Surrealistic-like Image Generation with Vision-Language Models
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection
Implementing Adaptations for Vision AutoRegressive Model
CharaConsist: Fine-Grained Consistent Character Generation
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
NeoBabel: A Multilingual Open Tower for Visual Generation
Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.