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Source channel @githubtrending · Post #14988 · Jul 23

#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

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@tuttoelezioni · Post #1356 · 10/27/2025, 07:15 AM

#Argentina🇦🇷 #Parlamentari Risultati parziali. Camera dei deputati. Sezioni scrutinate: 108.125/108.992 (99,20%). 🟪 La Libertà Avanza (#LLA|Libertaristi di destra): 40,65% (64) 🟦 Forza Patria (#FP|Centrosinistra peronista): 24,31% (31) 🟦 Province Unite (#PU|Centro): 4,96% (5) 🟥 Fronte di Sinistra (#FITU|Trotskisti): 3,71% (3) 🟦 Fronte Prima il Tucumán (#FTP): 2,28% (2) 🟥 Fronte Civico per Santiago (#FCS): 1,23% (2) 🟦 Forza Giustizialista Mendoza (#FJM): 1,10% (1) 🟦 Forza Entre Ríos (#FER): 1,06% (2) 🟪 Proposta federale per il cambiamento (#PFC): 1,05% (0) Altri: 19,65% (17) N.B.: Tra parentesi sono indicati i seggi ottenuti. @TuttoElezioni