BigColor, an AI model that provides colorization for natural images, presents numerous applications for technical users. One potential use case is in image restoration. By utilizing the model's generative color prior, users can accurately restore the original colors of old or damaged black and white images, bringing them back to life. Another use case for BigColor is in enhancing visual quality. Users can utilize the model to add vibrant colors to monochrome images, making them visually striking and more engaging. This could be particularly valuable in fields such as graphic design, advertising, and art curation. Additionally, the model may find practical uses in creating realistic visualizations for historical or documentary purposes, where adding color to archival photos can provide a more immersive and relatable experience. Overall, BigColor's ability to preserve original details and fine structures while providing high-quality colorization opens up a wide range of possibilities for creating compelling and visually appealing content.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Compositional Vsual Generation With Composable Diffusion Models Pytorch||$0.01155||774|
You can use this area to play around with demo applications that incorporate the Bigcolor model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
Colorization using a Generative Color Prior for Natural Images
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.0033|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||6 seconds|