IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917900981370880 |
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| author | Lei, Jiayi Zhang, Renrui Hu, Xiangfei Lin, Weifeng Li, Zhen Sun, Wenjian Du, Ruoyi Zhuo, Le Li, Zhongyu Li, Xinyue Zhao, Shitian Guo, Ziyu Lu, Yiting Gao, Peng Li, Hongsheng |
| author_facet | Lei, Jiayi Zhang, Renrui Hu, Xiangfei Lin, Weifeng Li, Zhen Sun, Wenjian Du, Ruoyi Zhuo, Le Li, Zhongyu Li, Xinyue Zhao, Shitian Guo, Ziyu Lu, Yiting Gao, Peng Li, Hongsheng |
| contents | With the rapid development of diffusion models, text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation. Recently launched models such as FLUX.1 and Ideogram2.0, along with others like Dall-E3 and Stable Diffusion 3, have demonstrated exceptional performance across various complex tasks, raising questions about whether T2I models are moving towards general-purpose applicability. Beyond traditional image generation, these models exhibit capabilities across a range of fields, including controllable generation, image editing, video, audio, 3D, and motion generation, as well as computer vision tasks like semantic segmentation and depth estimation. However, current evaluation frameworks are insufficient to comprehensively assess these models' performance across expanding domains. To thoroughly evaluate these models, we developed the IMAGINE-E and tested six prominent models: FLUX.1, Ideogram2.0, Midjourney, Dall-E3, Stable Diffusion 3, and Jimeng. Our evaluation is divided into five key domains: structured output generation, realism, and physical consistency, specific domain generation, challenging scenario generation, and multi-style creation tasks. This comprehensive assessment highlights each model's strengths and limitations, particularly the outstanding performance of FLUX.1 and Ideogram2.0 in structured and specific domain tasks, underscoring the expanding applications and potential of T2I models as foundational AI tools. This study provides valuable insights into the current state and future trajectory of T2I models as they evolve towards general-purpose usability. Evaluation scripts will be released at https://github.com/jylei16/Imagine-e. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models Lei, Jiayi Zhang, Renrui Hu, Xiangfei Lin, Weifeng Li, Zhen Sun, Wenjian Du, Ruoyi Zhuo, Le Li, Zhongyu Li, Xinyue Zhao, Shitian Guo, Ziyu Lu, Yiting Gao, Peng Li, Hongsheng Computer Vision and Pattern Recognition Computation and Language Machine Learning With the rapid development of diffusion models, text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation. Recently launched models such as FLUX.1 and Ideogram2.0, along with others like Dall-E3 and Stable Diffusion 3, have demonstrated exceptional performance across various complex tasks, raising questions about whether T2I models are moving towards general-purpose applicability. Beyond traditional image generation, these models exhibit capabilities across a range of fields, including controllable generation, image editing, video, audio, 3D, and motion generation, as well as computer vision tasks like semantic segmentation and depth estimation. However, current evaluation frameworks are insufficient to comprehensively assess these models' performance across expanding domains. To thoroughly evaluate these models, we developed the IMAGINE-E and tested six prominent models: FLUX.1, Ideogram2.0, Midjourney, Dall-E3, Stable Diffusion 3, and Jimeng. Our evaluation is divided into five key domains: structured output generation, realism, and physical consistency, specific domain generation, challenging scenario generation, and multi-style creation tasks. This comprehensive assessment highlights each model's strengths and limitations, particularly the outstanding performance of FLUX.1 and Ideogram2.0 in structured and specific domain tasks, underscoring the expanding applications and potential of T2I models as foundational AI tools. This study provides valuable insights into the current state and future trajectory of T2I models as they evolve towards general-purpose usability. Evaluation scripts will be released at https://github.com/jylei16/Imagine-e. |
| title | IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models |
| topic | Computer Vision and Pattern Recognition Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2501.13920 |