In this blogpost, we examined the architecture and capabilities of the Versatile Diffusion framework. We then demonstrated this model within a Gradient Notebook to perform txt2img, img2txt, image variation, text variation, dual-guided, and Latent Image to Text to Image synthesis.
Follow this guide to learn how to deploy your model with FastAPI using a Gradient Deployment. Readers should expect to learn how to upload their trained model as a Gradient model artifact, create a Docker image that will serve their model, and deploy their image on Gradient using a deployment.
In this article, we examine the processes of implementing training, undergoing validation, and obtaining accuracy metrics - theoretically explained at a high level. We then demonstrate them by combining all three processes in a class, and using them to train a convolutional neural network.
In this article, we discuss the process of conducting end-to-end data science on Gradient with Nvidia Merlin. This includes walkthroughs on 3 examples: Multi-stage recommenders, training and serving a MovieLens model, and scaling for the massive Criteo dataset.
In this article, we explore what dimensions imply in a convolutional neural network context. We show how to create a custom convnet as a baseline model, and then proceed to create new versions of it, with increased width, increased depth and the last one with both increased depth and width.
This tutorial provides step-by-step instructions on how to handle class imbalances in image datasets for training computer vision models.
This tutorial shows in detail how to train Textual Inversion for Stable Diffusion in a Gradient Notebook, and use it to generate samples that accurately represent the features of the training images using control over the prompt.
In this article, we took a look at data augmentation as an upsampling technique for handing class imbalance by looking at 5 sample methods. Thereafter, we augment a dataset and train it on a convnet using said dataset show how it improved accuracy and recall scores.
In this article, we walked through each of the steps for creating a Dreambooth concept from scratch within a Gradient Notebook, generated novel images from inputted prompts, and showed how to export the concept as a model checkpoint.
