bigcolor
Maintainer: cjwbw
423
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
bigcolor is a novel colorization model developed by Geonung Kim et al. that provides vivid colorization for diverse in-the-wild images with complex structures. Unlike previous generative priors that struggle to synthesize image structures and colors, bigcolor learns a generative color prior to focus on color synthesis given the spatial structure of an image. This allows it to expand its representation space and enable robust colorization for diverse inputs. bigcolor is inspired by the BigGAN architecture, using a spatial feature map instead of a spatially-flattened latent code to further enlarge the representation space. The model supports arbitrary input resolutions and provides multi-modal colorization results, outperforming existing methods especially on complex real-world images.
Model inputs and outputs
bigcolor takes a grayscale input image and produces a colorized output image. The model can operate in different modes, including "Real Gray Colorization" for real-world grayscale photos, and "Multi-modal" colorization using either a class vector or random vector to produce diverse colorization results.
Inputs
- image: The input grayscale image to be colorized.
- mode: The colorization mode, either "Real Gray Colorization" or "Multi-modal" using a class vector or random vector.
- classes (optional): A space-separated list of class IDs for multi-modal colorization using a class vector.
Outputs
- ModelOutput: An array containing one or more colorized output images, depending on the selected mode.
Capabilities
bigcolor is capable of producing vivid and realistic colorizations for diverse real-world images, even those with complex structures. It outperforms previous colorization methods, especially on challenging in-the-wild scenes. The model's multi-modal capabilities allow users to generate diverse colorization results from a single input.
What can I use it for?
bigcolor can be used for a variety of applications that require realistic and vivid colorization of grayscale images, such as photo editing, visual effects, and artistic expression. Its robust performance on complex real-world scenes makes it particularly useful for tasks like colorizing historical photos, enhancing black-and-white movies, or bringing old artwork to life. The multi-modal capabilities also open up creative opportunities for artistic exploration and experimentation.
Things to try
One interesting aspect of bigcolor is its ability to generate multiple colorization results from a single input by leveraging either a class vector or a random vector. This allows users to explore different color palettes and stylistic interpretations of the same image, which can be useful for creative projects or simply finding the most visually appealing colorization. Additionally, the model's support for arbitrary input resolutions makes it suitable for a wide range of use cases, from small thumbnails to high-resolution images.
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