CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

Related tags

Deep Learningpytorchcliptext-to-imagetext2image
Overview

CLIP-GEN

[简体中文][English]

本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。

clip-gen

CLIP-GEN 是一个 Language-Free 的文本生成图像的方法，它不依赖图文训练样本，通过预训练 CLIP 模型的强大表征能力，只需要图片数据就可以训练出一个文本生成图像的模型。该方法的基本原理是：CLIP-GEN 首先会训练一个 VQ-GAN，把图片映射到离散空间；然后再训练一个 GPT 模型，把 CLIP embedding 映射到 VQ-GAN 的离散空间；由于在 CLIP 中，文本和图像共享一个特征空间，在 inference 的时候我们就可以通过同样的方法把文本映射到 VQ-GAN 的离散空间，然后 decode 为 RGB 图像。

Requirements

  • hfai (to be released soon)
  • torch>=1.8

Training

支持的数据集：coco, imagenet, googlecc

  1. 下载 CLIP 预训练模型

    下载 CLIP 后放至 pretrained/clip_vit_b32.pt，该预训练模型来自 OpenAI.

  2. 在 COCO 上训练 VQGAN

    提交任务至萤火集群：

    hfai python train_vqgan.py --ds coco -- -n 1 -p 30

    本地运行：

    python train_vqgan.py --ds coco

  3. 在 COCO 上训练 Conditional GPT

    提交任务至萤火集群：

    hfai python train_gpt.py --ds coco --vqgan_ckpt /path/to/vqgan/ckpt -- -n 4 -p 30

    本地运行：

    python train_gpt.py --ds coco --vqgan_ckpt /path/to/vqgan/ckpt

Demo

下载在 COCO 上训练好的 VQGAN 和 GPT 模型，分别放到 pretrained/vqgan_coco.pt 和 pretrained/gpt_coco.pt；然后运行：

python demo.py --text "A city bus driving on the city street" --out "bus.jpg"

NOTE: demo 的运行不依赖 hfai，用户可以在装有 PyTorch 的环境下直接使用

Samples

下面是一些文本生成图像的样本：

tower bus living train skiing

References

Citation

@article{wang2022clip,
  title={CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP},
  author={Wang, Zihao and Liu, Wei and He, Qian and Wu, Xinglong and Yi, Zili},
  journal={arXiv preprint arXiv:2203.00386},
  year={2022}
}

TODO

  • 预训练模型
  • FFRecord 数据
You might also like...
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized

VQGAN-CLIP-Docker About Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized This is a stripped and minimal dependency repository for running loca

Kevin Costa 73 Sep 11, 2022
A Jupyter notebook to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

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

Eugenio Herrera 175 Dec 29, 2022
Text2Art is an AI art generator powered with VQGAN + CLIP and CLIPDrawer models
Text2Art is an AI art generator powered with VQGAN + CLIP and CLIPDrawer models

Text2Art is an AI art generator powered with VQGAN + CLIP and CLIPDrawer models. You can easily generate all kind of art from drawing, painting, sketch, or even a specific artist style just using a text input. You can also specify the dimensions of the image. The process can take 3-20 mins and the results will be emailed to you.

Muhammad Fathy Rashad 643 Dec 30, 2022
A 1.3B text-to-image generation model trained on 14 million image-text pairs
A 1.3B text-to-image generation model trained on 14 million image-text pairs

minDALL-E on Conceptual Captions minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for no

Kakao Brain 604 Dec 14, 2022
A PyTorch Lightning solution to training OpenAI's CLIP from scratch.
A PyTorch Lightning solution to training OpenAI's CLIP from scratch.

train-CLIP 📎 A PyTorch Lightning solution to training CLIP from scratch. Goal ⚽ Our aim is to create an easy to use Lightning implementation of OpenA

Cade Gordon 396 Dec 30, 2022
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

ZJU-VIPA 47 Jan 9, 2023
Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus
Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus

free-duolingo-plus duolingo account creator that uses your invite code to get yo

null 1 Jan 6, 2022
Official implementation of SynthTIGER (Synthetic Text Image GEneratoR) ICDAR 2021
Official implementation of SynthTIGER (Synthetic Text Image GEneratoR) ICDAR 2021

🐯 SynthTIGER: Synthetic Text Image GEneratoR Official implementation of SynthTIGER | Paper | Datasets Moonbin Yim1, Yoonsik Kim1, Han-cheol Cho1, Sun

Clova AI Research 256 Jan 5, 2023
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

SuperGen The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. Requirements Before running, you

Yu Meng 38 Dec 12, 2022
Comments

  • "nn.TransformerEncoderLayer" is adopted to construct the "conditonal transformer" in your paper.

    Thanks for your great work.

    I noticed that you utilize "nn.TransformerEncoderLayer" when constructing "conditional transformer". Since it is used to predict the next token index, I am wondering whether the decoder of transformer is more appropriate for the construction of your conditional transformer? or what's the reason that you don't adopt "nn.TransformerdecoderLayer" ?

