Part 2 of our series examining techniques for adding control, guidance, and variety to your stable diffusion pipeline.
In this article, we examine four new techniques for brining greater control to your Stable Diffusion pipeline: T2IAdapter, Instruct Pix2Pix, Attend and Excite, and MultiDiffusion
n this article, we looked in depth at ControlNet, a new technique for imparting high levels of control over the shape of synthesized images, and demonstrate how to run it in a Gradient Notebook
In this article, we explored a broad overview of epistemic uncertainty in deep learning classifiers, and develop intuition about how an ensemble of models can be used to detect its presence for a particular image instance.
In this article, we took a brief look at uncertainties in deep learning. Thereafter, we took a more keen look at aleatoric uncertainty and how convolutional autoencoder can help to screen out-of-sample images for classification tasks.
In this blog post, we examine what's new in Ultralytics awesome new model, YOLOv8, take a peak under the hood at the changes to the architecture compared to YOLOv5, and then demo the new model's Python API functionality by testing it to detect on our Basketball dataset.
In this article, we saw how to use various tools to maximize GPU utilization by finding the right batch size for model training in Gradient Notebooks.
In this article, we examine two features of convolutional neural networks, translation equivariance and invariance.
Learn how to customize your diffusion model images with multiple concepts!
