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	## FateZero: Fusing Attentions for Zero-shot Text-based Video Editing

	[Chenyang Qi](https://chenyangqiqi.github.io/), [Xiaodong Cun](http://vinthony.github.io/), [Yong Zhang](https://yzhang2016.github.io), [Chenyang Lei](https://chenyanglei.github.io/), [Xintao Wang](https://xinntao.github.io/), [Ying Shan](https://scholar.google.com/citations?hl=zh-CN&user=4oXBp9UAAAAJ), and [Qifeng Chen](https://cqf.io)

	<a href='https://arxiv.org/abs/2303.09535'><img src='https://img.shields.io/badge/ArXiv-2303.09535-red'></a>
	<a href='https://fate-zero-edit.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ChenyangQiQi/FateZero/blob/main/colab_fatezero.ipynb)
	[![GitHub](https://img.shields.io/github/stars/ChenyangQiQi/FateZero?style=social)](https://github.com/ChenyangQiQi/FateZero)


	<!-- ![fatezero_demo](./docs/teaser.png) -->

	<table class="center">
	<td><img src="docs/gif_results/17_car_posche_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/3_sunflower_vangogh_conat_result.gif"></td>
	<tr>
	<td width=25% style="text-align:center;">"silver jeep ➜ posche car"</td>
	<td width=25% style="text-align:center;">"+ Van Gogh style"</td>
	<!-- <td width=25% style="text-align:center;">"Wonder Woman, wearing a cowboy hat, is skiing"</td>
	<td width=25% style="text-align:center;">"A man, wearing pink clothes, is skiing at sunset"</td> -->
	</tr>
	</table >

	## Abstract
	<b>TL;DR: Using FateZero, Edits your video via pretrained Diffusion models without training.</b>

	<details><summary>CLICK for full abstract</summary>


	> The diffusion-based generative models have achieved
	remarkable success in text-based image generation. However,
	since it contains enormous randomness in generation
	progress, it is still challenging to apply such models for
	real-world visual content editing, especially in videos. In
	this paper, we propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt
	training or use-specific mask. To edit videos consistently,
	we propose several techniques based on the pre-trained
	models. Firstly, in contrast to the straightforward DDIM
	inversion technique, our approach captures intermediate
	attention maps during inversion, which effectively retain
	both structural and motion information. These maps are
	directly fused in the editing process rather than generated
	during denoising. To further minimize semantic leakage of
	the source video, we then fuse self-attentions with a blending
	mask obtained by cross-attention features from the source
	prompt. Furthermore, we have implemented a reform of the
	self-attention mechanism in denoising UNet by introducing
	spatial-temporal attention to ensure frame consistency. Yet
	succinct, our method is the first one to show the ability of
	zero-shot text-driven video style and local attribute editing
	from the trained text-to-image model. We also have a better
	zero-shot shape-aware editing ability based on the text-tovideo
	model. Extensive experiments demonstrate our
	superior temporal consistency and editing capability than
	previous works.
	</details>

	## Changelog
	- 2023.03.27 Release [`attribute editing config`](config/attribute) and
	<!-- [`data`](https://hkustconnect-my.sharepoint.com/:u:/g/personal/cqiaa_connect_ust_hk/Ee7J2IzZuaVGkefh-ZRp1GwB7RCUYU7MVJCKqeNWmOIpfg?e=dcOwb7) -->
	[`data`](https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/attribute.zip) used in the paper.
	- 2023.03.22 Upload a `colab notebook` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ChenyangQiQi/FateZero/blob/main/colab_fatezero.ipynb). Enjoy the fun of zero-shot video-editing freely!
	- 2023.03.22 Release [`style editing config`](config/style) and
	<!--[`data`](https://hkustconnect-my.sharepoint.com/:u:/g/personal/cqiaa_connect_ust_hk/EaTqRAuW0eJLj0z_JJrURkcBZCC3Zvgsdo6zsXHhpyHhHQ?e=FzuiNG) -->
	[`data`](https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip)
	used in the paper.
	- 2023.03.21 [Editing guidance](docs/EditingGuidance.md) is provided to help users to edit in-the-wild video. Welcome to play and give feedback!
	- 2023.03.21 Update the `codebase and configuration`. Now, it can run with lower resources (16G GPU and less than 16G CPU RAM) with [new configuration](config/low_resource_teaser) in `config/low_resource_teaser`.
	<!-- A new option store all the attentions in hard disk, which require less ram. -->
	- 2023.03.17 Release Code and Paper!

