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# Anime Video Models
:white_check_mark: We add small models that are optimized for anime videos :-)<br>
More comparisons can be found in [anime_comparisons.md](anime_comparisons.md)
- [How to Use](#how-to-use)
- [PyTorch Inference](#pytorch-inference)
- [ncnn Executable File](#ncnn-executable-file)
- [Step 1: Use ffmpeg to extract frames from video](#step-1-use-ffmpeg-to-extract-frames-from-video)
- [Step 2: Inference with Real-ESRGAN executable file](#step-2-inference-with-real-esrgan-executable-file)
- [Step 3: Merge the enhanced frames back into a video](#step-3-merge-the-enhanced-frames-back-into-a-video)
- [More Demos](#more-demos)
| Models | Scale | Description |
| ---------------------------------------------------------------------------------------------------------------------------------- | :---- | :----------------------------- |
| [realesr-animevideov3](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth) | X4 <sup>1</sup> | Anime video model with XS size |
Note: <br>
<sup>1</sup> This model can also be used for X1, X2, X3.
---
The following are some demos (best view in the full screen mode).
<https://user-images.githubusercontent.com/17445847/145706977-98bc64a4-af27-481c-8abe-c475e15db7ff.MP4>
<https://user-images.githubusercontent.com/17445847/145707055-6a4b79cb-3d9d-477f-8610-c6be43797133.MP4>
<https://user-images.githubusercontent.com/17445847/145783523-f4553729-9f03-44a8-a7cc-782aadf67b50.MP4>
## How to Use
### PyTorch Inference
```bash
# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P weights
# single gpu and single process inference
CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2
# single gpu and multi process inference (you can use multi-processing to improve GPU utilization)
CUDA_VISIBLE_DEVICES=0 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2
# multi gpu and multi process inference
CUDA_VISIBLE_DEVICES=0,1,2,3 python inference_realesrgan_video.py -i inputs/video/onepiece_demo.mp4 -n realesr-animevideov3 -s 2 --suffix outx2 --num_process_per_gpu 2
```
```console
Usage:
--num_process_per_gpu The total number of process is num_gpu * num_process_per_gpu. The bottleneck of
the program lies on the IO, so the GPUs are usually not fully utilized. To alleviate
this issue, you can use multi-processing by setting this parameter. As long as it
does not exceed the CUDA memory
--extract_frame_first If you encounter ffmpeg error when using multi-processing, you can turn this option on.
```
### NCNN Executable File
#### Step 1: Use ffmpeg to extract frames from video
```bash
ffmpeg -i onepiece_demo.mp4 -qscale:v 1 -qmin 1 -qmax 1 -vsync 0 tmp_frames/frame%08d.png
```
- Remember to create the folder `tmp_frames` ahead
#### Step 2: Inference with Real-ESRGAN executable file
1. Download the latest portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**
1. Taking the Windows as example, run:
```bash
./realesrgan-ncnn-vulkan.exe -i tmp_frames -o out_frames -n realesr-animevideov3 -s 2 -f jpg
```
- Remember to create the folder `out_frames` ahead
#### Step 3: Merge the enhanced frames back into a video
1. First obtain fps from input videos by
```bash
ffmpeg -i onepiece_demo.mp4
```
```console
Usage:
-i input video path
```
You will get the output similar to the following screenshot.
<p align="center">
<img src="https://user-images.githubusercontent.com/17445847/145710145-c4f3accf-b82f-4307-9f20-3803a2c73f57.png">
</p>
2. Merge frames
```bash
ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -c:v libx264 -r 23.98 -pix_fmt yuv420p output.mp4
```
```console
Usage:
-i input video path
-c:v video encoder (usually we use libx264)
-r fps, remember to modify it to meet your needs
-pix_fmt pixel format in video
```
If you also want to copy audio from the input videos, run:
```bash
ffmpeg -r 23.98 -i out_frames/frame%08d.jpg -i onepiece_demo.mp4 -map 0:v:0 -map 1:a:0 -c:a copy -c:v libx264 -r 23.98 -pix_fmt yuv420p output_w_audio.mp4
```
```console
Usage:
-i input video path, here we use two input streams
-c:v video encoder (usually we use libx264)
-r fps, remember to modify it to meet your needs
-pix_fmt pixel format in video
```
## More Demos
- Input video for One Piece:
<https://user-images.githubusercontent.com/17445847/145706822-0e83d9c4-78ef-40ee-b2a4-d8b8c3692d17.mp4>
- Out video for One Piece
<https://user-images.githubusercontent.com/17445847/164960481-759658cf-fcb8-480c-b888-cecb606e8744.mp4>
**More comparisons**
<https://user-images.githubusercontent.com/17445847/145707458-04a5e9b9-2edd-4d1f-b400-380a72e5f5e6.MP4>
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