LoRA (Low-Rank Adaptation) is an add-on commonly used by AI Arts users to refine artworks generated from Stable Diffusion Model Checkpoints according to their preferences.
This small additional model can apply impressive changes to standard Model CheckPoints with relatively good quality, depending on its processing.
LoRA models typically have a capacity between 10 – 200 MB, which is significantly smaller than checkpoint files that exceed 1 GB.
However, it's important to note that various factors contribute to the varying sizes of LoRA, such as its settings/parameters. Nonetheless, LoRA generally has a smaller capacity than Model Checkpoints.
Given its relatively small size, LoRA makes it easier for users to upload, download, and combine it with the checkpoint models they use, enriching the AI Arts results with backgrounds, characters, and styles according to the user's desires.
It's important to note that LoRA cannot be used independently. It requires a checkpoint model to be functional. So, how do you create a LoRA? What do you need to prepare to make a LoRA?
To create a LoRA, you need to prepare the following:
Datasets Before creating a LoRA, ensure you have datasets that will be trained/used to create the LoRA. If you don't have one, you must prepare it first.
There is no set rule for the quantity. Too many datasets don't necessarily yield a good LoRA, and too few may not either. Ensure to prepare high-quality datasets with various angles, poses, expressions, positions, and others. Align it with your goal in creating the LoRA, whether it's for style, creating fictional/realistic characters, or other purposes.
Often, users process their datasets first before training them to become LoRA. This process includes cropping to ensure all datasets are the same size and adjusting the image resolution for better quality and clarity. If the datasets are blurry, the LoRA might produce poor and blurry quality.
Understanding LoRA Parameters and Settings Understanding LoRA parameters and settings is not easy, but you can learn it gradually over time if you are determined to study it. This is necessary knowledge that you must have.
There might not be many articles in Indonesian discussing LoRA, so to learn more, you may need to dive deeper into various English or other language sites that cover the topic, join the community, and learn what they are learning too.
A commonly used formula for LoRA is "Datasets x num_repeats x epoch / train_batch_size = steps", which calculates the number of steps for LoRA and sets the steps for each epoch.
Example: Datasets (40 images) x 10 (num_repeats) x 10 epochs / 4 (train batch size) results in 1000 steps for the 10th epoch of LoRA. Steps for the first epoch are 100 steps, the second epoch: 200 steps, and so on until the 10th epoch, which has 1000 steps.
This formula is not the only thing you need to know. You also need to learn about network_dim, network_alpha, learning_rate, and many others.
PC/Colab The most crucial and primary thing you need to have is a PC with Stable Diffusion or Kohya Trainer installed locally. However, this requires a high-spec computer, including a powerful processor, RAM, high VRAM GPU, and others. If you don't have one, an alternative is to train LoRA using Google Colab.
Luckily, now you can easily train Lora in tensor.art, by logging in to your account, clicking on profile then selecting train Lora, or you can click here to go directly to the train lora page
Local/Cloud Storage Media Make sure you have storage media to save your datasets and LoRA. You can upload these datasets and LoRA to popular cloud storage services like Google Drive, Mega, MediaFire, etc., so if your files are lost from your computer, you still have a backup.
You can also upload them to platforms like HuggingFace, Civitai, and others. This will make it easier for you to use them through Google Colab.
