Experimental nodes for Machine Learning Operators in Houdini distributed under a Limited Commercial (Indie) license.
Machine Learning Operators (MLOPs) is a collection of nodes that empower Houdini with the capabilities of machine learning. By establishing a framework for working with image data at the SOP (Points) context and streamlining the integration of Python dependencies, MLOPs opens up exciting possibilities. While earlier versions of MLOPs primarily focused on working with Stable Diffusion models, I took it upon myself to enhance its functionality by introducing new nodes that cater to the diverse needs of artists when constructing MLOPs-driven pipelines.
These additional utility nodes, designed to seamlessly integrate within the MLOPs ecosystem, have garnered the attention of MLOPs developers. Many of them are slated for inclusion in the comprehensive commercial release, while others have already found their place in it. Nevertheless, I believe in the power of sharing knowledge and empowering the community. As a result, I have chosen to make these nodes available to the public, accessible through my GitHub repository. My hope is that they will prove valuable to those who wish to explore the possibilities of MLOps and elevate their artistic endeavors.
On the dedicated GitHub page: https://github.com/Faitel/MLOPsLC you'll find a comprehensive repository with all assets, documentation, installation guide, node's descriptions and other useful information.
Here are just a few examples of the nodes available on Github: