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Kid coloring picture generation using AI

Snap back to childhood

Ah, coloring. This fun and simple activity has so many benefits for kids. Coloring can help little ones develop motor skills, encourage concentration, and foster creative expression. For older kids, coloring offers a chance to practice expressing themselves in different ways while having fun. It can also help to strengthen problem-solving and critical thinking skills.

One of the most enjoyable benefits of coloring is the opportunity for kids to explore different colors and color combinations. While coloring, they can experiment with different color combinations and shades, and develop their own unique palettes. Additionally, kids can learn about color theory and how different colors work together when they combine them on paper. Coloring can also be used to teach kids about the importance of color in art and design.

Overall, coloring is a great way for kids to have fun, learn, and develop their creativity and critical thinking skills. It’s also a great way for parents to bond with their children and spend quality time together.

AI for the win

Artificial Intelligence (AI) has gained attention of the researchers in the field of computer vision. Generative Adversarial Network (GAN) is part of the recent development that allows the model to generate realistic pictures with pinpoint accuracy [1]. GAN is composed of two main paths for generating the pictures and criticizing the quality of generated pictures. The main part of the GAN for generating pictures is called the generator model. Stable diffusion is part of the newly developed by a team of researchers from the University of Texas at Austin GAN architecture for image generation [2].

The stable diffusion architecture is characterized by a stable diffusion process. This process involves a series of steps, including:

1. Generating a noise vector from a random distribution.
2. Passing the noise vector through a generator network to produce an image.
3. Passing the generated image and an original image through a discriminator network to classify them as either real or fake.
4. Modifying the noise vector based on the discriminator’s output to make it more realistic.

The stable diffusion architecture has been shown to produce high-quality images and has been successfully implemented in a variety of tasks, including image super-resolution, image inpainting, and image-to-image translation.

Another interesting part of the AI model is image enhancement. Image enhancement using AI is a process of improving the visibility of an image by enhancing its contrast, brightness, or color, and improving its sharpness or resolution. AI-based image enhancement algorithms use a combination of supervised and unsupervised machine learning techniques to identify and endow features that can improve the overall image quality. AI-based image enhancement can be used to sharpen an image, make colors brighter, reduce noise, and increase the dynamic range of an image. This technology is useful for medical imaging, surveillance, satellite imagery, and other applications [3].

Proposed Solution

AI in image generation can create kid-friendly coloring pictures through a machine learning algorithm. This enables children to color images with their desired colors and shapes, crafting unique masterpieces. AI can generate coloring images featuring beloved characters, providing a fun coloring experience. The result would be a unique work of art that they could proudly display. Simply by generating the pictures the parent can print them and deliver them to their children for a fun coloring experience.

How does the solution work?

In this application, we use AI to generate kid coloring pictures based on user descriptions. Results can be achieved through AI with a fine-tuned stable diffusion model or generic AI search, both undergoing an image enhancement tunnel to boost quality. This step guarantees picture quality, crucial for capturing children’s interest. An additional step removes color if not specified for a kid’s coloring book. The flow chart of generated pictures is shown in Figure 1.

Figure 1. Generating pictures from the two tunnels of the AI models.

Conclusion

In this article, we have explained the applications of AI in the case of generating new improved pictures for kid coloring books. The provided pictures were generated based on the written quote of each user. There are two options for providing the essential quotes in this program. The first option is the written text by the user such as “Superman and Pepe’s pig eating ice cream for kid coloring book for kid coloring book” and “Pepe pig flying like a superman eating ice cream for kid coloring book”. The second option in this program is to provide a cluster of quotes for generating pictures using an Excel file. The final outcome of this work will be the generated pictures that the user can download and use it for a fun experience.

Reference

[1]. Durgadevi, M., 2021, July. Generative adversarial network (gan): a general review on different variants of gan and applications. In 2021 6th International Conference on Communication and Electronics Systems (ICCES) (pp. 1-8). IEEE.
[2]. Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M. and Aberman, K., 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 22500-22510).
[3]. Zhang, M. and Ling, Q., 2020. Supervised pixel-wise GAN for face super-resolution. IEEE Transactions on Multimedia, 23, pp.1938-1950.

 

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