real-esrgan-nitroviper
Maintainer: nicholascelestin
5
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The real-esrgan-nitroviper model is a variation of the Real-ESRGAN upscaling model, developed by the maintainer nicholascelestin. While this model is currently marked as "Broken - Only Public For API Usage & Debugging", it is similar to other Real-ESRGAN models like the one created by nightmareai, which can perform high-quality image upscaling with optional face enhancement.
Model inputs and outputs
The real-esrgan-nitroviper model takes in an image and allows the user to specify the upscaling factor as well as whether to enable face enhancement. The output is a high-resolution version of the input image.
Inputs
- image: The original input image
- model: The specific model to use, defaulting to "RealESRGAN_x4plus"
- scale: The upscale factor, defaulting to 4
- face_enhance: Whether to enable face enhancement, defaulting to false
Outputs
- Output: The upscaled and potentially face-enhanced image
Capabilities
The real-esrgan-nitroviper model can perform high-quality image upscaling, preserving details and sharpness. When the face enhancement option is enabled, the model can also improve the appearance of faces in the image.
What can I use it for?
The real-esrgan-nitroviper model could be useful for a variety of image enhancement tasks, such as improving the resolution of low-quality images or touching up portraits. Similar models like real-esrgan and classic-anim-diffusion can also be used for image upscaling and animation generation.
Things to try
While this specific model is marked as broken, exploring other Real-ESRGAN models can be a great way to enhance the resolution and quality of your images. Experimenting with different upscaling factors and face enhancement settings can help you achieve the desired results for your project.
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
glid-3
3
glid-3 is a combination of OpenAI's GLIDE, Latent Diffusion, and CLIP. It uses the same text conditioning as GLIDE, but instead of training a new text transformer, it uses the existing one from OpenAI CLIP. Instead of upsampling, it does diffusion in the latent diffusion space and adds classifier-free guidance. Similar models include glid-3-xl-stable, which has more powerful in-painting and out-painting capabilities, and glid-3-xl, which is a CompVis latent-diffusion text2im model fine-tuned for inpainting. Another related model is icons, which is fine-tuned to generate slick icons and flat pop constructivist graphics. The well-known stable-diffusion is also a similar latent text-to-image diffusion model. Model inputs and outputs glid-3 takes in a text prompt and outputs a generated image. The model can generate images quickly, though the image quality may not be ideal as the model is still a work in progress. Inputs Prompt**: The text prompt describing the image you want to generate. Negative**: An optional negative prompt to guide the model away from generating certain elements. Batch Size**: The number of images to generate at once, up to 20. Outputs Array of image URLs**: The generated images, returned as an array of image URLs. Capabilities glid-3 can generate a wide variety of photographic images based on text prompts. While it may not work as well for illustrations or artwork, it can create compelling images of scenes, objects, and people described in the prompt. What can I use it for? You can use glid-3 to quickly generate images for various applications, such as marketing materials, blog posts, social media, or even as a creative tool for ideation. The model's ability to translate text into visual concepts can be a powerful asset for content creators and designers. Things to try One interesting aspect of glid-3 is its use of latent diffusion, which allows for more efficient generation compared to upsampling approaches. You could experiment with different prompts and techniques, such as using classifier-free guidance, to see how it affects the quality and creativity of the generated images.
Updated Invalid Date
real-esrgan-v2
486
The real-esrgan-v2 model is an AI-powered image upscaling tool created by maintainer juergengunz. It builds upon the popular Real-ESRGAN model, which is known for its ability to enhance images with AI-driven face correction. Similar models include real-esrgan by nightmareai, ultimate-portrait-upscale by juergengunz, and real-esrgan by lucataco. Model inputs and outputs The real-esrgan-v2 model takes an image as input and provides an upscaled and enhanced version of that image as output. Users can control various parameters like the scale factor and whether to enhance the eyes, face, or mouth. Inputs image**: The input image to be upscaled scale**: The factor to scale the image by, up to 2x enhance_eyes**: Whether to enhance the eyes in the image face_enhance**: Whether to enhance the face in the image enhance_mouth**: Whether to enhance the mouth in the image Outputs Output**: The upscaled and enhanced output image Capabilities The real-esrgan-v2 model is capable of significantly improving the quality and detail of images through its powerful upscaling and enhancement capabilities. It can produce visually stunning results, especially for portraits and other images with prominent facial features. What can I use it for? The real-esrgan-v2 model can be useful for a variety of applications, such as enhancing low-resolution images for use in marketing materials, creating high-quality images for social media, or improving the visual quality of images used in presentations or publications. Businesses could potentially use it to improve the visual impact of their digital content. Photographers and digital artists may also find it helpful for enhancing their work. Things to try One interesting aspect of the real-esrgan-v2 model is its ability to selectively enhance specific facial features like the eyes and mouth. This could be useful for creating more dramatic or striking portraits, or for emphasizing particular aspects of a subject's appearance. Experimenting with the different enhancement options could lead to some unique and creative results.
Updated Invalid Date
real-esrgan
53.3K
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.
Updated Invalid Date
real-esrgan-xxl-images
1
real-esrgan-xxl-images is an image upscaling model developed by sfarrowgr. It is designed to significantly enlarge images, with capabilities that go beyond those of similar models like real-esrgan, upscaler, gfpgan, and ultimate-sd-upscale. Model inputs and outputs This model takes an image as input and outputs an upscaled version of that image. The input image can be scaled up by a factor of up to 16x, and the model also supports an optional face enhancement feature. Inputs Image**: The input image to be upscaled Scale**: The factor to scale the image by, up to 16x Face Enhance**: A boolean flag to enable or disable face enhancement Outputs Output**: The upscaled and (optionally) face-enhanced image Capabilities real-esrgan-xxl-images is capable of dramatically increasing the resolution of images while maintaining high image quality. It can be used to enlarge low-resolution images, such as those captured by older devices or downloaded from the web, transforming them into high-quality, detailed versions. What can I use it for? The real-esrgan-xxl-images model can be useful for a variety of applications, such as enhancing product images for e-commerce, improving the quality of images used in marketing materials, or upscaling personal photos. By leveraging the power of this model, you can create high-resolution images that are perfect for printing, large-scale displays, or detailed digital analysis. Things to try Experiment with different scaling factors to find the optimal balance between image quality and file size. Additionally, try enabling the face enhancement feature to see how it can improve the appearance of portraits and other images with prominent faces.
Updated Invalid Date