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Sponsored by
YouArt
ApacheOne / HSWQ-fp8-SDXL
Model's Last Updated: April 21 2026
Introduction of HSWQ-fp8-SDXL
Model Details of HSWQ-fp8-SDXL
Model info
Creator: https://civitai.com/user/jice
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
with taesd vae
https://github.com/madebyollin/taesd
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
with taesd vae
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
How to quantize: md/HSWQ_ How to quantize SDXL.md
SDXL Benchmark Test Results: md/SDXL Benchmark Test Results.md
Credit & Special Acknowledgement
https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization
https://github.com/tritant/ComfyUI_Kitchen_nvfp4_Converter
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
Runs of ApacheOne HSWQ-fp8-SDXL on huggingface.co
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More Information About HSWQ-fp8-SDXL huggingface.co Model
Url of HSWQ-fp8-SDXL
HSWQ-fp8-SDXL huggingface.co Url
Provider of HSWQ-fp8-SDXL huggingface.co
Other API from ApacheOne
Updated:August 13 2026
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