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| license: agpl-3.0 |
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| `absolutereality_v181_r32_r0.1_HSWQ_fp8e4m3fn.safetensors` This is only the sd15 unet layers at HSWQ_fp8e4m3fn |
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| Example images and workflow inside, workflow is not the best as ComfyUI and other pipelines have dropped the ball for sd15. |
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| 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 |
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| Negative embeddings files: BadDream, UnrealisticDream |
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| <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> |
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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) |
