Runpod Blog.

Learn how to use Runpod’s DreamBooth API to automate training and image generation. This guide covers preparing training data, sending requests via Postman, checking job status, and retrieving outputs, with tips for customizing models and prompts.

This guide explains how to launch a Runpod instance using the "Runpod Fast Stable Diffusion" template and train Dreambooth models using the included Jupyter notebooks. The post walks users through deploying the pod, connecting to JupyterLab, preparing instance images, setting training parameters, and running the Dreambooth training workflow. It also covers optional steps such as captioning, adding concept images, testing the trained model using Automatic1111, and uploading to Hugging Face.

Learn how to deploy Basaran, an open-source text generation API, on Runpod using a prebuilt template. Customize the model via environment variables and interact through the web UI or API.

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.

Want to code remotely like it’s local? This guide walks you through connecting VSCode to your Runpod instance using SSH for fast, seamless GPU development.

Learn how to deploy Stable Diffusion quickly on Runpod using the fast template, including GPU selection, configuration options, and inference steps. This guide walks you through launching your pod, accessing the WebUI, and generating your first images.

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.