PIE-Bench (Prompt-based Image Editing Benchmark)
Introduced by Ju et al. in Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
PIE-Bench comprises 700 images featuring 10 distinct editing types. Images are evenly distributed in natural and artificial scenes (e.g., paintings) among four categories: animal, human, indoor, and outdoor. Each image in PIE-Bench includes five annotations: source image prompt, target image prompt, editing instruction, main editing body, and the editing mask. Notably, the editing mask annotation (indicating the anticipated editing region) is crucial in accurate metrics computations as we expect the editing to only occur within a designated area.
Benchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
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PIE-Bench
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Virtual Inversion+Unified Attention Control+LCM
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Papers
| Paper | Code | Results | Date | Stars |
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