--- license: agpl-3.0 --- # Model info Creator: [https://civitai.com/user/jice](https://civitai.com/user/jice) [https://civitai.com/models/383364?modelVersionId=471056](https://civitai.com/models/383364?modelVersionId=471056) `creapromptLightning_creapromtHypersdxlV1_r32_r0.1_HSWQ_fp8e4m3.safetensors` full model `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) [https://civitai.com/models/383364?modelVersionId=505350](https://civitai.com/models/383364?modelVersionId=505350) `creapromptLightning_creapromptHyperCFGV2_r32_r0.1_HSWQ_fp8e4m3.safetensors` full model `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) # Hybrid-Sensitivity-Weighted-Quantization (HSWQ)

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). **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) **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) **SDXL Benchmark Test Results:** [md/SDXL Benchmark Test Results.md](https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/blob/main/test/benchmark_test.md) # Credit & Special Acknowledgement [https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization](https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization) [https://github.com/tritant/ComfyUI_Kitchen_nvfp4_Converter](https://github.com/tritant/ComfyUI_Kitchen_nvfp4_Converter) [https://github.com/NVIDIA/Model-Optimizer](https://github.com/NVIDIA/Model-Optimizer) 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)