    Because of the structure of "nn.TransformerEncoderLayer" is simpler or more concise than that of "nn.TransformerDEcoderLayer" ?

    opened by fido20160817 0
  • Add Web Demo & Docker environment

    Add Web Demo & Docker environment

    This pull request makes it possible to run your model inside a Docker environment, which makes it easier for other people to run it. We're using an open source tool called Cog to make this process easier.

    This also means we can make a web page where other people can try out your model, view it here: https://replicate.com/hfailab/clip-gen. You can find the docker file under the tab ‘run model with docker’.

    We have added some examples to the page, but do claim the page so you can own the page, customise the Example gallery as you like, push any future update to the web demo, and we'll feature it on our website and tweet about it too. You can find the 'Claim this model' button on the top of the page. Any member of the HFAiLab organization on GitHub can claim the model ~ When the page is claimed, it will be automatically linked to the arXiv website as well (under “Demos”).

    In case you're wondering who I am, I'm from Replicate, where we're trying to make machine learning reproducible. We got frustrated that we couldn't run all the really interesting ML work being done. So, we're going round implementing models we like. 😊

    opened by chenxwh 0
Owner
GitHub Repository
Pytorch implementation of YOLOX、PPYOLO、PPYOLOv2、FCOS an so on.

简体中文 | English miemiedetection 概述 miemiedetection是女装大佬咩酱基于YOLOX进行二次开发的个人检测库（使用的深度学习框架为pytorch），支持Windows、Linux系统，以女装大佬咩酱的名字命名。miemiedetection是一个不需要安装的

248 Jan 02, 2023
A python program to hack instagram

hackinsta a program to hack instagram Yokoback_(instahack) is the file to open, you need libraries write on import. You run that file in the same fold

2 Jan 22, 2022
This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras)

Yogi-Optimizer_Keras This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras) The NeurIPS-Paper can be found here: http://papers.nips.c

14 Sep 13, 2022
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce

Shen Lab at Texas A&M University 8 Sep 02, 2022
Boostcamp AI Tech 3rd / Basic Paper reading w.r.t Embedding

Boostcamp AI Tech 3rd : Basic Paper Reading w.r.t Embedding TL;DR 1992년부터 2018년도까지 이루어진 word/sentence embedding의 중요한 줄기를 이루는 기초 논문 스터디를 진행하고자 합니다. 논

Soyeon Kim 14 Nov 14, 2022
PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017

pytorch-deep-generative-replay PyTorch implementation of Continual Learning with Deep Generative Replay, NIPS 2017 Results Continual Learning on Permu

Junsoo Ha 127 Dec 14, 2022
Multi agent DDPG algorithm written in Python + Pytorch

Multi agent DDPG algorithm written in Python + Pytorch. It also includes a Jupyter notebook, Tennis.ipynb, as a showcase.

Rogier Wachters 2 Feb 26, 2022
Code and data of the Fine-Grained R2R Dataset proposed in paper Sub-Instruction Aware Vision-and-Language Navigation

Fine-Grained R2R Code and data of the Fine-Grained R2R Dataset proposed in the EMNLP2020 paper Sub-Instruction Aware Vision-and-Language Navigation. C

YicongHong 34 Nov 15, 2022
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022

PyCRE Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed

<a href=[email protected]"> 7 May 06, 2022
NUANCED is a user-centric conversational recommendation dataset that contains 5.1k annotated dialogues and 26k high-quality user turns.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions Overview NUANCED is a user-centric conversational recommen

Facebook Research 18 Dec 28, 2021
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates

Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates Installation Clone the repository: git clone https://github.com/Zengyi-Qi

Zengyi Qin 3 Oct 18, 2022
Source code related to the article submitted to the International Conference on Computational Science ICCS 2022 in London

POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection Source code related to the article submitted to the Internati

Tomasz Szczepański 1 Apr 29, 2022
Current state of supervised and unsupervised depth completion methods

Awesome Depth Completion Table of Contents About Sparse-to-Dense Depth Completion Current State of Depth Completion Unsupervised VOID Benchmark Superv

224 Dec 28, 2022
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis Figure: Shape-Accurate 3D-Aware Image Synthesis. A Shading-Guid

Xingang Pan 115 Dec 18, 2022
C3D is a modified version of BVLC caffe to support 3D ConvNets.

C3D C3D is a modified version of BVLC caffe to support 3D convolution and pooling. The main supporting features include: Training or fine-tuning 3D Co

Meta Archive 1.1k Nov 14, 2022
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
Package for working with hypernetworks in PyTorch.

Package for working with hypernetworks in PyTorch.

Christian Henning 71 Jan 05, 2023
A project for developing transformer-based models for clinical relation extraction

Clinical Relation Extration with Transformers Aim This package is developed for researchers easily to use state-of-the-art transformers models for ext

uf-hobi-informatics-lab 101 Dec 19, 2022
HybridNets: End-to-End Perception Network

HybridNets: End2End Perception Network HybridNets Network Architecture. HybridNets: End-to-End Perception Network by Dat Vu, Bao Ngo, Hung Phan 📧 FPT

Thanh Dat Vu 370 Dec 29, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022