@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 #15064 · Aug 16
#csharp#2d#avaloniaui#csharp#dotnet_core#dotnetcore#editor#game_development#graphics#graphics_editor#linux_desktop#painting#pixel_art#pixi#procedural_drawing#procedural_generation#raster_graphics#sprites#tabs#vector_graphics PixiEditor is a free, easy-to-use 2D graphics editor that combines pixel art, painting, and vector tools all in one program. You can create game sprites, animations, logos, and edit images with a simple interface. It supports mixing vector and raster graphics on the same canvas and lets you export to many formats like PNG, SVG, GIF, and MP4. The powerful Node Graph system allows you to create complex, non-destructive effects and animations. It also has a timeline for frame-by-frame animation and autosaves your work to prevent loss. This makes it a versatile tool for artists and game developers. https://github.com/PixiEditor/PixiEditor
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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