GANji: A Framework for Introductory AI Image Generation
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918150090522624 |
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| author | Hamel, Chandon Busch, Mike |
| author_facet | Hamel, Chandon Busch, Mike |
| contents | The comparative study of generative models often requires significant computational resources, creating a barrier for researchers and practitioners. This paper introduces GANji, a lightweight framework for benchmarking foundational AI image generation techniques using a dataset of 10,314 Japanese Kanji characters. It systematically compares the performance of a Variational Autoencoder (VAE), a Generative Adversarial Network (GAN), and a Denoising Diffusion Probabilistic Model (DDPM). The results demonstrate that while the DDPM achieves the highest image fidelity, with a Fréchet Inception Distance (FID) score of 26.2, its sampling time is over 2,000 times slower than the other models. The GANji framework is an effective and accessible tool for revealing the fundamental trade-offs between model architecture, computational cost, and visual quality, making it ideal for both educational and research purposes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GANji: A Framework for Introductory AI Image Generation Hamel, Chandon Busch, Mike Computer Vision and Pattern Recognition The comparative study of generative models often requires significant computational resources, creating a barrier for researchers and practitioners. This paper introduces GANji, a lightweight framework for benchmarking foundational AI image generation techniques using a dataset of 10,314 Japanese Kanji characters. It systematically compares the performance of a Variational Autoencoder (VAE), a Generative Adversarial Network (GAN), and a Denoising Diffusion Probabilistic Model (DDPM). The results demonstrate that while the DDPM achieves the highest image fidelity, with a Fréchet Inception Distance (FID) score of 26.2, its sampling time is over 2,000 times slower than the other models. The GANji framework is an effective and accessible tool for revealing the fundamental trade-offs between model architecture, computational cost, and visual quality, making it ideal for both educational and research purposes. |
| title | GANji: A Framework for Introductory AI Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.24128 |