Real-ESRGAN

Install

Real-ESRGAN needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreRealESRGANx4-restore.pt")result = model(SAMPLE_IMAGE, save=True) restored = result.restoredprint(restored.array.shape, restored.array.dtype)

libreyolo predict model=LibreRealESRGANx4-restore.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

from libreyolo import LibreYOLO model = LibreYOLO("LibreRealESRGANx4-restore.pt") # tile splits the forward pass into overlapping tiles and blends the# seams back together; tile_pad is the halo added around each tile# before it is cropped back out. Both are Python-only keyword# arguments, not CLI flags.result = model("large-photo.jpg", tile=512, tile_pad=10, save=True)

A restore result carries no boxes; result.restored is a dense (H, W, 3) uint8 RGB image, on a canvas Results.restore_scale times the input in each dimension. save=True writes that image directly rather than an annotated plot. Input is converted to RGB and any alpha channel is dropped. A source larger than memory allows can be split with tile and tile_pad, which blend the tile seams back together in the output. See prediction for sources, streaming and result handling.

Variants

Three checkpoints, named for their upscale factor. x4 is RRDBNet (RealESRGAN_x4plus), 23 residual-in-residual dense blocks, the quality default at 4x. x2 is the same RRDBNet architecture at 2x. x4t is SRVGGNetCompact (realesr-general-x4v3), a smaller, faster generator built for video and lower-latency use at 4x. The upstream general-purpose model also ships a paired denoise-strength network blended in at inference time; that strength knob is not part of this port, which runs the base x4t generator.

Validate

val() measures PSNR and SSIM between the restored output and a clean target image, both computed in RGB on the original canvas with no border crop and no resizing. SSIM uses an 11x11 Gaussian window with sigma 1.5, averaged over the three color channels.

from libreyolo import LibreYOLO model = LibreYOLO("LibreRealESRGANx4-restore.pt")metrics = model.val(data="my-restore-dataset.yaml") print(metrics["metrics/PSNR"])print(metrics["metrics/SSIM"])

libreyolo val model=LibreRealESRGANx4-restore.pt data=my-restore-dataset.yaml

The dataset argument is a YAML pairing a directory of degraded input images with a directory of clean target images of matching resolution; see dataset formats for the exact keys.

Export

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results. Export lists the arguments every format accepts and the extras a few of them add.

from libreyolo import LibreYOLO model = LibreYOLO("LibreRealESRGANx4-restore.pt") # imgsz defaults to a small internal patch size when omitted, not# your working resolution, so pass the size your deployment actually# feeds the model.model.export(format="onnx", imgsz=512)model.export(format="tensorrt", imgsz=512, half=True)

libreyolo export model=LibreRealESRGANx4-restore.pt format=onnx imgsz=512

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreRealESRGANx4-restore.onnx")result = model(SAMPLE_IMAGE) print(result.restored.array.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@InProceedings{wang2021realesrgan,
    author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
    title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
    booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
    date      = {2021}
}