mewzoom 1.0.0

Ultra high-quality image super-resolution for purrfect pixels.

MewZoom

A family of parameter-efficient super-resolution models with cat-like vision and clarity. Pre-trained on a diverse set of high-quality images and fine-tuned with an adversarial network, MewZoom transforms your fuzzy images into crystal-clear high-resolution masterpieces with exceptional realism. In addition to upscaling images by 2X, 3X, 4X, or 8X the original size, MewZoom's degradation-aware training enables it to surgically identify and remove blur, noise, and artifacts without removing details.

Key Features

• Fast and scalable: MewZoom incorporates parameter-efficiency into the architecture requiring less parameters than models with similar performance.

• Ultra clarity: In addition to upscaling, MewZoom is trained to predict and remove various forms of degradation including blur, noise, and compression artifacts.

• Full RGB: Unlike many efficient SR models that only operate in the luminance domain, MewZoom operates within the full RGB color domain enhancing both luminance and chrominance for the best possible image quality.

Demos

View at full resolution for best results. More comparisons can be found here.

This comparison demonstrates the strength of the enhancements (deblurring, denoising, and deartifacting) applied to the upscaled image.

This comparison demonstrates the individual enhancements applied in isolation.

Pretrained Models

The latest pretrained models are available on HuggingFace Hub. They use the newer mewzoom library for inference.

Name	Upscale	Architecture	Channels	Layers	Parameters	Library Version
andrewdalpino/MewZoom-V1-2X	2X	TrunkNet	48	64	5.3M	1.x
andrewdalpino/MewZoom-V1-2X-Unet	2X	UNet	48/96/192/384	4/4/4/4	32M	1.x
andrewdalpino/MewZoom-V1-4X	4X	TrunkNet	96	64	21M	1.x
andrewdalpino/MewZoom-V1-4X-Unet	4X	UNet	96/192/384/768	4/4/4/4	128M	1.x

Legacy Models

The following legacy pretrained models are also available on HuggingFace Hub. Note that legacy models use the ultrazoom library for inference.

Name	Upscale	Channels	Layers	Parameters	Control Modules	Library Version
andrewdalpino/MewZoom-V0-2X-Ctrl	2X	48	20	1.8M	Yes	0.2.x
andrewdalpino/MewZoom-V0-3X-Ctrl	3X	54	30	3.5M	Yes	0.2.x
andrewdalpino/MewZoom-V0-4X-Ctrl	4X	96	40	14M	Yes	0.2.x
andrewdalpino/MewZoom-V0-2X	2X	48	20	1.8M	No	0.1.x
andrewdalpino/MewZoom-V0-3X	3X	54	30	3.5M	No	0.1.x
andrewdalpino/MewZoom-V0-4X	4X	96	40	14M	No	0.1.x

Example

If you'd just like to load the pretrained weights and do inference, getting started is as simple as in the example below.

First, you'll need the mewzoom package installed into your project. We'll also need the torchvision library to do some basic image preprocessing. We recommend using a virtual environment to make package management easier.

pip install mewzoom~=1.0.0 torchvision

Then, load the weights from HuggingFace Hub, convert the input image to a tensor, and upscale the image.

import torch

from torchvision.io import decode_image, ImageReadMode
from torchvision.transforms.v2 import ToDtype, ToPILImage

from mewzoom.model import MewZoom


model_name = "andrewdalpino/MewZoom-V1-2X-Unet"
image_path = "./bird.png"

model = MewZoom.from_pretrained(model_name)

image_to_tensor = ToDtype(torch.float32, scale=True)
tensor_to_pil = ToPILImage()

image = decode_image(image_path, mode=ImageReadMode.RGB)

x = image_to_tensor(image).unsqueeze(0)

y_pred = model.upscale(x)

pil_image = tensor_to_pil(y_pred.squeeze(0))

pil_image.show()

References

• A. Jolicoeur-Martineau. The Relativistic Discriminator: A Key Element Missing From Standard GAN, 2018.
• J. Yu, et al. Wide Activation for Efficient and Accurate Image Super-Resolution, 2018.
• J. Johnson, et al. Perceptual Losses for Real-time Style Transfer and Super-Resolution, 2016.
• W. Shi, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, 2016.
• T. Salimans, et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks, OpenAI, 2016.
• T. Miyato, et al. Spectral Normalization for Generative Adversarial Networks, ICLR, 2018.
• A. Kendall, et. al. Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geomtery and Semantics, 2018.
• L. Mescheder, et al. Which Training Methods for GANs do actually Converge?, PMLR 80, 2018.
• M. Heusel, et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, NIPS 2017.
• Z. Huang, et al. ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection, NeurIPS 2023.
• H. Wang, et al. Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation, 2023.
• Z. Wang, et al. RA‑Net: reverse attention for generalizing residual learning, Nature Scientific Reports, 2024.
• X. Jiang, et al. Residual Spatial and Channel Attention Networks for Single Image Dehazing, Sensors, 2024.
• A. Gomaa, et al. Residual Channel-attention (RCA) network for remote sensing image scene classification, Multimedia Tools and Applications, 2025.