@phygitalcreative · Post #3136 · 06/26/2023, 01:04 AM
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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Source channel @githubtrending · Post #14698 · May 12
#typescript This repository offers many practical JavaScript/TypeScript examples for learning AI development, requiring Node.js and Bun runtimes. It includes ready-to-run demos like conversation summarization, web search integration, memory management, and API interactions with services like OpenAI, Langfuse, and Qdrant. You can run these examples locally or via Docker for easy setup. The code covers advanced AI topics such as token counting, prompt engineering, vector databases, and audio/video processing. Using Bun, a fast and TypeScript-friendly runtime compatible with Node.js, enhances performance and development speed. This setup helps you quickly experiment with AI features and build your own AI-powered apps efficiently. https://github.com/i-am-alice/3rd-devs
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@phygitalcreative · Post #3136 · 06/26/2023, 01:04 AM
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 · 06/29/2023, 01:26 PM
Mixed Image Editing Playground AI выкатили редактор изображений с большинством последних достижений в этой области. #image2image#imageediting
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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