A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

Last update: Dec 30, 2022

Overview

StyleGAN3 CLIP-based guidance

StyleGAN3 + CLIP

StyleGAN3 + inversion + CLIP

This repo is a collection of Jupyter notebooks made to easily play with StyleGAN3¹ and CLIP² for a text-based guided image generation.

Both notebooks are heavily based on this notebook, created by nshepperd (thank you!).

Special thanks too to Katherine Crowson for coming up with many improved sampling tricks, as well as some of the code.

Feel free to suggest any changes! If anyone has any idea what license should this repo use, please let me know.

StyleGAN3 was created by NVIDIA. Here is the original repo. ↩
CLIP (Contrastive Language-Image Pre-Training) is a multimodal model made by OpenAI. For more information head over here. ↩

Owner

Eugenio Herrera

Data Scientist, Full-Stack Engineer, and aspiring Researcher.

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A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

Related tags

Overview

StyleGAN3 CLIP-based guidance

StyleGAN3 + CLIP

StyleGAN3 + inversion + CLIP

Owner

Eugenio Herrera

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A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

Related tags

Overview

StyleGAN3 CLIP-based guidance

StyleGAN3 + CLIP

StyleGAN3 + inversion + CLIP

Footnotes

Owner

Eugenio Herrera

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

MQBench Quantization Aware Training with PyTorch

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space"

Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

code for TCL: Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022

Official repository for the CVPR 2021 paper "Learning Feature Aggregation for Deep 3D Morphable Models"

PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

A toolkit for developing and comparing reinforcement learning algorithms.

For IBM Quantum Challenge 2021 (May 20 - 26)

This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

Trajectory Extraction of road users via Traffic Camera

A mini-course offered to Undergrad chemistry students

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Code for "Typilus: Neural Type Hints" PLDI 2020

Retinal Vessel Segmentation with Pixel-wise Adaptive Filters (ISBI 2022)

Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval.

Sibur challange 2021 competition - 6 place