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license: agpl-3.0
---

`absolutereality_v181_r32_r0.1_HSWQ_fp8e4m3fn.safetensors` This is only the sd15 unet layers at HSWQ_fp8e4m3fn

Example images and workflow inside, workflow is not the best as ComfyUI and other pipelines have dropped the ball for sd15.

Positive rompt:  realistic, a photo of a gothic horror woman in a black lake, beautiful woman, unsettling horror, professional, (Extremely Detailed:1.2), glow effects, godrays, intricate details, sharp focus, dramatic, photorealistic, tribal village, wooden village, burning forest background, sharp contrast, many colours, serious face, smirking, Eva Green

Negative embeddings files: BadDream, UnrealisticDream

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        <small><code>absolutereality_v181.safetensors</code></small>
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# Hybrid-Sensitivity-Weighted-Quantization (HSWQ)

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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).

**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)