Investigating Sampler Impact on AI Image Generation: A Case Study on Dogs Playing in the River

AI image generation is a new and exciting field with many different uses. It is important to understand how different sampling techniques affect the quality of AI-generated images in order to get the best results. This study looks at how different sampling techniques affect the quality of AI-generated images of dogs playing in the river. This study is limited to a specific scenario, as there are not many images of dogs playing in the river already on the internet. The study used the Playground.ai open-source web platform to test different sampling techniques. DDIM was found to be the best sampling technique for generating realistic images of dogs playing in the river. Euler was also found to be very fast, which is an important consideration when choosing a sampling technique. These findings show that different sampling techniques have different strengths and weaknesses, and it is important to choose the right sampling technique for the specific task at hand. This study provides valuable insights into how sampling techniques affect AI image generation. It is important to choose the right sampling technique for the specific task at hand in order to get the best results. The study also demonstrates the societal relevance of AI-generated imagery in various applications.

@article{Deshmukh2024,
  title     = {Investigating Sampler Impact on AI Image Generation: A Case Study on Dogs Playing in the River},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Sanjay Deshmukh},
  doi       = {10.14569/IJACSA.2024.0150443},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150443}
}