IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationImage GenerationMotion GenerationSemantic SegmentationStructured Output GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation
Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references. This differs from human language processing, for which visual imaginatio…
nlg evaluationText GenerationImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation
Automatic evaluations for natural language generation conventionally rely on token-level or embedding-level comparisons with the text references. This is different from human evaluation manners, in which people also form…
nlg evaluationText GenerationImagine yourself: Tuning-Free Personalized Image Generation
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike con…
DiversityImage GenerationPersonalized Image GenerationIMAGINE: Image Synthesis by Image-Guided Model Inversion
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We leverage the knowledge of image semantics from…
Image GenerationmodelSpecificityLearning to Imagine: Visually-Augmented Natural Language Generation
People often imagine relevant scenes to aid in the writing process. In this work, we aim to utilize visual information for composition in the same manner as humans. We propose a method, LIVE, that makes pre-trained langu…
SentenceText Generation