Surrealistic-like Image Generation with Vision-Language Models
On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 3 of its 21 references that are in the map. First posted on arXiv 18 Dec 2024.
Builds on
3 of 21 references.
References · 3
- 2013Auto-Encoding Variational Bayescited by 3,980
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
- 2018A Note on the Inception Scorecited by 225
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