Dasturchilar uchun Google tomonidan Code Jam onlayn musobaqasi. Tanlov g'oliblariga pul mukofotlari topshiriladi Talablar — Tanlovda 18 yoshdan katta bo'lgan dasturchilik sohasiga qiziquvchi yoshlar qatnashishlari mumkin; — Dasturchilarning Google accountlarida o'z ism-shariflari, telefon nomerlari va qaysi davlatda yashashlari aniq va batafsil keltirib o'tishlari so'raladi; — Dastur ishchi tili ingliz tili ekanligi uchun shu tildan xabardor bo'lishi kerak (sertifikat shartmas). Foydali tomonlari — 1-raunddan 2-raundga o'tgan eng yaxshi 1000 ta dasturchi ichiga kirgan nomzodlarga Code Jam futbolkalari beriladi; — Code Jam musobaqasida oxirgi 5-bosqichiga yetib kelgan ishtirokchilar quyidagi miqdordagi pul mukofotlari bilan taqdirlanadilar: — 1-o'rin - $15 000; — 2-oʻrin — $2000; — 3-oʻrin — $1000; — 4-25-oʻrin — $100. Oxirgi muddat 03.04.2022 23:59 Batafsil https://grantgo.uz/go/56580 #tanlovlar#mukofot#AQSh
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검색: #image2image
@phygitalcreative · Post #3136 · 2023. 06. 26. AM 01:04
А вот подвезли официальный код DragGAN. Интересно насколько его работа отличается от неофициальной имплементации. В основе StyleGAN3 и StyleGAN-Human. Код #image2image
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@phygitalcreative · Post #3158 · 2023. 06. 29. PM 01:26
Mixed Image Editing Playground AI выкатили редактор изображений с большинством последних достижений в этой области. #image2image#imageediting
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@githubtrending · Post #14988 · 2025. 07. 23. AM 12:00
#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