	## Todo

	- [x] Release the edit config for teaser
	- [x] Memory and runtime profiling
	- [x] Hands-on guidance of hyperparameters tuning
	- [x] Colab
	- [x] Release configs for other result and in-the-wild dataset
	<!-- - [x] Style editing: done
	- [-] Attribute editing: in progress -->
	- [-] hugging-face: inprogress
	- [ ] Tune-a-video optimization and shape editing configs
	- [ ] Release more application

	## Setup Environment
	Our method is tested using cuda11, fp16 of accelerator and xformers on a single A100 or 3090.

	```bash
	conda create -n fatezero38 python=3.8
	conda activate fatezero38

	pip install -r requirements.txt
	```

	`xformers` is recommended for A100 GPU to save memory and running time.

	<details><summary>Click for xformers installation </summary>

	We find its installation not stable. You may try the following wheel:
	```bash
	wget https://github.com/ShivamShrirao/xformers-wheels/releases/download/4c06c79/xformers-0.0.15.dev0+4c06c79.d20221201-cp38-cp38-linux_x86_64.whl
	pip install xformers-0.0.15.dev0+4c06c79.d20221201-cp38-cp38-linux_x86_64.whl
	```

	</details>

	Validate the installation by
	```
	python test_install.py
	```

	Our environment is similar to Tune-A-video ([official](https://github.com/showlab/Tune-A-Video), [unofficial](https://github.com/bryandlee/Tune-A-Video)) and [prompt-to-prompt](https://github.com/google/prompt-to-prompt/). You may check them for more details.


	## FateZero Editing

	#### Style and Attribute Editing in Teaser

	Download the [stable diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) (or other interesting image diffusion model) and put it to `./ckpt/stable-diffusion-v1-4`.

	<details><summary>Click for bash command: </summary>

	```
	mkdir ./ckpt
	# download from huggingface face, takes 20G space
	git lfs install
	git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
	cd ./ckpt
	ln -s ../stable-diffusion-v1-4 .
	```
	</details>

	Then, you could reproduce style and shape editing result in our teaser by running:

	```bash
	accelerate launch test_fatezero.py --config config/teaser/jeep_watercolor.yaml
	# or CUDA_VISIBLE_DEVICES=0 python test_fatezero.py --config config/teaser/jeep_watercolor.yaml
	```

	<details><summary>The result is saved at `./result` . (Click for directory structure) </summary>

	```
	result
	├── teaser
	│ ├── jeep_posche
	│ ├── jeep_watercolor
	│ ├── cross-attention # visualization of cross-attention during inversion
	│ ├── sample # result
	│ ├── train_samples # the input video

	```

	</details>

	Editing 8 frames on an Nvidia 3090, use `100G CPU memory, 12G GPU memory` for editing. We also provide some [`low cost setting`](config/low_resource_teaser) of style editing by different hyper-parameters on a 16GB GPU.
	You may try these low cost setting on colab.
	[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ChenyangQiQi/FateZero/blob/main/colab_fatezero.ipynb)

	More the speed and hardware benchmark [here](docs/EditingGuidance.md#ddim-hyperparameters).

	#### Shape and large motion editing with Tune-A-Video

	Besides style and attribution editing above, we also provide a `Tune-A-Video` [checkpoint](https://hkustconnect-my.sharepoint.com/:f:/g/personal/cqiaa_connect_ust_hk/EviSTWoAOs1EmHtqZruq50kBZu1E8gxDknCPigSvsS96uQ?e=492khj). You may download the it and move it to `./ckpt/jeep_tuned_200/`.
	<!-- We provide the [Tune-a-Video](https://drive.google.com/file/d/166eNbabM6TeJVy7hxol2gL1kUGKHi3Do/view?usp=share_link), you could download the data, unzip and put it to `data`. : -->

	<details><summary>The directory structure should like this: (Click for directory structure) </summary>

	```
	ckpt
	├── stable-diffusion-v1-4
	├── jeep_tuned_200
	...
	data
	├── car-turn
	│ ├── 00000000.png
	│ ├── 00000001.png
	│ ├── ...
	video_diffusion
	```
	</details>

