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| license: agpl-3.0 |
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| # Model info |
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| Creator: [https://civitai.com/user/jice](https://civitai.com/user/jice) |
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| [https://civitai.com/models/383364?modelVersionId=471056](https://civitai.com/models/383364?modelVersionId=471056) |
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| `creapromptLightning_creapromtHypersdxlV1_r32_r0.1_HSWQ_fp8e4m3.safetensors` full model |
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| `creapromptLightning_creapromtHypersdxlV1_r32_r0.1_HSWQ_fp8e4m3_unetonly.safetensors` unet only can use my [NVFP4 clips](https://huggingface.co/ApacheOne/sdxl_text_encoders-NVFP4) with taesd vae [https://github.com/madebyollin/taesd](https://github.com/madebyollin/taesd) |
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| [https://civitai.com/models/383364?modelVersionId=505350](https://civitai.com/models/383364?modelVersionId=505350) |
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| `creapromptLightning_creapromptHyperCFGV2_r32_r0.1_HSWQ_fp8e4m3.safetensors` full model |
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| `creapromptLightning_creapromptHyperCFGV2_r32_r0.1_HSWQ_fp8e4m3_unetonly.safetensors` unet only can use my [NVFP4 clips](https://huggingface.co/ApacheOne/sdxl_text_encoders-NVFP4) with taesd vae [https://github.com/madebyollin/taesd](https://github.com/madebyollin/taesd) |
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| # Hybrid-Sensitivity-Weighted-Quantization (HSWQ) |
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| <p align="center"> |
| <img src="https://raw.githubusercontent.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/main/icon.png" width="128"> |
| </p> |
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| High-fidelity FP8 quantization for diffusion models (SDXL). HSWQ uses **sensitivity** and **importance** analysis instead of naive uniform cast, and offers two modes: standard-compatible (V1) and high-performance scaled (V2). |
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| **Technical details:** [md/HSWQ_ Hybrid Sensitivity Weighted Quantization.md](https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/blob/main/md/HSWQ_%20Hybrid%20Sensitivity%20Weighted%20Quantization.md) |
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| **How to quantize:** [md/HSWQ_ How to quantize SDXL.md](https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/blob/main/md/How%20to%20quantize%20SDXL.md) |
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| **SDXL Benchmark Test Results:** [md/SDXL Benchmark Test Results.md](https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/blob/main/test/benchmark_test.md) |
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| # Credit & Special Acknowledgement |
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| [https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization](https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization) |
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| [https://github.com/tritant/ComfyUI_Kitchen_nvfp4_Converter](https://github.com/tritant/ComfyUI_Kitchen_nvfp4_Converter) |
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| [https://github.com/NVIDIA/Model-Optimizer](https://github.com/NVIDIA/Model-Optimizer) |
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| We extend our deepest respect and gratitude to the **Nunchaku Team** for their groundbreaking work on SVDQ quantization and for sharing their models with the community. This collection relies heavily on their research and original implementation. |
| - **Original Repository:** [nunchaku-tech/nunchaku-sdxl](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) |
