A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions
It addresses performance issues in image generation models for researchers and practitioners, but is incremental as it synthesizes existing work.
This survey examines unresolved challenges in diffusion-based image generation models, such as Google's Imagen and OpenAI's DALL-E 2, and reviews current solutions based on the stable diffusion framework.
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.