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https://github.com/nihui/realcugan-ncnn-vulkan
real-cugan converter ncnn version, runs fast on intel / amd / nvidia / apple-silicon GPU with vulkan
https://github.com/nihui/realcugan-ncnn-vulkan
amd gpu intel linux macos ncnn nvidia realcugan vulkan windows
Last synced: 20 days ago
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real-cugan converter ncnn version, runs fast on intel / amd / nvidia / apple-silicon GPU with vulkan
- Host: GitHub
- URL: https://github.com/nihui/realcugan-ncnn-vulkan
- Owner: nihui
- License: mit
- Created: 2022-02-13T03:05:45.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-03-12T03:13:34.000Z (almost 2 years ago)
- Last Synced: 2024-11-20T01:53:49.736Z (23 days ago)
- Topics: amd, gpu, intel, linux, macos, ncnn, nvidia, realcugan, vulkan, windows
- Language: C
- Homepage:
- Size: 41.4 MB
- Stars: 774
- Watchers: 10
- Forks: 46
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-ncnn - Real-CUGAN-ncnn-vulkan - CUGAN converter. Runs fast on Intel / AMD / Nvidia with Vulkan API. (Application projects / Super Resolution)
- awesome-mad - realcugan-ncnn-vulkan - real-cugan converter ncnn version. (超分辨率 / 特效/实用工具)
README
# Real-CUGAN ncnn Vulkan
:exclamation: :exclamation: :exclamation: This software is in the early development stage, it may bite your cat
ncnn implementation of Real-CUGAN converter. Runs fast on Intel / AMD / Nvidia / Apple-Silicon with Vulkan API.
realcugan-ncnn-vulkan uses [ncnn project](https://github.com/Tencent/ncnn) as the universal neural network inference framework.
## [Download](https://github.com/nihui/realcugan-ncnn-vulkan/releases)
Download Windows/Linux/MacOS Executable for Intel/AMD/Nvidia/Apple-Silicon GPU
**https://github.com/nihui/realcugan-ncnn-vulkan/releases**
This package includes all the binaries and models required. It is portable, so no CUDA or PyTorch runtime environment is needed :)
## About Real-CUGAN
Real-CUGAN (Real Cascade U-Nets for Anime Image Super Resolution)
https://github.com/bilibili/ailab/tree/main/Real-CUGAN
## Usages
### Example Command
```shell
realcugan-ncnn-vulkan.exe -i input.jpg -o output.png
```
### Full Usages
```console
Usage: realcugan-ncnn-vulkan -i infile -o outfile [options]...
-h show this help
-v verbose output
-i input-path input image path (jpg/png/webp) or directory
-o output-path output image path (jpg/png/webp) or directory
-n noise-level denoise level (-1/0/1/2/3, default=-1)
-s scale upscale ratio (1/2/3/4, default=2)
-t tile-size tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu
-c syncgap-mode sync gap mode (0/1/2/3, default=3)
-m model-path realcugan model path (default=models-se)
-g gpu-id gpu device to use (-1=cpu, default=auto) can be 0,1,2 for multi-gpu
-j load:proc:save thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
-x enable tta mode
-f format output image format (jpg/png/webp, default=ext/png)
```
- `input-path` and `output-path` accept either file path or directory path
- `noise-level` = noise level, large value means strong denoise effect, -1 = no effect
- `scale` = scale level, 1 = no scaling, 2 = upscale 2x
- `tile-size` = tile size, use smaller value to reduce GPU memory usage, default selects automatically
- `syncgap-mode` = sync gap mode, 0 = no sync, 1 = accurate sync, 2 = rough sync, 3 = very rough sync
- `load:proc:save` = thread count for the three stages (image decoding + realcugan upscaling + image encoding), using larger values may increase GPU usage and consume more GPU memory. You can tune this configuration with "4:4:4" for many small-size images, and "2:2:2" for large-size images. The default setting usually works fine for most situations. If you find that your GPU is hungry, try increasing thread count to achieve faster processing.
- `format` = the format of the image to be output, png is better supported, however webp generally yields smaller file sizes, both are losslessly encoded
If you encounter a crash or error, try upgrading your GPU driver:
- Intel: https://downloadcenter.intel.com/product/80939/Graphics-Drivers
- AMD: https://www.amd.com/en/support
- NVIDIA: https://www.nvidia.com/Download/index.aspx
## Build from Source
1. Download and setup the Vulkan SDK from https://vulkan.lunarg.com/
- For Linux distributions, you can either get the essential build requirements from package manager
```shell
dnf install vulkan-headers vulkan-loader-devel
```
```shell
apt-get install libvulkan-dev
```
```shell
pacman -S vulkan-headers vulkan-icd-loader
```
2. Clone this project with all submodules
```shell
git clone https://github.com/nihui/realcugan-ncnn-vulkan.git
cd realcugan-ncnn-vulkan
git submodule update --init --recursive
```
3. Build with CMake
- You can pass -DUSE_STATIC_MOLTENVK=ON option to avoid linking the vulkan loader library on MacOS
```shell
mkdir build
cd build
cmake ../src
cmake --build . -j 4
```
## Sample Images
### Original Image
### Upscale 2x with ImageMagick
```shell
convert origin.jpg -resize 200% output.png
```
### Upscale 2x with ImageMagick Lanczo4 Filter
```shell
convert origin.jpg -filter Lanczos -resize 200% output.png
```
### Upscale 2x with Real-CUGAN
```shell
realcugan-ncnn-vulkan.exe -i origin.jpg -o output.png -s 2 -n 1 -x
```
## Original Real-CUGAN Project
- https://github.com/bilibili/ailab/tree/main/Real-CUGAN
## Other Open-Source Code Used
- https://github.com/Tencent/ncnn for fast neural network inference on ALL PLATFORMS
- https://github.com/webmproject/libwebp for encoding and decoding Webp images on ALL PLATFORMS
- https://github.com/nothings/stb for decoding and encoding image on Linux / MacOS
- https://github.com/tronkko/dirent for listing files in directory on Windows