Check out our guide on creating a full video synthesis pipeline with audio and speech using VideoFusion, YourTTS, Riffusion, and MoviePy.
In this article, we take a look at GLIGEN, one of the latest techniques for controlling the outputs of txt2img models like Stable Diffusion, and show how to run the model in a Gradient Notebook
In this tutorial, we look at the LLaMA model from Meta AI, and show how to implement it in a Gradient Notebook with lightning fast access to the models using the Public Dataset.
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 looked at the novel VALL-E TTS model, and showed how to train it within a Gradient Notebook using Libri Light and our own voice recordings.
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.
Learn how to customize your diffusion model images with multiple concepts!
