eye-color

Maintainer: juergengunz

Total Score

2

Last updated 5/7/2024
AI model preview image
PropertyValue
Run this modelRun on Replicate
API specView on Replicate
Github linkNo Github link provided
Paper linkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The eye-color model allows you to modify the color of the eyes (iris) in an image. This can be useful for tasks like image editing, character design, or creating stylized portraits. Compared to similar models like ultimate-portrait-upscale, real-esrgan, and become-image, the eye-color model focuses specifically on adjusting the eye color rather than more general image manipulation or upscaling.

Model inputs and outputs

The eye-color model takes an input image and several parameters to adjust the eye color, including red, green, blue, and alpha (blending) values, as well as hue shift and blur radius. The output is a new image with the eyes modified according to the specified color settings.

Inputs

  • Image: The input image to modify
  • Red, Green, Blue: The desired RGB color values for the eyes
  • Alpha: The alpha value for blending the eye color
  • Hue Shift: Adjusts the hue of the eye color
  • Blur Radius: Applies a blur to the eye color

Outputs

  • Output Image: The modified image with the new eye color

Capabilities

The eye-color model can be used to quickly and easily change the color of the eyes in an image. This can be useful for a variety of applications, such as character design, photo editing, or creating stylized portraits. The model allows for fine-tuning of the eye color, including adjusting the hue, saturation, and blur, to achieve the desired look.

What can I use it for?

The eye-color model can be a valuable tool for artists, designers, and content creators who need to modify the appearance of eyes in their work. For example, you could use it to create custom character designs with unique eye colors, or to enhance the eyes in portrait photos. The model could also be integrated into image editing workflows or used to generate stock images with a range of eye colors.

Things to try

One interesting thing to try with the eye-color model is experimenting with different color combinations and settings to create unique and unexpected eye looks. You could also try combining the eye-color model with other image manipulation tools or AI models, such as marigold for depth estimation or gfpgan for face restoration, to create even more sophisticated and polished results.



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-v2

juergengunz

Total Score

485

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.

Read more

Updated Invalid Date

AI model preview image

ultimate-portrait-upscale

juergengunz

Total Score

31

ultimate-portrait-upscale is a powerful AI model developed by juergengunz that specializes in upscaling and enhancing portrait images. This model builds upon similar tools like high-resolution-controlnet-tile, real-esrgan, gfpgan, and controlnet-tile, offering advanced features for creating stunning, photorealistic portrait upscales. Model inputs and outputs ultimate-portrait-upscale takes in a portrait image and various configuration parameters to fine-tune the upscaling process. It then generates a high-quality, upscaled version of the input image. Inputs Image**: The input portrait image Positive Prompt**: A text description to guide the upscaling process towards a desired aesthetic Negative Prompt**: A text description to avoid certain undesirable elements in the output Upscale By**: The factor by which to upscale the input image Upscaler**: The specific upscaling method to use Seed**: A random seed value to ensure reproducibility Steps**: The number of iterative refinement steps to perform Denoise**: The amount of noise reduction to apply Scheduler**: The algorithm used to schedule the sampling process Sampler Name**: The specific sampling algorithm to use Controlnet Strength**: The strength of the ControlNet guidance Use Controlnet Tile**: Whether to use the ControlNet tile feature Outputs Upscaled Portrait Image**: The high-quality, upscaled version of the input portrait Capabilities ultimate-portrait-upscale is capable of generating stunning, photorealistic upscales of portrait images. It leverages advanced techniques like ControlNet guidance and tile-based processing to maintain sharp details and natural-looking textures, even when significantly increasing the resolution. What can I use it for? This model is a great tool for enhancing portrait photography, creating high-quality assets for design or advertising, and improving the visual quality of AI-generated portraits. It can be particularly useful for businesses or individuals who need to produce professional-grade portrait images for their products, marketing materials, or other applications. Things to try Experiment with different combinations of prompts, upscaling factors, and ControlNet settings to achieve unique and creative results. You can also try applying additional post-processing techniques, such as face correction or style transfer, to further refine the upscaled portraits.

