How To Train Stable Diffusion 3 Lora A Step By Step Guide

Home » Technology » How to Train Stable Diffusion LoRA Models: Complete Guide I spent three weeks and $400 in cloud compute costs learning what not to do when training LoRA models. My first attempt resulted in distorted outputs that looked nothing like my training data. The second attempt crashed after 7 hours due to memory issues. But once I understood the fundamentals and fixed my approach, I successfully trained 15 different LoRA models that consistently generate high-quality results. This guide will teach you everything I learned about LoRA training, from hardware requirements to advanced optimization techniques, helping you avoid the costly mistakes that plague 30% of first-time trainers.

Complete guide to training Stable Diffusion LoRAs on AMD GPUs using ROCm 6.2+ in 2025. Step-by-step setup with Kohya, Derrian, and troubleshooting tips. You have an AMD GPU like the RX 7900 XTX or RX 6800 XT and want to train custom LoRAs for Stable Diffusion, but most guides assume NVIDIA hardware with CUDA support. Training on AMD GPUs is absolutely possible in 2025 thanks to ROCm improvements, but the setup process differs significantly from NVIDIA workflows and outdated guides cause frustration. Quick Answer: Training Stable Diffusion LoRAs on AMD GPUs in 2025 requires ROCm 6.2 or newer, Python 3.10, and PyTorch built for ROCm. Use Kohya's sd-scripts or Derrian's LoRA Easy Training Scripts with specific AMD configurations.

Key differences from NVIDIA include using ROCm instead of CUDA, setting HSA_OVERRIDE_GFX_VERSION environment variable for your specific GPU, avoiding xformers which doesn't exist for AMD, and using fp16 or bf16 precision. Training works reliably on RX 6000 and 7000 series cards with 12GB+ VRAM. Training Stable Diffusion LoRAs on AMD hardware requires specific software components and compatible hardware. Understanding these prerequisites prevents frustrating setup failures and helps you determine if your system can handle training. Learning ComfyUI? Join 115 other course members

A killer application of Stable Diffusion is training your own model. Being an open-source software, the community has developed easy-to-use tools for that. Training LoRA models is a smart alternative to checkpoint models. Although it is less powerful than whole-model training methods like Dreambooth or finetuning, LoRA models have the benefit of being small. You can store many of them without filling up your local storage. Why train your own model?

You may have an art style you want to put in Stable Diffusion. Or you want to generate a consistent face in multiple images. Or it’s just fun to learn something new! In this post, you will learn how to train your own LoRA models using a Google Colab notebook. So, you don’t need to own a GPU to do it. This tutorial is for training a LoRA for Stable Diffusion v1.5 models.

See training instructions for SDXL LoRA models. © 2025 BetterWaifu.com. All rights reserved. In this quick tutorial we will show you exactly how to train your very own Stable Diffusion LoRA models in a few short steps, using the Kohya GUI. Not only is this process relatively quick and simple, but it also can be done on most GPUs, with even less than 8 GB of VRAM. Let’s go through each step of the best LoRA training guide you can find online!

Check out also: Kohya LoRA Training Settings Explained The only thing you need to go through with training your own LoRA is the Kohya GUI which is a Gradio based graphical interface that makes it possible to train your own LoRA models... You will also need to install a few dependencies to be able to run Kohya GUI on your system. Can you train LoRA models using just the Stable Diffusion Automatic1111 WebUI? While you could also attempt training LoRA models using only the Stable Diffusion WebUI, our method utilizing Kohya GUI is much simpler, faster and less complicated. How to Train a Lora Model for Stable Diffusion

🌟 Introduction Welcome to a new chapter of our free tutorial on stable diffusion. This tutorial will teach you how to train a Lora model for stable diffusion using your own face or any other person's face. The goal of this tutorial is to make the process as easy as possible. After extensive research and experimentation, we have discovered the simplest and fastest method for creating a Lora model. Please note that the quality, type, and size of the photos used in the training process are crucial for successful results. 🎯 Creating the Data Set The first and most important step in this video is to create our data set or collection of images.

There are specific characteristics that these photos need to have. Firstly, it is important that the photos have good quality as the outcome of our training will heavily depend on it. The higher the quality of the photo, the more data the stable diffusion model can extract. Secondly, the type of photo is also significant. Most of the images used in this tutorial are full-face photographs. It is recommended to include a majority of such photos and some additional photos with half-body or full-body shots.

Lastly, all photos need to be resized to a dimension of 512 by 512 pixels. This can be done by using a website called "virme" and renaming the files accordingly. 🔧 Preparing the Data Set To prepare the data set, we will be using Notebook 1, also known as the Data Set Preparer. In this notebook, we will execute specific cells to perform the necessary tasks. Before executing any cells, make sure to enter the name of your model, which should match the name given to your photos. It is important to note that this process requires a Google Drive account, so make sure you are connected to it.

Once the execution is complete, your project folder will be created in your Google Drive account. 💻 Notebook 2: Training the Lora Model Once the data set is prepared, it's time to move on to Notebook 2, where we will train the Lora model. Similarly to Notebook 1, enter the name of your project in this notebook as well. Additionally, we need to choose the model we want to train our project on. Civitai offers several models for stable diffusion, so choose one that catches your attention. We recommend selecting a realistic model like "Anal of Diffusion".

Download the model and copy the link. Paste the link in the designated area of Notebook 2. Finally, click on the "Play" button to initiate the training process. Stable Diffusion LoRA training represents one of the most powerful techniques for customizing AI image generation. This comprehensive guide will take you from beginner to expert, covering everything from basic concepts to advanced optimization strategies. Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning technique that allows you to adapt large pre-trained models like Stable Diffusion without modifying the original weights.

Instead of training billions of parameters, LoRA introduces small adapter modules that capture the specific adaptations needed for your use case. Before diving into LoRA training, you need to prepare your environment properly. This includes selecting appropriate hardware, installing necessary software, and organizing your dataset. While LoRA training is more efficient than full fine-tuning, it still requires substantial computational resources: The most popular training framework is Kohya's sd-scripts, which provides a comprehensive suite of tools for LoRA training. Here's how to set it up:

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