---
title: HSWQ-fp8-SD15
canonical_url: "https://www.modelscope.cn/models/ApacheOne/HSWQ-fp8-SD15"
md_url: "https://www.modelscope.cn/models/ApacheOne/HSWQ-fp8-SD15.md"
repository: ApacheOne/HSWQ-fp8-SD15
last_updated: 2026-04-21
license: agpl-3.0
model_type:
  - custom
parameters: 1.9B
tensor_type:
  - F8_E4M3
  - F16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 12
stars: 0
---

# HSWQ-fp8-SD15

> HSWQ-fp8-SD15 - ApacheOne 在 ModelScope 开源的模型。absoluterealityv181r32r0.1HSWQfp8e4m3fn.safetensors This is only the sd15 unet layers at HSWQfp8e4m3fn

ApacheOne/HSWQ-fp8-SD15 是 ModelScope 魔搭社区上的 1.9B 参数机器学习模型，采用 agpl-3.0 许可。

- **Repository**: ApacheOne/HSWQ-fp8-SD15
- **License**: agpl-3.0
- **Parameters**: 1.9B
- **Downloads**: 12
- **Stars**: 0
- **Last updated**: 2026-04-21

Source: https://www.modelscope.cn/models/ApacheOne/HSWQ-fp8-SD15

---

`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

<table style="width: auto;">
  <tr>
    <!-- Image 1 -->
    <td align="center" valign="top" style="padding: 10px;">
      <a href="assets/ComfyUI_temp_hbtnv_00003_.png" target="_blank">
        <img src="assets/ComfyUI_temp_hbtnv_00003_.png" height="120" style="border: 1px solid #444; border-radius: 4px;">
      </a>
      <br>
      <p style="width: 150px; word-wrap: break-word; line-height: 1.2;">
        <small><code>absolutereality_v181_r32_r0.1_HSWQ_fp8e4m3fn.safetensors</code></small>
      </p>
    </td>
    <!-- Image 2 -->
    <td align="center" valign="top" style="padding: 10px;">
      <a href="assets/ComfyUI_temp_hbtnv_00004_.png" target="_blank">
        <img src="assets/ComfyUI_temp_hbtnv_00004_.png" height="120" style="border: 1px solid #444; border-radius: 4px;">
      </a>
      <br>
      <p style="width: 150px; word-wrap: break-word; line-height: 1.2;">
        <small><code>absolutereality_v181.safetensors</code></small>
      </p>
    </td>
  </tr>
</table>


# Hybrid-Sensitivity-Weighted-Quantization (HSWQ)

<p align="center">
  <img src="https://raw.githubusercontent.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/main/icon.png" width="128">
</p>

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)