Read more

Updated Invalid Date

AI model preview image

realisitic-vision-v3-image-to-image

mixinmax1990

Total Score

76

The realisitic-vision-v3-image-to-image model is a powerful AI-powered tool for generating high-quality, realistic images from input images and text prompts. This model is part of the Realistic Vision family of models created by mixinmax1990, which also includes similar models like realisitic-vision-v3-inpainting, realistic-vision-v3, realistic-vision-v2.0-img2img, realistic-vision-v5-img2img, and realistic-vision-v2.0. Model inputs and outputs The realisitic-vision-v3-image-to-image model takes several inputs, including an input image, a text prompt, a strength value, and a negative prompt. The model then generates a new output image that matches the provided prompt and input image. Inputs Image**: The input image to be used as a starting point for the generation process. Prompt**: The text prompt that describes the desired output image. Strength**: A value between 0 and 1 that controls the strength of the input image's influence on the output. Negative Prompt**: A text prompt that describes characteristics to be avoided in the output image. Outputs Output Image**: The generated output image that matches the provided prompt and input image. Capabilities The realisitic-vision-v3-image-to-image model is capable of generating highly realistic and detailed images from a variety of input sources. It can be used to create portraits, landscapes, and other types of scenes, with the ability to incorporate specific details and styles as specified in the text prompt. What can I use it for? The realisitic-vision-v3-image-to-image model can be used for a wide range of applications, such as creating custom product images, generating concept art for games or films, and enhancing existing images. It could also be used in the field of digital art and photography, where users can experiment with different styles and techniques to create unique and visually appealing images. Things to try One interesting aspect of the realisitic-vision-v3-image-to-image model is its ability to blend the input image with the desired prompt in a seamless and natural way. Users can experiment with different combinations of input images and prompts to see how the model responds, exploring the limits of its capabilities and creating unexpected and visually striking results.

Read more

Updated Invalid Date

AI model preview image

deoldify_image

arielreplicate

Total Score

397

The deoldify_image model from maintainer arielreplicate is a deep learning-based AI model that can add color to old black-and-white images. It builds upon techniques like Self-Attention Generative Adversarial Network and Two Time-Scale Update Rule, and introduces a novel "NoGAN" training approach to achieve high-quality, stable colorization results. The model is part of the DeOldify project, which aims to colorize and restore old images and film footage. It offers three variants - "Artistic", "Stable", and "Video" - each optimized for different use cases. The Artistic model produces the most vibrant colors but may leave important parts of the image gray, while the Stable model is better suited for natural scenes and less prone to leaving gray human parts. The Video model is optimized for smooth, consistent and flicker-free video colorization. Model inputs and outputs Inputs model_name**: Specifies which model to use - "Artistic", "Stable", or "Video" input_image**: The path to the black-and-white image to be colorized render_factor**: Determines the resolution at which the color portion of the image is rendered. Lower values render faster but may result in less vibrant colors, while higher values can produce more detailed results but may wash out the colors. Outputs The colorized version of the input image, returned as a URI. Capabilities The deoldify_image model can produce high-quality, realistic colorization of old black-and-white images, with impressive results on a wide range of subjects like historical photos, portraits, landscapes, and even old film footage. The use of the "NoGAN" training approach helps to eliminate common issues like flickering, glitches, and inconsistent coloring that plagued earlier colorization models. What can I use it for? The deoldify_image model can be a powerful tool for breathtaking photo restoration and enhancement projects. It could be used to bring historical images to life, add visual interest to old family photos, or even breathe new life into classic black-and-white films. Potential applications include historical archives, photo sharing services, film restoration, and more. Things to try One interesting aspect of the deoldify_image model is that it seems to have learned some underlying "rules" about color based on subtle cues in the black-and-white images, resulting in remarkably consistent and deterministic colorization decisions. This means the model can produce very stable, flicker-free results even when coloring moving scenes in video. Experimenting with different input images, especially ones with unique or challenging elements, could yield fascinating insights into the model's inner workings.

Read more

Updated Invalid Date