real-esrgan

Maintainer: cjwbw

Total Score

1.5K

Last updated 5/23/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

Get summaries of the top AI models delivered straight to your inbox:

Model overview

real-esrgan is an AI model developed by the creator cjwbw that focuses on real-world blind super-resolution. This means the model can upscale low-quality images without relying on a reference high-quality image. In contrast, similar models like real-esrgan and realesrgan also offer additional features like face correction, while seesr and supir incorporate semantic awareness and language models for enhanced image restoration.

Model inputs and outputs

real-esrgan takes an input image and an upscaling factor, and outputs a higher-resolution version of the input image. The model is designed to work well on a variety of real-world images, even those with significant noise or artifacts.

Inputs

  • Image: The input image to be upscaled

Outputs

  • Output Image: The upscaled version of the input image

Capabilities

real-esrgan excels at enlarging low-quality images while preserving details and reducing artifacts. This makes it useful for tasks such as enhancing photos, improving video resolution, and restoring old or damaged images.

What can I use it for?

real-esrgan can be used in a variety of applications where high-quality image enlargement is needed, such as photography, video editing, digital art, and image restoration. For example, you could use it to upscale low-resolution images for use in marketing materials, or to enhance old family photos. The model's ability to handle real-world images makes it a valuable tool for many image-related projects.

Things to try

One interesting aspect of real-esrgan is its ability to handle a wide range of input image types and qualities. Try experimenting with different types of images, such as natural scenes, portraits, or even text-heavy images, to see how the model performs. Additionally, you can try adjusting the upscaling factor to find the right balance between quality and file size for your specific use case.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

AI model preview image

real-esrgan

nightmareai

Total Score

45.8K

real-esrgan is a practical image restoration model developed by researchers at the Tencent ARC Lab and Shenzhen Institutes of Advanced Technology. It aims to tackle real-world blind super-resolution, going beyond simply enhancing image quality. Compared to similar models like absolutereality-v1.8.1, instant-id, clarity-upscaler, and reliberate-v3, real-esrgan is specifically focused on restoring real-world images and videos, including those with face regions. Model inputs and outputs real-esrgan takes an input image and outputs an upscaled and enhanced version of that image. The model can handle a variety of input types, including regular images, images with alpha channels, and even grayscale images. The output is a high-quality, visually appealing image that retains important details and features. Inputs Image**: The input image to be upscaled and enhanced. Scale**: The desired scale factor for upscaling the input image, typically between 2x and 4x. Face Enhance**: An optional flag to enable face enhancement using the GFPGAN model. Outputs Output Image**: The restored and upscaled version of the input image. Capabilities real-esrgan is capable of performing high-quality image upscaling and restoration, even on challenging real-world images. It can handle a variety of input types and produces visually appealing results that maintain important details and features. The model can also be used to enhance facial regions in images, thanks to its integration with the GFPGAN model. What can I use it for? real-esrgan can be useful for a variety of applications, such as: Photo Restoration**: Upscale and enhance low-quality or blurry photos to create high-resolution, visually appealing images. Video Enhancement**: Apply real-esrgan to individual frames of a video to improve the overall visual quality and clarity. Anime and Manga Upscaling**: The RealESRGAN_x4plus_anime_6B model is specifically optimized for anime and manga images, producing excellent results. Things to try Some interesting things to try with real-esrgan include: Experiment with different scale factors to find the optimal balance between quality and performance. Combine real-esrgan with other image processing techniques, such as denoising or color correction, to achieve even better results. Explore the model's capabilities on a wide range of input images, from natural photographs to detailed illustrations and paintings. Try the RealESRGAN_x4plus_anime_6B model for enhancing anime and manga-style images, and compare the results to other upscaling solutions.

Read more

Updated Invalid Date

AI model preview image

rudalle-sr

cjwbw

Total Score

470

The rudalle-sr model is a real-world blind super-resolution model based on the Real-ESRGAN architecture, which was created by Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. This model has been retrained on the ruDALL-E dataset by cjwbw from Replicate. The rudalle-sr model is capable of upscaling low-resolution images with impressive results, producing high-quality, photo-realistic outputs. Model inputs and outputs The rudalle-sr model takes a single input - an image file - and an optional upscaling factor. The model can upscale the input image by a factor of 2, 3, or 4, producing a higher-resolution output image. Inputs Image**: The input image to be upscaled Outputs Output Image**: The upscaled, high-resolution version of the input image Capabilities The rudalle-sr model is capable of producing high-quality, photo-realistic upscaled images from low-resolution inputs. It can effectively handle a variety of image types and scenes, making it a versatile tool for tasks like image enhancement, editing, and content creation. What can I use it for? The rudalle-sr model can be used for a wide range of applications, such as improving the quality of low-resolution images for use in digital art, photography, web design, and more. It can also be used to upscale images for printing or display on high-resolution devices. Additionally, the model can be integrated into various image processing pipelines or used as a standalone tool for enhancing visual content. Things to try With the rudalle-sr model, you can experiment with upscaling a variety of image types, from portraits and landscapes to technical diagrams and artwork. Try adjusting the upscaling factor to see the impact on the output quality, and explore how the model handles different types of image content and detail.

