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https://github.com/omerbt/Text2LIVE

Official Pytorch Implementation for "Text2LIVE: Text-Driven Layered Image and Video Editing" (ECCV 2022 Oral)
https://github.com/omerbt/Text2LIVE

clip eccv2022 generative-model image-editing image-manipulation single-image single-video text-driven-editing text2live video-editing

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Official Pytorch Implementation for "Text2LIVE: Text-Driven Layered Image and Video Editing" (ECCV 2022 Oral)

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# Text2LIVE: Text-Driven Layered Image and Video Editing (ECCV 2022 - Oral)

## [Project Page]



[![arXiv](https://img.shields.io/badge/arXiv-Text2LIVE-b31b1b.svg)](https://arxiv.org/abs/2204.02491)

![Pytorch](https://img.shields.io/badge/PyTorch->=1.10.0-Red?logo=pytorch)

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/weizmannscience/text2live)



![teaser](https://user-images.githubusercontent.com/22198039/179798581-ca6f6652-600a-400a-b21b-713fc5c15d56.png)



**Text2LIVE** is a method for text-driven editing of real-world images and videos, as described in (link to paper).



[//]: # (. It can be used for localized and global edits that change the texture of existing objects or augment the scene with semi-transparent effects (e.g. smoke, fire, snow).)



[//]: # (### Abstract)

>We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Specifically, given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with new visual effects (e.g., smoke, fire) in a semantically meaningful manner. Our framework trains a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. Thus, it can perform localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes.



## Getting Started

### Installation



```

git clone https://github.com/omerbt/Text2LIVE.git

conda create --name text2live python=3.9 

conda activate text2live 

pip install -r requirements.txt

```



### Download sample images and videos

Download sample images and videos from the DAVIS dataset:

```

cd Text2LIVE

gdown https://drive.google.com/uc?id=1osN4PlPkY9uk6pFqJZo8lhJUjTIpa80J&export=download

unzip data.zip

```

It will create a folder `data`:

```

Text2LIVE

├── ...

├── data

│   ├── pretrained_nla_models # NLA models are stored here

│   ├── images # sample images

│   └── videos # sample videos from DAVIS dataset

│         ├── car-turn # contains video frames 

│         ├── ...

└── ...

```

To enforce temporal consistency in video edits, we utilize the Neural Layered Atlases (NLA). Pretrained NLA models are taken from here, and are already inside the `data` folder.



### Run examples 

* Our method is designed to change textures of existing objects / augment the scene with semi-transparent effects (e.g., smoke, fire). It is not designed for adding new objects or significantly deviating from the original spatial layout.

* Training **Text2LIVE** multiple times with the same inputs can lead to slightly different results.

* CLIP sometimes exhibits bias towards specific solutions (see figure 9 in the paper), thus slightly different text prompts may lead to different flavors of edits.



The required GPU memory depends on the input image/video size, but you should be good with a Tesla V100 32GB :).

Currently mixed precision introduces some instability in the training process, but it could be added later.



#### Video Editing

Run the following command to start training

```

python train_video.py --example_config car-turn_winter.yaml

```

#### Image Editing

Run the following command to start training

```

python train_image.py --example_config golden_horse.yaml

```

Intermediate results will be saved to `results` during optimization. The frequency of saving intermediate results is indicated in the `log_images_freq` flag of the configuration.



## Sample Results

https://user-images.githubusercontent.com/22198039/179797381-983e0453-2e5d-40e8-983d-578217b358e4.mov



For more see the [supplementary material](https://text2live.github.io/sm/index.html).



## Citation

```

@inproceedings{bar2022text2live,

  title={Text2live: Text-driven layered image and video editing},

  author={Bar-Tal, Omer and Ofri-Amar, Dolev and Fridman, Rafail and Kasten, Yoni and Dekel, Tali},

  booktitle={European Conference on Computer Vision},

  pages={707--723},

  year={2022},

  organization={Springer}

}

```