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Изходен канал @clockstackwheels · Post #963 · 1.03

Мысли за февраль поменьше, чем обычно, потому что месяц какой-то сонный. О защите ИИ от людей, о политизировавшихся музыкантах, снова о генеративных сетках и о правильности изобретения велосипедов. https://telegra.ph/Mysli-za-fevral-03-01 #thoughts

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@githubtrending · Post #14988 · 23.07.2025 г., 00:00

#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