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Luke Wood: Software Generalist & Machine Learning Specialist
Teach StableDiffusion new concepts via Textual Inversion
Learning new visual concepts with KerasCV's StableDiffusion implementation.
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KerasCV
Author, #1 contributor, and lead of KerasCV. KerasCV is the official Keras repository of computer vision extensions to the Keras API (layers, metrics, losses, models, data-augmentation) that applied computer vision engineers can leverage to quickly assemble production-grade, state-of-the-art training and inference pipelines. KerasCV has over 32 contributors, and four full time team members.
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A walk through latent space with Stable Diffusion
Explore the latent manifold of Stable Diffusion using latent space walking. By Ian Stenbit, Francois Chollet, Luke Wood
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Train an Object Detection Model on Pascal VOC 2007 using KerasCV
Use KerasCV to train a RetinaNet on Pascal VOC 2007.
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wizardwars.online
wizardwars.online is a real-time multiplayer web game written entirely in TypeScript. Some highlights are the custom game engine, use of the Colyseus framework for authoritative multiplayer games, distributed physics computation and prediction, and a full React based rendering system.
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bulletz.io
bulletz.io is an open source real time multiplayer 2D shooter. To celebrate bulletz.io reaching 10k unique users, I have open sourced the codebase. Check it out on GitHub!
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Understanding and applying latent diffusion models
Conference presentation at Devoxx, This talk walks through the architecture and theory enabling these models to generate novel yet coherent images, explores some more advanced uses of text to image models, and lastly shows you how to get started generating images using KerasCV, the most optimized implementation of StableDiffusion available to date.
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Efficient Graph-Friendly COCO Metric Computation for Train-Time Model Evaluation
Pre-print publication written alongside Francois Chollet on a novel algorithm to closely approximate Mean Average Precision within the constraints of the TensorFlow graph. The algorithm used in the publication is used in the KerasCV COCO metric implementation, and can be used to perform train time evaluation with any KerasCV object detection model.
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Parametric Spectral Filters
Primary author of 2021 ICASSP Publication: Parametric Spectral Filters for Fast Converging, Scalable Convolutional Neural Networks. Please see GitHub repo for full information.
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