PHYGITAL+CREATIVE
@phygitalcreative · Post #3136 · 26/06/2023 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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Chaîne source @OnePlusGuide · Post #2409 · 6 avr.
~ NUOVI DETTAGLI SULLE ONEPLUS BULLET WIRELESS Z ~ #OP Il 14 aprile ci sarà la presentazione dei nuovi OnePlus 8. Molti rumors suggeriscono che ci saranno anche le prime cuffie true wireless di OnePlus, le Bullet Wireless Z. Emergono nuovi dettagli sulla batteria, che avrà una durata di 20 ore. Le cuffie avranno la Warp Charge, garantendo 10 ore con 10 minuti di ricarica. Inoltre, avranno un ritardo inferiore alle AirPods di Apple (110ms contro 144ms) e saranno certificate IP55 contro acqua e polvere Pierre
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PHYGITAL+CREATIVE
@phygitalcreative · Post #3158 · 29/06/2023 13:26
Mixed Image Editing Playground AI выкатили редактор изображений с большинством последних достижений в этой области. #image2image#imageediting
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@githubtrending · Post #14988 · 23/07/2025 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