A Survey of Diffusion Based Image Generation Models: Issues and Their
  Solutions
Tianyi Zhang Zheng Wang Jing Huang Mohiuddin Muhammad Tasnim Wei Shi
Recently, there has been significant progress in the development of large
models. Following the success of ChatGPT, numerous language models have been
introduced, demonstrating remarkable performance. Similar advancements have
also been observed in image generation models, such as Google's Imagen model,
OpenAI's DALL-E 2, and stable diffusion models, which have exhibited impressive
capabilities in generating images. However, similar to large language models,
these models still encounter unresolved challenges. Fortunately, the
availability of open-source stable diffusion models and their underlying
mathematical principles has enabled the academic community to extensively
analyze the performance of current image generation models and make
improvements based on this stable diffusion framework. This survey aims to
examine the existing issues and the current solutions pertaining to image
generation models.