DOGS: Design-Space Sampling for Prompt-Driven Logo Generation
We therefore recast logo prompting as sampling within a structured design space, and instantiate this idea as DOGS (Design-space prompting with an Originality-aware GFlowNet Sampler).
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Key points
- Prompt optimization for text-to-image (T2I) generation has been pursued almost entirely as text rewriting, in which a short user brief is expanded into a longer, model-preferred token sequence.
- We argue that such a language-space formulation is ill-suited to structured visual design tasks such as logo creation, where a one-line brief leaves most design decisions unspecified.
- From a large corpus of real-world logos, we mine a typed, graph-structured design grammar whose edges record empirical co-occurrence.
- A GFlowNet sampler then generates design graphs with probability proportional to a terminal reward that combines recognizability, aesthetics, and corpus-relative originality.
Sources (1)
- [1]DOGS: Design-Space Sampling for Prompt-Driven Logo GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:24 PM
We therefore recast logo prompting as sampling within a structured design space, and instantiate this idea as DOGS (Design-space prompting with an Originality-aware GFlowNet Sampler).
Prompt optimization for text-to-image (T2I) generation has been pursued almost entirely as text rewriting, in which a short user brief is expanded into a longer, model-preferred token sequence.
Extractive summary: sentences quoted from the sources.