Read more

Updated Invalid Date

AI model preview image

esrgan

xinntao

Total Score

74

The esrgan model is an image super-resolution model that can upscale low-resolution images by 4x. It was developed by researchers at Tencent and the Chinese Academy of Sciences, and is an enhancement of the SRGAN model. The esrgan model uses a deeper neural network architecture called Residual-in-Residual Dense Blocks (RRDB) without batch normalization layers, which helps it achieve superior performance compared to previous models like SRGAN. It also employs the Relativistic average GAN loss function and improved perceptual loss to further boost image quality. The esrgan model can be seen as a more advanced version of the Real-ESRGAN model, which is a practical algorithm for real-world image restoration that can also remove JPEG compression artifacts. The Real-ESRGAN model extends the original esrgan with additional features and improvements. Model inputs and outputs Inputs Image**: A low-resolution input image that the model will upscale by 4x. Outputs Image**: The output of the model is a high-resolution image that is 4 times the size of the input. Capabilities The esrgan model can effectively upscale low-resolution images while preserving important details and textures. It outperforms previous state-of-the-art super-resolution models on standard benchmarks like Set5, Set14, and BSD100 in terms of both PSNR and perceptual quality. The model is particularly adept at handling complex textures and details that can be challenging for other super-resolution approaches. What can I use it for? The esrgan model can be useful for a variety of applications that require high-quality image upscaling, such as enhancing old photos, improving the resolution of security camera footage, or generating high-res images from low-res inputs for graphic design and media production. Companies could potentially use the esrgan model to improve the visual quality of their products or services, such as by upscaling product images on an ecommerce site or enhancing the resolution of user-generated content. Things to try One interesting aspect of the esrgan model is its network interpolation capability, which allows you to smoothly transition between the high-PSNR and high-perceptual quality versions of the model. By adjusting the interpolation parameter, you can find the right balance between visual fidelity and objective image quality metrics to suit your specific needs. This can be a powerful tool for fine-tuning the model's performance for different use cases.

Read more

Updated Invalid Date

AI model preview image

realesrgan

lqhl

Total Score

12

realesrgan is an AI model for image restoration and face enhancement. It was created by Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan from the Tencent ARC Lab and Shenzhen Institutes of Advanced Technology. realesrgan extends the powerful ESRGAN model to a practical restoration application, training on pure synthetic data. It aims to develop algorithms for general image and video restoration. realesrgan can be contrasted with similar models like GFPGAN, which focuses on restoring real-world faces, and real-esrgan, which adds optional face correction and adjustable upscaling to the base realesrgan model. Model inputs and outputs realesrgan takes an input image and can output an upscaled and enhanced version of that image. The model supports arbitrary upscaling factors using the --outscale argument. It can also optionally perform face enhancement using the --face_enhance flag, which integrates the GFPGAN model for improved facial details. Inputs img**: The input image to be processed tile**: The tile size to use for processing. Setting this to a non-zero value can help with GPU memory issues, but may introduce some artifacts. scale**: The upscaling factor to apply to the input image. version**: The specific version of the realesrgan model to use, such as the general "RealESRGAN_x4plus" or the anime-optimized "RealESRGAN_x4plus_anime_6B". face_enhance**: A boolean flag to enable face enhancement using the GFPGAN model. Outputs The upscaled and enhanced output image. Capabilities realesrgan can effectively restore and enhance a variety of image types, including natural scenes, anime illustrations, and faces. It is particularly adept at upscaling low-resolution images while preserving details and reducing artifacts. The model's face enhancement capabilities can also improve the appearance of faces in images, making them appear sharper and more natural. What can I use it for? realesrgan can be a valuable tool for a wide range of image processing and enhancement tasks. For example, it could be used to upscale low-resolution images for use in presentations, publications, or social media. The face enhancement capabilities could also be leveraged to improve the appearance of portraits or AI-generated faces. Additionally, realesrgan could be integrated into content creation workflows, such as anime or video game development, to enhance the quality of in-game assets or animated scenes. Things to try One interesting aspect of realesrgan is its ability to handle a wide range of input image types, including those with alpha channels or grayscale. Experimenting with different input formats and the --outscale parameter can help you find the best configuration for your specific needs. Additionally, the model's performance can be tuned by adjusting the --tile size, which can be particularly useful when dealing with high-resolution or memory-intensive images.

Read more

Updated Invalid Date