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Nov 22, 2021 · A picture worth a thousand words now takes just three or four words to create, thanks to GauGAN2, the latest version of NVIDIA Research’s wildly popular AI painting demo. The deep learning model behind GauGAN allows anyone to channel their imagination into photorealistic masterpieces — and it’s easier than ever.
Mar 18, 2019 · A deep learning model developed by NVIDIA Research uses GANs to turn segmentation maps into lifelike images with breathtaking ease.
NVIDIA Canvas lets you customize your image so that it’s exactly what you need. Modify the look and feel of your painting with nine styles in Standard Mode, eight styles in Panorama Mode, and different materials ranging from sky and mountains to river and stone.
Experience AI Models On the Fly. The AI Playground on NGC™ offers an easy-to-use interface that allows you to quickly try generative AI models such as NeVA, SDXL, Llama 2, and CLIP directly from your browser. Learn More. Generative AI.
Mar 1, 2022 · GauGAN, an AI demo for photorealistic image generation, allows anyone to create stunning landscapes using generative adversarial networks. Named after post-Impressionist painter Paul Gauguin, it was created by NVIDIA Research and can be experienced free through NVIDIA AI Demos.
The NVIDIA Canvas update released today, powered by the GauGAN2 AI model and NVIDIA RTX GPU Tensor Cores, generates backgrounds with increased quality and 4x higher resolution, and adds five new materials to paint with.
Nov 22, 2021 · NVIDIA has announced the latest version of NVIDIA Research's AI painting demo, GauGAN2. The model is powered by deep learning and now features a text-to-image feature.
Nov 23, 2021 · NVIDIA’s launch of the GauGAN2 web app is a game-changer for artists. Now, creating stunning images from simple phrases is a breeze. This innovative tool opens up endless possibilities for creativity.
A deep learning model that turns a simple written phrase into a photorealistic masterpiece. It combines multiple modalities such as text, semantic segme...
GauGAN: Semantic Image Synthesis With Spatially Adaptive Normalization Taesung Park, Chris Hebert, Ting-Chun Wang, Jun-Yan Zhu, Gavriil Klimov, Ming-Yu Liu
