Runpod has raised $20MM in a funding round led by Intel Capital and Dell Technologies Capital, fueling our mission to power AI/ML cloud computing and strengthen our team.

SECourses breaks down how to use LoRA with Kohya on Runpod in a beginner-friendly tutorial. Learn how to apply lightweight LoRA files to existing models for powerful generative art results—no full model retraining required.

DreamBooth can generate amazing, highly personalized images—but only if you train it well. In this post, Zhen walks through best practices for getting the most out of DreamBooth on Runpod. Learn what datasets to use, when to use regularization, how many steps are ideal, and which hyperparameters to tweak.

DreamBooth tends to overfit and produce weird artifacts—like extra heads or multiple faces—especially with only a few training images. One trick to improve output quality is adding offset noise during training. This short guide explains what offset noise is, why it helps, and how to apply it to get sharper, more realistic results from your DreamBooth models.

Learn how to create your own scalable serverless endpoint using AUTOMATIC1111 and a custom model. This step-by-step guide walks you through customizing the worker repo, modifying the Dockerfile, and configuring your serverless API deployment—from local build to Docker Hub push.

This post walks you through launching Invoke AI on Runpod using an easy-deploy template. If you don’t have a powerful local GPU—or don’t want to deal with dependency headaches—you can use this guide to spin up a cloud-hosted version of Invoke’s infinite canvas UI with just a few clicks.

This post shows how to deploy a full Stable Diffusion UI using a community-created template on Runpod. With just a few clicks, you can generate high-quality images through a browser interface—no setup or coding required.