Awesome

BasicSR (Enhanced)

This is a fork of victorca25's BasicSR branch. Most of the documentation is there if you need any information regarding BasicSR. This readme will focus specifically on the differences of this fork.

Table of Contents

1. Dependencies
2. Features
3. To Do

Dependencies

• Minimum PyTorch 1.5, PyTorch 1.6 (Preview) required if you want to use AMP for training.
• All BasicSR dependencies as documented at victorca25's branch.

Optional Dependencies

• ImageMagick for the image manipulation library. Manually install IM dev library, or just get the library via apt-get install libmagickwand-dev
• Python package: pip install wand, to access IM from Python.

New feature : Automatic Mixed Precision (AMP)

Implemented AMP, which will automatically cast tensors to 16-bit floating point depending on usage. The reason for this is for the newer Volta/Turing card to take advantage of their Tensor Cores. Testing this feature shows a speed-up of about ~50% during training. You can read more about AMP at nvidia's dev site.

Features

These features are configured in the training .yml file. Because of the nature of the changes, set training mode to LRHROTF beforehand. Using any other modes will behave as the original branch.

Load state via CPU

• Lower end graphics card with low VRAM may have difficulty resuming from a state. If you get a out of memory error when continuing a training session, then set load2CPU: true so that it is loaded to the system RAM instead.

Image transformation

• Random flipping, 90 degree rotate and HR rotate are all independent from each other, and can be applied together.

Revamped HR transform workflow

Currently only usable with LRHROTF mode only.

• When training with no LR data sources set, transformations are done only on the HR tile and LR tile are only auto-generated at the last step.
• If hr_downscale: true is set, large dataset images are randomly downscaled before cropping to the training tile size. This also applies to the LR dataset if same-scale training is used.
• If dataset image is smaller than training tile size, then it is automatically padded to the proper size with a random colour. This is different from original branch which scales the tile up, thus potentially compromising image quality.
• If hr_rrot: true is set, a different image rotate function is used which does not scale up the result. This function is used in conjunction with cropping, so the image tile is built directly from the dataset image.

• Since the tile cropping happens at the same time as rotation, and will be applied to a downscaled input image if HR downscale is used as well. If all transformations are used in tandem, the results in a highly robust model with a low possibility of over-fitting. The downside is takes longer for the model to take shape.

Enhanced LR noises

• imdither uses Imagemagick's dither engine to create more colour-accurate ordered dithering. Unlike the default ordered dither noise, this produces more random varying levels of colour depth that may help represent the original image colours more accurately. A noticeable trend when using dither to train models was that the colour contrast slowly declined over time, which is due to the extreme colours in the generated image being mapped to less vibrant colours. Even when using low colour depth, imdither has slightly better colour assignment. This approach emulates how the Fatality model's undithering training is done. As a bonus, it requires less processing time than the normal dithering method. By default, the higher colour depth is clamped out. You can reenable it by increasing the colour depth in scripts/augmentations.py if required.

• imrandither uses Imagemagick's dither engine to create mapped scattered dithering. Main difference between the implemention in the original dither noise is that it allows for higher colour depth, and adds Riemersma dither apart from Floyd-Steinberg. Care must be taken place when enabling this noise because the result may be almost similar to how some pixel art portray detail. Use only if you need extra denoising & blending strength.

• imquantize is similar to the standard quantize noise, except has better colour accuracy at higher colour depths. Used to train your model to blend. Also helps with antialiasing of sharp diagonals.

• imkuwahara uses Imagemagick's Kuwahara filter that basically reduces fine details in the image by simplifying to a general shape. This theoretically helps to train inpainting, though it is recommended to be used sparringly because of its tendency to create artifacts.

• imtonephoto uses Imagemagick's dither engine to simulate screen angle tones used in printing. Use if you want to train a de-toner model.

• imtonecomic is same as above, except the black channel is not screentoned. Use to emulate how old comics were printed via colour-seperation.

New LR downscale types

• 123 will use Imagemagick's RGB scale, which supposedly maintains contrast when downscaling.
• 420 will use Imagemagick's liquid scale, which in theory has no use whatsoever. However in practice, it forces the model to keep certain details while blurring out all other. Use only if one needs to get high.

To Do list:

• Migrate to BasicSR Lite fork. DONE.
• Update PyTorch 1.6 compatibility, and creation of BasicSR-AMP branch. DONE.

Additional Help

If you have any questions, we have a discord server where you can ask them and a Wiki with more information.

Acknowledgement

• Big thanks to victorca25 for encouraging the creation of this fork.
• Thanks to Twittman for sharing how Fatality's training tiles and screentones were generated.
• Thanks to BlueAmulet for his various bug fixes to BasicSR.
• Thanks to JoeyBallentine for his fixes as well.
• Code architecture is inspired by pytorch-cyclegan.
• Thanks to Wai Ho Kwok, who contributes to the initial version.

BibTex

@InProceedings{wang2018esrgan,
    author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
    title = {ESRGAN: Enhanced super-resolution generative adversarial networks},
    booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},
    month = {September},
    year = {2018}
}
@InProceedings{wang2018sftgan,
    author = {Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},
    title = {Recovering realistic texture in image super-resolution by deep spatial feature transform},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2018}
}
@article{Hui-PPON-2019,
    title={Progressive Perception-Oriented Network for Single Image Super-Resolution},
    author={Hui, Zheng and Li, Jie and Gao, Xinbo and Wang, Xiumei},
    booktitle={arXiv:1907.10399v1},
    year={2019}
}
@InProceedings{Liu2019abpn,
    author = {Liu, Zhi-Song and Wang, Li-Wen and Li, Chu-Tak and Siu, Wan-Chi},
    title = {Image Super-Resolution via Attention based Back Projection Networks},
    booktitle = {IEEE International Conference on Computer Vision Workshop(ICCVW)},
    month = {October},
    year = {2019}
}