zsxkib / st-mfnet

📽️ Increase Framerate 🎬 ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

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Total runs: 51.6K
24-hour runs: 0
7-day runs: 0
30-day runs: 0
Github
Model's Last Updated: October 09 2023

Introduction of st-mfnet

Model Details of st-mfnet

Readme

ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

📽️ Increase Framerate 🎬
Duolikun Danier, Fan Zhang, David Bull
Accepted in CVPR 2022.

Project | Paper | arXiv | Video | Poster

Replicate

Welcome to the Video Frame Rate Enhancer on Replicate’s WebUI! 🎥 If you’ve ever wanted your videos to play back smoother, you’re in the right place. Let’s dive in and make some movie magic! 🌠

Model description 🧠

Our Video Frame Rate Enhancer works behind the scenes with a piece of advanced tech named STMFNet . Think of it as a smart tool that studies short segments of your video (sets of 4 frames) and predicts additional frames to slip in between. The result? Your video plays back at a higher frame rate, making movements look silky smooth, almost like those high-end slow-motion clips you adore on professional broadcasts. 🍿

Intended use 💡
Who is this for?
  1. Content Creators 🎨 : Elevate your vlogs, tutorials, or any content by making playback smoother, giving it that premium feel.
  2. Hobby Videographers 📹 : Transition your standard videos into breathtaking slow-motion works of art, minus the usual stutter.
  3. Retro Video Fans 🎞️ : Revitalize older, jittery clips by enhancing the frame rate.
WebUI Controls: A Deep Dive
  • mp4 : Upload your video magic here! We support .mp4 formats. 📥
  • keep_original_video_length :
  • Description : Do you want your enhanced video to keep its original length? This option controls that. If you turn this on ( True ), the model will adjust the frame rate so that the video remains the same duration while making it smoother. If turned off ( False ), you’ll then get to set a specific frame rate with custom_fps .
  • Default : True (on) ⏱️

  • custom_fps :

  • Description : This is where you dial in your desired video smoothness! FPS stands for “frames per second.” The higher the number, the smoother the video. But remember, this only kicks in if you’ve turned off the keep_original_video_length .
  • Range : 1 (minimal smoothness) to 240 (ultra-smooth playback)
  • Default : 24 FPS ⚙️

  • number_of_framerate_doubles :

  • Description : Here’s where the magic really happens! This control lets you decide how many times you want to multiply the frame rate. For instance, setting it to 1 will roughly double a 100-frame video to about 200 frames. Going for 2? Expect it to jump to around 400 frames, and so on.
  • Range : 1 (double the frames) to 4 (multiply by a whopping 16 times!)
  • Default : 1 (double it up!) 🔄
Ethical considerations 🌟

Using tools like ours is super fun, but let’s keep a few things in mind:

  1. Play Fair 🤝 : Enhance videos you own or have rights to.
  2. Be Honest 🧐 : If you’re sharing the enhanced videos, give viewers a heads-up that it’s been tweaked.
  3. Respect Privacy 🕶️ : Respect boundaries. Not every moment needs to be in HD.
Caveats and recommendations ⚠️
  1. Quality vs. Smoothness : We make videos smoother, not clearer. Keep expectations in check! 📺
  2. Best for Mid to High Frame Rate Videos : Super slow originals might not get the full VIP treatment.
  3. Storage Space 📦 : We use an interim .avi format before the final .mp4 . A tad more storage is needed while processing.
  4. Using Custom FPS : If you’re setting a custom frame rate, remember to toggle off “keep_original_video_length”, or it’ll be overlooked. 🚫

We hope you love using our Video Frame Rate Enhancer! 🌈 If things get confusing or if you bump into snags, feel free to contact us! Happy enhancing! 🚀

Note: your video will lose its sound!


Example results

Paper

Citation
@InProceedings{Danier_2022_CVPR,
    author    = {Danier, Duolikun and Zhang, Fan and Bull, David},
    title     = {ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {3521-3531}
}
Acknowledgement from Danier et al.

Lots of code in this repository are adapted/taken from the following repositories:

We would like to thank the authors for sharing their code.

Pricing of st-mfnet replicate.com

Run time and cost

This model costs approximately $0.052 to run on Replicate, or 19 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker .

This model runs on Nvidia A40 GPU hardware . Predictions typically complete within 91 seconds. The predict time for this model varies significantly based on the inputs.

Runs of zsxkib st-mfnet on replicate.com

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st-mfnet replicate.com

st-mfnet replicate.com is an AI model on replicate.com that provides st-mfnet's model effect (📽️ Increase Framerate 🎬 ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation), which can be used instantly with this zsxkib st-mfnet model. replicate.com supports a free trial of the st-mfnet model, and also provides paid use of the st-mfnet. Support call st-mfnet model through api, including Node.js, Python, http.

st-mfnet replicate.com Url

https://replicate.com/zsxkib/st-mfnet

zsxkib st-mfnet online free

st-mfnet replicate.com is an online trial and call api platform, which integrates st-mfnet's modeling effects, including api services, and provides a free online trial of st-mfnet, you can try st-mfnet online for free by clicking the link below.

zsxkib st-mfnet online free url in replicate.com:

https://replicate.com/zsxkib/st-mfnet

st-mfnet install

st-mfnet is an open source model from GitHub that offers a free installation service, and any user can find st-mfnet on GitHub to install. At the same time, replicate.com provides the effect of st-mfnet install, users can directly use st-mfnet installed effect in replicate.com for debugging and trial. It also supports api for free installation.

st-mfnet install url in replicate.com:

https://replicate.com/zsxkib/st-mfnet

st-mfnet install url in github:

https://github.com/zsxkib/ST-MFNet

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