Update README.md

README.md

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-license: mit
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+# Text2LIVE: Text-Driven Layered Image and Video Editing (ECCV 2022 - Oral)
+## [<a href="https://text2live.github.io/" target="_blank">Project Page</a>]
+
+[![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 <a href="https://arxiv.org/abs/2204.02491" target="_blank">(link to paper)</a>.
+
+[//]: # (. It can be used for localized and global edits that change the texture of existing objects or augment the scene with semi-transparent effects &#40;e.g. smoke, fire, snow&#41;.)
+
+[//]: # (### 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 <a href="https://layered-neural-atlases.github.io">here</a>, 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}
+}
+```