PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911629859356672 |
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| author | Buchnick, Asaf Shamsian, Aviv Navon, Aviv Fetaya, Ethan |
| author_facet | Buchnick, Asaf Shamsian, Aviv Navon, Aviv Fetaya, Ethan |
| contents | Text-to-image generation has progressed rapidly, but faithfully generating complex scenes requires extensive trial-and-error to find the exact prompt. In the prompt inversion task, the goal is to recover a textual prompt that can faithfully reconstruct a given target image. Currently, existing methods frequently yield suboptimal reconstructions and produce unnatural, hard-to-interpret prompts that hinder transparency and controllability. In this work, we present PromptEvolver, a prompt inversion approach that generates natural-language prompts while achieving high-fidelity reconstructions of the target image. Our method uses a genetic algorithm to optimize the prompt, leveraging a strong vision-language model to guide the evolution process. Importantly, it works on black-box generation models by requiring only image outputs. Finally, we evaluate PromptEvolver across multiple prompt inversion benchmarks and show that it consistently outperforms competing methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_06061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space Buchnick, Asaf Shamsian, Aviv Navon, Aviv Fetaya, Ethan Machine Learning Text-to-image generation has progressed rapidly, but faithfully generating complex scenes requires extensive trial-and-error to find the exact prompt. In the prompt inversion task, the goal is to recover a textual prompt that can faithfully reconstruct a given target image. Currently, existing methods frequently yield suboptimal reconstructions and produce unnatural, hard-to-interpret prompts that hinder transparency and controllability. In this work, we present PromptEvolver, a prompt inversion approach that generates natural-language prompts while achieving high-fidelity reconstructions of the target image. Our method uses a genetic algorithm to optimize the prompt, leveraging a strong vision-language model to guide the evolution process. Importantly, it works on black-box generation models by requiring only image outputs. Finally, we evaluate PromptEvolver across multiple prompt inversion benchmarks and show that it consistently outperforms competing methods. |
| title | PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.06061 |