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-# FRESCO - Official PyTorch Implementation
-**FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation**<br>
-[Shuai Yang](https://williamyang1991.github.io/), [Yifan Zhou](https://zhouyifan.net/), [Ziwei Liu](https://liuziwei7.github.io/) and [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/)<br>
-in CVPR 2024 <br>
-[**Project Page**](https://www.mmlab-ntu.com/project/fresco/) | [**Paper**](https://arxiv.org/abs/2403.12962) | [**Supplementary Video**](https://youtu.be/jLnGx5H-wLw) | [**Input Data and Video Results**](https://drive.google.com/file/d/12BFx3hp8_jp9m0EmKpw-cus2SABPQx2Q/view?usp=sharing) <br>
-**Abstract:** *The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains.
-Zero-shot methods seek to extend image diffusion models to videos without necessitating model training.
-Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However, the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient, resulting in temporal inconsistency.
-In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance, our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video, significantly improving the visual coherence of the resulting translated videos.
-Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality, coherent videos, marking a notable improvement over existing zero-shot methods.*
-**Features**:<br>
-- **Temporal consistency**: use intra-and inter-frame constraint with better consistency and coverage than optical flow alone.
-    - Compared with our previous work [Rerender-A-Video](https://github.com/williamyang1991/Rerender_A_Video), FRESCO is more robust to large and quick motion.
-- **Zero-shot**: no training or fine-tuning required.
-- **Flexibility**: compatible with off-the-shelf models (e.g., [ControlNet](https://github.com/lllyasviel/ControlNet), [LoRA](https://civitai.com/)) for customized translation.
-https://github.com/williamyang1991/FRESCO/assets/18130694/aad358af-4d27-4f18-b069-89a1abd94d38
-## Updates
-- [03/2023] Paper is released.
-- [03/2023] Code is released.
-- [03/2024] This website is created.
-### TODO
-- [ ] Integrate into Diffusers
-- [ ] Add Huggingface web demo
-- [x] ~~Add webUI.~~
-- [x] ~~Update readme~~
-- [x] ~~Upload paper to arXiv, release related material~~
-## Installation
-1. Clone the repository.
-```shell
-git clone https://github.com/williamyang1991/FRESCO.git
-cd FRESCO
-```
-2. You can simply set up the environment with pip based on [requirements.txt](https://github.com/williamyang1991/FRESCO/blob/main/requirements.txt)
-    - Create a conda environment and install torch >= 2.0.0. Here is an example script to install torch 2.0.0 + CUDA 11.8 :
-    ```
-    conda create --name diffusers python==3.8.5
-    conda activate diffusers
-    pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118
-    ```
-    - Run `pip install -r requirements.txt` in an environment where torch is installed.
-    - We have tested on torch 2.0.0/2.1.0 and diffusers 0.19.3
-    - If you use new versions of diffusers, you need to modify [my_forward()](https://github.com/williamyang1991/FRESCO/blob/fb991262615665de88f7a8f2cc903d9539e1b234/src/diffusion_hacked.py#L496)
-3. Run the installation script. The required models will be downloaded in `./model`, `./src/ControlNet/annotator` and `./src/ebsynth/deps/ebsynth/bin`.
-    - Requires access to huggingface.co
-```shell
-python install.py
-```
-4. You can run the demo with `run_fresco.py`
-```shell
-python run_fresco.py ./config/config_music.yaml
-```
-5. For issues with Ebsynth, please refer to [issues](https://github.com/williamyang1991/Rerender_A_Video#issues)
-## (1) Inference
-### WebUI (recommended)
-```
-python webUI.py
-```
-The Gradio app also allows you to flexibly change the inference options. Just try it for more details.
-Upload your video, input the prompt, select the model and seed, and hit:
-- **Run Key Frames**: detect keyframes, translate all keyframes.
-- **Run Propagation**: propagate the keyframes to other frames for full video translation
-- **Run All**: **Run Key Frames** and **Run Propagation**
-Select the model:
-- **Base model**: base Stable Diffusion model (SD 1.5)
-    - Stable Diffusion 1.5: official model
-    - [rev-Animated](https://huggingface.co/stablediffusionapi/rev-animated): a semi-realistic (2.5D) model
-    - [realistic-Vision](https://huggingface.co/SG161222/Realistic_Vision_V2.0): a photo-realistic model
-    - [flat2d-animerge](https://huggingface.co/stablediffusionapi/flat-2d-animerge): a cartoon model
-    - You can add other models on huggingface.co by modifying this [line](https://github.com/williamyang1991/FRESCO/blob/1afcca9c7b1bc1ac68254f900be9bd768fbb6988/webUI.py#L362)
-![overview](https://github.com/williamyang1991/FRESCO/assets/18130694/6ce5d54e-b020-4e43-95e7-72ab1783f482)
-We provide abundant advanced options to play with
-</details>
-<details id="option1">
-<summary> <b>Advanced options for single frame processing</b></summary>
-1. **Frame resolution**: resize the short side of the video to 512.
