. Quality before quantity.

example training images with fluffy carpets

#3 captioning your images

To get use from your images as training data, your images need to be annotated. We need to generate descriptions of what is shown on the image, so your LoRa model can be trained according to the diffusion process needs.

blip captioning basic setup

If you like to introduce a trigger word for your model, just add a suitable word in the prefix input field.
console output after blip captioning

your image data folder should have your images along text captions text files

#4 dataset preparation

The Kohya training engine needs a specific folder structure to manage the data. The engine has the feature to prepare this for you. Go to the LORA Tab ( NOT DREAMBOOTH !!! ) and scroll down for „Dataset Preparation“.

dataset preparation and training start

Click the button to prepare your dataset / folders. Then copy into respective fields.

#5 initiate training

After preparation is done, you can start the test training itself.

automatic download of tools before first training

Note, that before you will train with your data, some additional models and tools need to be downloaded. This might take some time! check your console for this.
default training progress console output

Your console will show the current training progress. Depending on your dataset size and your GPU, this can vary between some minutes and some hours!

#6 check the output

After the training is done, there comes the fun part – checking your freshly trained LoRa! Just place the *.safetensors file, you will find in your output folder and place it in your LoRa folder of you prefered generator UI ( A1111/ComfyUI/… ). You might use your magic word in your prompt and play with different LoRa weights to see how it affects the default diffusion process.

PROMPT: fluffx style structure , bio inspired complex engine , white round cables and wires, floral – weight: 1.5
fluffx style structure , bio inspired body suit armour, big hair strands , white round cables and wires, floral, crop top – weight: 1.7
fluffx style structure , bio inspired indoor architecture made from white hair strands – weight: 1.7
simple LoRa test workflow in ComfyUI


#7 vary, iterate, trial and error

Now the mysterious part. You have quiet a complex set of design factors to train your LoRa. Depending on your needs, you need to decide for your own, what suits best. As rules of thumb, you can go with these tips in mind:

  • Use quality training data. Good quality images of the things you want to introduce the neural net.
  • Start with simple concepts first. Whether a specific drawing style, color palette or composition style. Setup a homogenic dataset first to see what it induces in the diffusion process and repeat!

  • helpful sources

    Great simple guide how to install Kohya on your machine!

    Good guide how to use Kohya to train your model!

    (SPOILER: Check the structure of your training folder! )

    Very annoying video feed, but very good work done!

    other training experiments

    minimalistic dataset made from handdrawn abstract drawings

    You can try it on your own if you download the pretty small LORA: trx_one

    (hyper detailed breaking ocean waves, gusty sea :1.2) , trx_one thin curved black ink strokes, (white plain paper background:1.2), high contrast, sharp, diffuse bright lighting, (blur, blurry:1.1), dots


    training with two colored pen drawing patterns

    hand drawn minimal training set with two colored pen drawings
    drawing a complex motif – pen drawing – with strong LORA influence
    generating a complex embroidery with mid LORA impact

    training with hand drawn color panels


    handdrawn color panels as training dataset
    using the painterly LORA to guide the img2img overworking
    using the painterly LORA to guide different forms like textile cables
    using the LORA to guide form finding process – here to set cables in a composition
    using the LORA to guide form finding process – here to set textile cables in a composition

    training with photo panels

    This distinct LoRa model is trained with photographs, that are experimental close up shots of sheep and goats with well composed formal compositions. This creates distinct furry textures and interesting, non stereotypical renderings, when mixed in the diffusion.

    some picks from the mea LoRa training data
    mixing the mae LoRa half way to the diffusion creates interessting deformations