Expressive Text-to-Image Generation with Rich Text · HackerPapers discussions

Expressive Text-to-Image Generation with Rich Text — mentioned by jbhuang0604

Fri, 06 Oct 2023 02:30:57 GMT

Yup, it could be similar, but it mostly only works for very simple prompts (e.g., one subject in the image). For example, in Figure 11 of the paper (https://arxiv.org/pdf/2304.06720.pdf), you can see that full-text "rustic cabin -> rustic orange cabin" does not turn the cabin orange. For coloring, the core benefit of our method is that it allows precise color control. For example, it can generate colors with rare names (e.g., Plum Purple or Dodger Blue) or even particular RGB triplets that we cannot describe well with texts. You can examples in Figure 4 here…

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Expressive Text-to-Image Generation with Rich Text — mentioned by jbhuang0604

Thu, 05 Oct 2023 01:39:52 GMT

We compared with three other prompt term weighting (aka font size) methods: - Prompt-to-Prompt - Parenthesis - Repeating Our results show that Prompt-to-Prompt would generate artifacts and other heuristic methods are not very effective. You can see some examples in Figure 21, 22, 23. https://arxiv.org/pdf/2304.06720.pdf

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Expressive Text-to-Image Generation with Rich Text — mentioned by jbhuang0604

Thu, 05 Oct 2023 01:07:53 GMT

Thanks a lot for the comment! (one of the authors here) RE: plaintext - The "plain-text" result is just a baseline. We call the "plaintext parentheses in the prompt" full-text (i.e., expanding the rich text info into a long sentence). We show many "full-text" in the paper https://arxiv.org/pdf/2304.06720.pdf. You can see in Fig 11 that full-text results cannot change the color, style, and do not respect the description. More examples in Figure 13, 14, and 15. The main issue of using full-text is that it cannot preserve the original plain text image, thereby requiring many rounds of prompt…

Paper and discussion history. A mention is not an endorsement.