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A year and a half ago, I embarked on an open-source project that has since grown and evolved significantly. Inspired by the AUTOMATIC1111 project, which was just starting to gain traction at the time, I kept adding new features and capabilities. Today, my project integrates over 50 different neural networks, each handling a unique task. In this article, I want to share some practical tips and key takeaways from my journey. I hope they prove helpful to you and motivate you to refactor your code.
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CodeRabbit
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wunjo.wladradchenko.ru
Wunjo CE: Face Swap, Lip Sync, Control Remove Objects & Text & Background, Restyling, Audio Separator, Clone Voice, Video Generation. Open Source, Local & Free.
My open-source project focuses on creating and editing video, images, and audio using neural networks. Often, different methods can achieve similar outcomes, but ensuring consistency across the project has been a major challenge. As I integrated open-source solutions, optimized them, and added new functionality, maintaining a unified approach became essential. For instance, features like face swapping, lip synchronization, and portrait animation all require facial recognition. Rather than using separate methods for each, as was common in the original solutions, I opted for a single, shared model for facial recognition. Consequently, the 50+ neural networks are organized such that each one serves a unique purpose without redundancy.
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Check xformers Compatibility Visit the xformers GitHub repo to ensure compatibility with your torch and CUDA versions. Support for older versions can be dropped, so staying updated is vital, especially if you're running CUDA 11.8 and want to leverage xformers for limited VRAM.