自良
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README.md
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<img src="https://img.alicdn.com/imgextra/i1/O1CN01OCdAMv1xOpJDMJRo1_!!6000000006434-0-tps-4345-2809.jpg" width="100%" height="100%">
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</p>
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### Dataset Overview
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IDEA-Bench is a comprehensive benchmark designed to evaluate generative models' performance in professional design tasks. It includes **100** carefully selected tasks across five categories: text-to-image, image-to-image, images-to-image, text-to-images, and image(s)-to-images. These tasks encompass a wide range of applications, including storyboarding, visual effects, photo retouching, and more.
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<img src="https://img.alicdn.com/imgextra/i1/O1CN01OCdAMv1xOpJDMJRo1_!!6000000006434-0-tps-4345-2809.jpg" width="100%" height="100%">
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</p>
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- **Homepage:** [https://ali-vilab.github.io/IDEA-Bench-Page](https://ali-vilab.github.io/IDEA-Bench-Page)
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- **Repository:** [https://github.com/ali-vilab/IDEA-Bench](https://github.com/ali-vilab/IDEA-Bench)
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- **Paper:** [https://arxiv.org/abs/2412.11767](https://arxiv.org/abs/2412.11767)
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### Dataset Overview
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IDEA-Bench is a comprehensive benchmark designed to evaluate generative models' performance in professional design tasks. It includes **100** carefully selected tasks across five categories: text-to-image, image-to-image, images-to-image, text-to-images, and image(s)-to-images. These tasks encompass a wide range of applications, including storyboarding, visual effects, photo retouching, and more.
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