Sketch to Color Image Generation Using Conditional GANs

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Sketch to Color Image Generation using Conditional GANs

Sketch to Color Image generation is an image-to-image translation model using Conditional Generative Adversarial Networks as described in the original paper by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros 2016, Image-to-Image Translation with Conditional Adversarial Networks.

The model was trained on the Anime Sketch-Colorization Pair Dataset available on Kaggle which contains 14.2k pairs of Sketch-Color Anime Images. The training of the model was done for 150 epochs which took approximately 23 hours on a single GeForce GTX 1060 6GB Graphic Card and 16 GB RAM.

Table of Contents

  1. Requirements
  2. Usage
  3. Future Works
  4. Credits
  5. Send Me Queries or Feedback
  6. License


To download and use this code, the minimum requirements are:


Once the requirements are checked, you can easily download this project and use it on your machine in few simple steps.


1 - You will have to change the path to dataset as per your machine environment on line #12. You can download the dataset from Kaggle at

2 - GANs are resource intensive models so if you run into OutOfMemory or such erros, try customizing the variables as per your needs available from line #15 to #19

Future Works

I’ve been working on GANs for a lot of time and planning to continue exploring the field for further applications and research work. Some of the points that I think this project can grow or be a base for are listed below.

  1. Trying different databases to get an idea of preprocessing different types of images and building models specific to those input image types.
  2. This is a project applied on individual Image to Image translation. Further the model can be used to process black and white sketch video frames to generate colored videos.
  3. Converting the model from HDF5 to json and building interesting web apps using TensorFlow.Js.


Send Me Queries or Feedback

It is all opensource and for further research purpose, so contact me if you…

  1. …have some valuable inputs to share
  2. …want to work on it
  3. …find any bug
  4. …want to share a feedback on the work
  5. …etc.

Send me a mail at or create a new Issue on this repository. You can also contact me through my LinkedIn Profile.


This project is freely available for free non-commercial/ commercial use, and may be redistributed under these conditions. Please, see the license for further details.