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Maintainer: tencentarc

Total Score


Last updated 5/16/2024


Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

animesr is a real-world super-resolution model for animation videos developed by the Tencent ARC Lab. It can effectively enhance the resolution and quality of low-quality animation videos, producing clear and high-quality results. Compared to similar models like GFPGAN, Real-ESRGAN, RealESRGAN, and PhotoMaker Style, animesr is specifically designed for animation videos, providing superior performance on this type of content.

Model inputs and outputs

animesr takes either a video file or a zip file of image frames as input, and outputs a high-quality, upscaled video. The model is capable of 4x super-resolution, meaning it can quadruple the resolution of the input video while preserving details and reducing artifacts.


  • Video: Input video file
  • Frames: Zip file of frames from a video


  • Video: High-quality, upscaled video


animesr excels at enhancing the resolution and visual quality of animation videos, producing clear and detailed results with fewer artifacts compared to traditional upscaling methods. The model has been trained on a large dataset of animation videos, allowing it to effectively handle a wide range of animation styles and content.

What can I use it for?

The animesr model can be used to improve the quality of low-resolution animation videos, making them more suitable for larger displays or video streaming platforms. This can be particularly useful for restoring old or degraded animation content, or for upscaling lower-quality animation created for mobile devices. Additionally, the model could be leveraged by animation studios or video editors to efficiently enhance the production value of their animated projects.

Things to try

One interesting aspect of animesr is its ability to handle a diverse range of animation styles and content. Try experimenting with the model on different types of animation, from classic cartoons to modern anime, to see how it performs across various visual styles and genres. Additionally, you can explore the impact of adjusting the output scale, as the model supports arbitrary scaling factors beyond the default 4x resolution.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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