Search Results for "deep-live-cam"
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Showing 9 open source projects for "deep-live-cam"
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FLUX.1 Krea
Powerful open source image generation model
...The repository offers easy-to-use inference scripts and a Jupyter Notebook example to facilitate quick experimentation and adoption. Users can run the model locally after downloading weights from Hugging Face and benefit from a live demo available on krea.ai. -
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CogView
Text-to-Image generation. The repo for NeurIPS 2021 paper
...With 4 billion parameters, it was one of the earliest transformer-based models to successfully generate high-quality images from natural language descriptions in Chinese, with partial support for English via translation. The model incorporates innovations such as PB-relax and Sandwich-LN to enable stable training of very deep transformers without NaN loss issues. CogView supports multiple tasks beyond text-to-image, including image captioning, post-selection (ranking candidate images by relevance to a prompt), and super-resolution (upscaling model-generated images). The repository provides pretrained models, inference scripts, and training examples, along with a Docker environment for reproducibility. -
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texturize
Generate photo-realistic textures based on source images
...A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. ... -
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Deep Feature Rotation Multimodal Image
Implementation of Deep Feature Rotation for Multimodal Image
Official implementation of paper Deep Feature Rotation for Multimodal Image Style Transfer [NICS'21] We propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while still achieving effective stylization compared to more complex methods in style transfer. Our approach is a representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to... -
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PaddleGAN
PaddlePaddle GAN library, including lots of interesting applications
...GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. It's the one research area in deep learning that AI researchers are most concerned about. -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread circulation. ... -
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Deep Exemplar-based Video Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization". End-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. ... -
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