Last Updated on March 6, 2023
In Operation
We evaluated the software mostly with the Python script as the portable executable file can add block inconsistencies.
Here are the available flags.
usage: inference_realesrgan.py [-h] [-i INPUT] [-n MODEL_NAME] [-o OUTPUT] [-dn DENOISE_STRENGTH] [-s OUTSCALE]
[--model_path MODEL_PATH] [--suffix SUFFIX] [-t TILE] [--tile_pad TILE_PAD] [--pre_pad PRE_PAD]
[--face_enhance] [--fp32] [--alpha_upsampler ALPHA_UPSAMPLER] [--ext EXT] [-g GPU_ID]
options:
-h, --help show this help message and exit
-i INPUT, --input INPUT
Input image or folder
-n MODEL_NAME, --model_name MODEL_NAME
Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus |
realesr-animevideov3 | realesr-general-x4v3
-o OUTPUT, --output OUTPUT
Output folder
-dn DENOISE_STRENGTH, --denoise_strength DENOISE_STRENGTH
Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. Only used for the realesr-
general-x4v3 model
-s OUTSCALE, --outscale OUTSCALE
The final upsampling scale of the image
--model_path MODEL_PATH
[Option] Model path. Usually, you do not need to specify it
--suffix SUFFIX Suffix of the restored image
-t TILE, --tile TILE Tile size, 0 for no tile during testing
--tile_pad TILE_PAD Tile padding
--pre_pad PRE_PAD Pre padding size at each border
--face_enhance Use GFPGAN to enhance face
--fp32 Use fp32 precision during inference. Default: fp16 (half precision).
--alpha_upsampler ALPHA_UPSAMPLER
The upsampler for the alpha channels. Options: realesrgan | bicubic
--ext EXT Image extension. Options: auto | jpg | png, auto means using the same extension as inputs
-g GPU_ID, --gpu-id GPU_ID
gpu device to use (default=None) can be 0,1,2 for multi-gpu
As you can see there are 6 pre-trained models included. And we can use GFPGAN to enhance images for face restoration. There is also GPU support, upsampling, and denoise support.
- RealESRGAN_x4plus – For anime images (real-life video upscaling);
- RealESRNet_x4plus – a model trained on the DIV2K dataset;
- RealESRGAN_x4plus_anime_6B – optimized for anime images with much smaller model size
- RealESRGAN_x2plus
- realesr-animevideov3 – Anime video model with XS size. It’s probably the best model for anime.
- realesr-general-x4v3 – e very tiny models for general scenes
Summary
Real-ESRGAN offers good performance with admirable texture and background restoration. It’s software that requires experience to make best use, as you’ll want to use your own trained models.
It’s a popular project amassing an impressive 18k GitHub stars.
The pre-trained model for general scenes is quite limited although it still produces good results. For the current models, the software is focused on anime images and video.
Website: github.com/xinntao/Real-ESRGAN
Support:
Developer: Xintao Wang
License: BSD 3-Clause License
Real-ESRGAN is written in Python. Learn Python with our recommended free books and free tutorials.
For other useful open source apps that use machine learning/deep learning, we’ve compiled this roundup.
Pages in this article:
Page 1 – Introduction and Installation
Page 2 – In Operation and Summary