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A latent text-to-image diffusion model
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
This repository contains the source code for the paper First Order Motion Model for Image Animation
Companion webpage to the book "Mathematics For Machine Learning"
High-Resolution Image Synthesis with Latent Diffusion Models
Code release for NeRF (Neural Radiance Fields)
Best Practices, code samples, and documentation for Computer Vision.
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
Image restoration with neural networks but without learning.
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
Python code for "Probabilistic Machine learning" book by Kevin Murphy
A unified framework for 3D content generation.
Taming Transformers for High-Resolution Image Synthesis
"Probabilistic Machine Learning" - a book series by Kevin Murphy
CoTracker is a model for tracking any point (pixel) on a video.
[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
Keras Implementation of Neural Style Transfer from the paper "A Neural Algorithm of Artistic Style" (http://arxiv.org/abs/1508.06576) in Keras 2.0+
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
Pytorch0.4.1 codes for InsightFace
Discovering Interpretable GAN Controls [NeurIPS 2020]
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
[ICCV 2023 Oral] "FateZero: Fusing Attentions for Zero-shot Text-based Video Editing"