	You could reproduce the shape editing result in our teaser by running:

	```bash
	accelerate launch test_fatezero.py --config config/teaser/jeep_posche.yaml
	```


	### Reproduce other results in the paper (in progress)
	<!-- Download the data of [style editing](https://hkustconnect-my.sharepoint.com/:u:/g/personal/cqiaa_connect_ust_hk/EaTqRAuW0eJLj0z_JJrURkcBZCC3Zvgsdo6zsXHhpyHhHQ?e=FzuiNG) and [attribute editing](https://hkustconnect-my.sharepoint.com/:u:/g/personal/cqiaa_connect_ust_hk/Ee7J2IzZuaVGkefh-ZRp1GwB7RCUYU7MVJCKqeNWmOIpfg?e=dcOwb7)
	-->
	Download the data of style editing and attribute editing
	from [onedrive](https://hkustconnect-my.sharepoint.com/:f:/g/personal/cqiaa_connect_ust_hk/EkIeHj3CQiBNhm6iEEhJQZwBEBJNCGt3FsANmyqeAYbuXQ?e=FxYtJk) or from Github [Release](https://github.com/ChenyangQiQi/FateZero/releases/tag/v0.0.1).
	<details><summary>Click for wget bash command: </summary>

	```
	wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/attribute.zip
	wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip
	```
	</details>

	Unzip and Place it in ['./data'](data). Then use the command in ['config/style'](config/style) and ['config/attribute'](config/attribute) to get the results.

	The config of our tune-a-video ckpts will be updated latter.

	## Tuning guidance to edit YOUR video
	We provided a tuning guidance to edit in-the-wild video at [here](./docs/EditingGuidance.md). The work is still in progress. Welcome to give your feedback in issues.

	## Style Editing Results with Stable Diffusion
	We show the difference of source prompt and target prompt in the box below each video.

	Note mp4 and gif files in this github page are compressed.
	Please check our [Project Page](https://fate-zero-edit.github.io/) for mp4 files of original video editing results.
	<table class="center">

	<tr>
	<td><img src="docs/gif_results/style/1_surf_ukiyo_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/style/2_car_watercolor_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/style/6_lily_monet_01_concat_result.gif"></td>
	<!-- <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/wonder-woman.gif"></td>
	<td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/pink-sunset.gif"></td> -->
	</tr>
	<tr>
	<td width=25% style="text-align:center;">"+ Ukiyo-e style"</td>
	<td width=25% style="text-align:center;">"+ watercolor painting"</td>
	<td width=25% style="text-align:center;">"+ Monet style"</td>
	</tr>

	<tr>
	<td><img src="docs/gif_results/style/4_rabit_pokemon_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/style/5_train_shikai_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/style/7_swan_carton_01_concat_result.gif"></td>

	</tr>
	<tr>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"+ Pokémon cartoon style"</td>
	<td width=25% style="text-align:center;">"+ Makoto Shinkai style"</td>
	<td width=25% style="text-align:center;">"+ cartoon style"</td>
	</tr>
	</table>

	## Attribute Editing Results with Stable Diffusion
	<table class="center">

	<tr>

	<td><img src="docs/gif_results/attri/15_rabbit_eat_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/15_rabbit_eat_02_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/15_rabbit_eat_04_concat_result.gif"></td>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"rabbit, strawberry ➜ white rabbit, flower"</td>
	<td width=25% style="text-align:center;">"rabbit, strawberry ➜ squirrel, carrot"</td>
	<td width=25% style="text-align:center;">"rabbit, strawberry ➜ white rabbit, leaves"</td>

	</tr>
	<tr>

	<td><img src="docs/gif_results/attri/16_sq_eat_04_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/16_sq_eat_02_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/16_sq_eat_03_concat_result.gif"></td>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"squirrel ➜ robot squirrel"</td>
	<td width=25% style="text-align:center;">"squirrel, Carrot ➜ rabbit, eggplant"</td>
	<td width=25% style="text-align:center;">"squirrel, Carrot ➜ robot mouse, screwdriver"</td>

	</tr>

	<tr>

	<td><img src="docs/gif_results/attri/13_bear_tiger_leopard_lion_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/13_bear_tiger_leopard_lion_02_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/13_bear_tiger_leopard_lion_03_concat_result.gif"></td>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"bear ➜ a red tiger"</td>
	<td width=25% style="text-align:center;">"bear ➜ a yellow leopard"</td>
	<td width=25% style="text-align:center;">"bear ➜ a brown lion"</td>

