Surrealistic-like Image Generation with Vision-Language Models
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913618353717248 |
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| author | Ayten, Elif Wang, Shuai Snoep, Hjalmar |
| author_facet | Ayten, Elif Wang, Shuai Snoep, Hjalmar |
| contents | Recent advances in generative AI make it convenient to create different types of content, including text, images, and code. In this paper, we explore the generation of images in the style of paintings in the surrealism movement using vision-language generative models, including DALL-E, Deep Dream Generator, and DreamStudio. Our investigation starts with the generation of images under various image generation settings and different models. The primary objective is to identify the most suitable model and settings for producing such images. Additionally, we aim to understand the impact of using edited base images on the generated resulting images. Through these experiments, we evaluate the performance of selected models and gain valuable insights into their capabilities in generating such images. Our analysis shows that Dall-E 2 performs the best when using the generated prompt by ChatGPT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14366 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Surrealistic-like Image Generation with Vision-Language Models Ayten, Elif Wang, Shuai Snoep, Hjalmar Computer Vision and Pattern Recognition Artificial Intelligence 68T07, 68T01, 68T20 J.5; I.2.0; I.4.7; I.4.9 Recent advances in generative AI make it convenient to create different types of content, including text, images, and code. In this paper, we explore the generation of images in the style of paintings in the surrealism movement using vision-language generative models, including DALL-E, Deep Dream Generator, and DreamStudio. Our investigation starts with the generation of images under various image generation settings and different models. The primary objective is to identify the most suitable model and settings for producing such images. Additionally, we aim to understand the impact of using edited base images on the generated resulting images. Through these experiments, we evaluate the performance of selected models and gain valuable insights into their capabilities in generating such images. Our analysis shows that Dall-E 2 performs the best when using the generated prompt by ChatGPT. |
| title | Surrealistic-like Image Generation with Vision-Language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07, 68T01, 68T20 J.5; I.2.0; I.4.7; I.4.9 |
| url | https://arxiv.org/abs/2412.14366 |