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.

GitHub Repository

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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

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

Controlling the MicriSpotAI robot from scratch

Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

This repo is the code release of EMNLP 2021 conference paper "Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories".

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[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Auditing Black-Box Prediction Models for Data Minimization Compliance

Fast Soft Color Segmentation

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SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

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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

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

Controlling the MicriSpotAI robot from scratch

Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

This repo is the code release of EMNLP 2021 conference paper "Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories".

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Auditing Black-Box Prediction Models for Data Minimization Compliance

Fast Soft Color Segmentation

Benchmarking the robustness of Spatial-Temporal Models

SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Pretty Tensor - Fluent Neural Networks in TensorFlow

Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

Instance-conditional Knowledge Distillation for Object Detection

SmartSim Infrastructure Library.

[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Learning Optical Flow from a Few Matches (CVPR 2021)

Discord bot-CTFD-Thread-Parser - Discord bot CTFD-Thread-Parser

A certifiable defense against adversarial examples by training neural networks to be provably robust