	</tr>
	<tr>

	<td><img src="docs/gif_results/attri/14_cat_grass_tiger_corgin_02_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/14_cat_grass_tiger_corgin_03_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/14_cat_grass_tiger_corgin_04_concat_result.gif"></td>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"cat ➜ black cat, grass..."</td>
	<td width=25% style="text-align:center;">"cat ➜ red tiger"</td>
	<td width=25% style="text-align:center;">"cat ➜ Shiba-Inu"</td>

	</tr>

	<tr>

	<td><img src="docs/gif_results/attri/10_bus_gpu_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/11_dog_robotic_corgin_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/11_dog_robotic_corgin_02_concat_result.gif"></td>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"bus ➜ GPU"</td>
	<td width=25% style="text-align:center;">"gray dog ➜ yellow corgi"</td>
	<td width=25% style="text-align:center;">"gray dog ➜ robotic dog"</td>

	</tr>
	<tr>

	<td><img src="docs/gif_results/attri/9_duck_rubber_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/12_fox_snow_wolf_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/attri/12_fox_snow_wolf_02_concat_result.gif"></td>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"white duck ➜ yellow rubber duck"</td>
	<td width=25% style="text-align:center;">"grass ➜ snow"</td>
	<td width=25% style="text-align:center;">"white fox ➜ grey wolf"</td>

	</tr>


	</table>

	## Shape and large motion editing with Tune-A-Video
	<table class="center">

	<tr>
	<td><img src="docs/gif_results/shape/17_car_posche_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/shape/18_swan_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/shape/18_swan_02_concat_result.gif"></td>
	<!-- <td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/wonder-woman.gif"></td>
	<td><img src="https://tuneavideo.github.io/assets/results/tuneavideo/man-skiing/pink-sunset.gif"></td> -->
	</tr>
	<tr>
	<td width=25% style="text-align:center;">"silver jeep ➜ posche car"</td>
	<td width=25% style="text-align:center;">"Swan ➜ White Duck"</td>
	<td width=25% style="text-align:center;">"Swan ➜ Pink flamingo"</td>
	</tr>

	<tr>
	<td><img src="docs/gif_results/shape/19_man_wonder_01_concat_result.gif"></td>
	<td><img src="docs/gif_results/shape/19_man_wonder_02_concat_result.gif"></td>
	<td><img src="docs/gif_results/shape/19_man_wonder_03_concat_result.gif"></td>

	</tr>
	<tr>

	</tr>
	<tr>
	<td width=25% style="text-align:center;">"A man ➜ A Batman"</td>
	<td width=25% style="text-align:center;">"A man ➜ A Wonder Woman, With cowboy hat"</td>
	<td width=25% style="text-align:center;">"A man ➜ A Spider-Man"</td>
	</tr>
	</table>


	## Demo Video

	https://user-images.githubusercontent.com/45789244/225698509-79c14793-3153-4bba-9d6e-ede7d811d7f8.mp4

	The video here is compressed due to the size limit of github.
	The original full resolution video is [here](https://hkustconnect-my.sharepoint.com/:v:/g/personal/cqiaa_connect_ust_hk/EXKDI_nahEhKtiYPvvyU9SkBDTG2W4G1AZ_vkC7ekh3ENw?e=Xhgtmk).


	## Citation

	```
	@misc{qi2023fatezero,
	title={FateZero: Fusing Attentions for Zero-shot Text-based Video Editing},
	author={Chenyang Qi and Xiaodong Cun and Yong Zhang and Chenyang Lei and Xintao Wang and Ying Shan and Qifeng Chen},
	year={2023},
	eprint={2303.09535},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	```


	## Acknowledgements

	This repository borrows heavily from [Tune-A-Video](https://github.com/showlab/Tune-A-Video) and [prompt-to-prompt](https://github.com/google/prompt-to-prompt/). thanks the authors for sharing their code and models.

	## Maintenance

	This is the codebase for our research work. We are still working hard to update this repo and more details are coming in days. If you have any questions or ideas to discuss, feel free to contact [Chenyang Qi](cqiaa@connect.ust.hk) or [Xiaodong Cun](vinthony@gmail.com).