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README.md
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@@ -98,6 +98,6 @@ Playground v2.5 outperforms both baselines by a large margin.
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| SDXL-1-0-refiner | 9.55 |
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| [playground-v2-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic) | 7.07 |
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| [playground-v2.5-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic) | 4.48 |
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Lastly, we report metrics using our MJHQ-30K benchmark which we [open-sourced](https://huggingface.co/datasets/playgroundai/MJHQ-30K) with the v2 release. We report both the overall FID and per category FID. All FID metrics are computed at resolution 1024x1024. Our results show that Playground v2.5 outperforms both Playground v2 and SDXL in overall FID and all category FIDs, especially in the people and fashion categories. This is in line with the results of the user study, which indicates a correlation between human preferences and the FID score of the MJHQ-30K benchmark.
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| SDXL-1-0-refiner | 9.55 |
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| [playground-v2-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic) | 7.07 |
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| [playground-v2.5-1024px-aesthetic](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic) | **4.48** |
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Lastly, we report metrics using our MJHQ-30K benchmark which we [open-sourced](https://huggingface.co/datasets/playgroundai/MJHQ-30K) with the v2 release. We report both the overall FID and per category FID. All FID metrics are computed at resolution 1024x1024. Our results show that Playground v2.5 outperforms both Playground v2 and SDXL in overall FID and all category FIDs, especially in the people and fashion categories. This is in line with the results of the user study, which indicates a correlation between human preferences and the FID score of the MJHQ-30K benchmark.
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