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

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Deep Learning pytorch clip text-to-image text2image
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
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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
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