-2. ControlNet related:
-   - **ControlNet strength**: how well the output matches the input control edges
-   - **Control type**: HED edge, Canny edge, Depth map
-   - **Canny low/high threshold**: low values for more edge details
-3. SDEdit related:
-   - **Denoising strength**: repaint degree (low value to make the output look more like the original video)
-   - **Preserve color**: preserve the color of the original video
-4. SD related:
-   - **Steps**: denoising step
-   - **CFG scale**: how well the output matches the prompt
-   - **Added prompt/Negative prompt**: supplementary prompts
-5. FreeU related:
-   - **FreeU first/second-stage backbone factor**: =1 do nothing; >1 enhance output color and details
-   - **FreeU first/second-stage skip factor**: =1 do nothing; <1 enhance output color and details
-</details>
-<details id="option2">
-<summary> <b>Advanced options for FRESCO constraints</b></summary>
-1. Keyframe related
-   - **Number of frames**: Total frames to be translated
-   - **Number of frames in a batch**: To avoid out-of-memory, use small batch size
-   - **Min keyframe interval (s_min)**: The keyframes will be detected at least every s_min frames
-   - **Max keyframe interval (s_max)**: The keyframes will be detected at most every s_max frames
-2. FRESCO constraints
-   - FRESCO-guided Attention:
-     - **spatial-guided attention**: Check to enable spatial-guided attention
-     - **cross-frame attention**: Check to enable efficient cross-frame attention
-     - **temporal-guided attention**: Check to enable temporal-guided attention
-   - FRESCO-guided optimization:
-     - **spatial-guided optimization**: Check to enable spatial-guided optimization
-     - **temporal-guided optimization**: Check to enable temporal-guided optimization
-3. **Background smoothing**: Check to enable background smoothing (best for static background)
-</details>
-<details id="option3">
-<summary> <b>Advanced options for the full video translation</b></summary>
-1. **Gradient blending**: apply Poisson Blending to reduce ghosting artifacts. May slow the process and increase flickers.
-2. **Number of parallel processes**: multiprocessing to speed up the process. Large value (4) is recommended.
-</details>
-![option](https://github.com/williamyang1991/FRESCO/assets/18130694/72600758-1dff-4b7c-8f3f-65ee3909f8f6)
-### Command Line
-We provide a flexible script `run_fresco.py` to run our method.
-Set the options via a config file. For example,
-```shell
-python run_fresco.py ./config/config_music.yaml
-```
-We provide some examples of the config in `config` directory.
-Most options in the config is the same as those in WebUI.
-Please check the explanations in the WebUI section.
-We provide a separate Ebsynth python script `video_blend.py` with the temporal blending algorithm introduced in
-[Stylizing Video by Example](https://dcgi.fel.cvut.cz/home/sykorad/ebsynth.html) for interpolating style between key frames.
-It can work on your own stylized key frames independently of our FRESCO algorithm.
-For the details, please refer to our previous work [Rerender-A-Video](https://github.com/williamyang1991/Rerender_A_Video/tree/main?tab=readme-ov-file#our-ebsynth-implementation)
-## (2) Results
-### Key frame translation
-<table class="center">
-<tr>
-  <td><img src="https://github.com/williamyang1991/FRESCO/assets/18130694/e8d5776a-37c5-49ae-8ab4-15669df6f572" raw=true></td>
-  <td><img src="https://github.com/williamyang1991/FRESCO/assets/18130694/8a792af6-555c-4e82-ac1e-5c2e1ee35fdb" raw=true></td>
-  <td><img src="https://github.com/williamyang1991/FRESCO/assets/18130694/10f9a964-85ac-4433-84c5-1611a6c2c434" raw=true></td>
-  <td><img src="https://github.com/williamyang1991/FRESCO/assets/18130694/0ec0fbf9-90dd-4d8b-964d-945b5f6687c2" raw=true></td>
-</tr>
-<tr>
-  <td width=26.5% align="center">a red car turns in the winter</td>
-  <td width=26.5% align="center">an African American boxer wearing black boxing gloves punches towards the camera, cartoon style</td>
-  <td width=26.5% align="center">a cartoon spiderman in black suit, black shoes and white gloves is dancing</td>
-  <td width=20.5% align="center">a beautiful woman holding her glasses in CG style</td>
-</tr>
-</table>
-### Full video translation
-https://github.com/williamyang1991/FRESCO/assets/18130694/bf8bfb82-5cb7-4b2f-8169-cf8dbf408b54
-## Citation
-If you find this work useful for your research, please consider citing our paper:
-```bibtex
-@inproceedings{yang2024fresco,
- title = {FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation},
- author = {Yang, Shuai and Zhou, Yifan and Liu, Ziwei and and Loy, Chen Change},
- booktitle = {CVPR},
- year = {2024},
-}
-```
-## Acknowledgments
-The code is mainly developed based on [Rerender-A-Video](https://github.com/williamyang1991/Rerender_A_Video), [ControlNet](https://github.com/lllyasviel/ControlNet), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [GMFlow](https://github.com/haofeixu/gmflow) and [Ebsynth](https://github.com/jamriska/ebsynth).

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+emoji: ⚡
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+app_file: app.py
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