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Source channel @githubtrending · Post #15007 · Jul 29

#c_lang You can find detailed guides for Linux kernel developers and users in the Documentation/ folder, which includes files in formats like HTML and PDF. To build these documents yourself, use commands like `make htmldocs` or `make pdfdocs`. The documentation covers important topics such as kernel building, running requirements, and upgrade issues. You can also view the latest formatted docs online. Additionally, the kernel source uses a special comment style called kernel-doc to embed documentation directly in the code, making it easier to understand functions and structures. This helps you learn, build, and maintain the Linux kernel more effectively. https://github.com/raspberrypi/linux

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@githubtrending · Post #14988 · 07/23/2025, 12:00 AM

#python#deep_learning#diffusion#flax#flux#hacktoberfest#image_generation#image2image#image2video#jax#latent_diffusion_models#pytorch#score_based_generative_modeling#stable_diffusion#stable_diffusion_diffusers#text2image#text2video#video2video The Hugging Face Diffusers library is a powerful and easy-to-use tool for generating images, audio, and 3D molecular structures using advanced diffusion models. It offers ready-to-use pretrained models and flexible components like pipelines, schedulers, and model building blocks, allowing you to quickly create or customize your own diffusion-based projects. Installation is simple via pip or conda, and you can generate high-quality outputs with just a few lines of code. This library benefits you by making cutting-edge AI generation accessible, customizable, and efficient, whether you want to run models or train your own[1][2][5]. https://github.com/huggingface/diffusers