AI Art Generation Handbook/Limitations of AI Art Generation

At Currently, there are some known limitations of AI Art Generation. (Including latest SDXL 1.0)

My criteria of limitations is if the AI ART are unable to generate less than 75% of the time (3 out of 4 images)

Training Image Dataset Issues
For the AI art generations, from the white paper, each AI Art generation system uses own dataset to train.

For example: OpenAI 's DALL-E it is trained using Image-GPT and Stable Diffusion using Common Crawl, Laion-5B(but it is believed it is not trained on all of 5B images). It is believed SDXL are trained in Laion-Aesthetic. https://github.com/google-research-datasets/conceptual-12m

As per saying goes, "Garbage In, Garbage Out", generally meant as if the training images (input) is not properly curated, there are chances that the output images may be gibberish as well. This is the lesser known issues but as times goes on, the AI Image models themself are also finetuned to let the generated images are getting better overtime. But generally, many of limitations is due to the images suffers the following issues:

(i) Many of the smaller resolution picture [Less than 512*512px, out of focus (but not for aesthetic purposes)]

(ii) Wrong / misleading captions related to the images

(iii) Incomplete captioning of the images

(iv) The images database are heavily biased towards Western contexts inside images

(v) Absence of certain images / subjects

To solve many of the limitations, more curations (but expensive) are needed to curate the images at least to Open-AI Dall-E standard (at least for year 2022 versions)