Downloads · 30 days
0
Hybrid-Sensitivity-Weighted-Quantization-Z-Image-Hybrid-ConvRot-NVFP4
ussoewwin/Hybrid-Sensitivity-Weighted-Quantization-Z-Image-Hybrid-ConvRot-NVFP4
Hybrid-Sensitivity-Weighted-Quantization-Z-Image-Hybrid-ConvRot-NVFP4 is a text-to-image model from ussoewwin. Use it when you need an image from a text prompt. It is set up for nunchaku. The card lists the license as other.
<p align="center" <img src="https://raw.githubusercontent.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/main/icon.png" width="128" </p
Access
Public
Updated Aug 30, 2026
Repo size
173 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors43.8 GB · 100%
At a glance
- Task
- Text-to-Image
- Library
- nunchaku
- License
- other
- Access
- Public
- Created
- Aug 1, 2026
- Updated
- Aug 30, 2026
- SHA
- c99095f4
Model card
From the Hugging Face model README
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 Hybrid ConvRot NVFP4 quantization for Z-Image Turbo diffusion models. Built from a complete native ConvRot INT8 UNet via the reverse method (converting lowest-impact layers to NVFP4 in ascending order of trajectory impact). This is highly useful for users who need to strictly manage their VRAM resources (~53–58% savings) while maintaining high image fidelity and completely eliminating trajectory bifurcations.
Technical details: https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization
How to quantize (Z-Image Hybrid NVFP4): md/How to quantize Z Image - Hybrid NVFP4.md
ComfyUI Loader for ConvRot NVFP4: To use these models in ComfyUI, please use this custom node: ComfyUI-HSWQ-Loader-and-Tools
Z Image ConvRot NVFP4 Benchmark Test Results (published tables): benchmark result/benchmark_zi_nvfp4.md
Benchmark (Deterministic Latent Trajectory Divergence)
Multi-seed trajectory evaluation (20 seeds per model, 200 total evaluations across 10 Z-Image models):
| Metric | HSWQ ConvRot Hybrid NVFP4 | Native NVFP4 (Full Model) | Advantage |
|---|---|---|---|
| Final Cosine (Avg) | 0.960 – 0.974 | 0.900 – 0.947 | +0.020 to +0.060 higher fidelity |
| Bifurcation Rate | 0/200 (0.0%) | 3/200 (1.5%) | Zero bifurcations across all seeds |
| Latent MSE Reduction | 0.74 – 1.25 | 1.45 – 2.54 | 36% – 61% lower quantization error |
| VRAM Footprint | ~53% – 58% Savings | ~50% Savings | Significantly reduced VRAM usage |
📦 Available Models
| Filename | Base Model | Version | License |
|---|---|---|---|
moodyProMix_zitV13_hswq_hybrid_nv80_convrot_nvfp4.safetensors | Moody Pro Mix | zit v1.3 (nv80) | CreativeML Open RAIL++-M |
moodyProMix_collectorsEdition_hswq_hybrid_nv90_convrot_nvfp4.safetensors | Moody Pro Mix | Collector's Edition (nv90/nv100) | CreativeML Open RAIL++-M |
moodyRealMix_zitV7_hswq_hybrid_nv100_convrot_nvfp4.safetensors | Moody Real Mix | zit v7.0 (nv100) | CreativeML Open RAIL++-M |
moodyRealMix_xhsEdition_hswq_hybrid_nv110_convrot_nvfp4.safetensors | Moody Real Mix | XHS Edition (nv100/nv110) | CreativeML Open RAIL++-M |
darkBeast30BF16INT8_dbzit9DIMRclaw_hswq_hybrid_nv100_convrot_nvfp4.safetensors | Dark Beast | dbzit9 DIMRclaw (nv100) | CreativeML Open RAIL++-M |
unstableRevolution_V3Fp16_hswq_hybrid_nv90_convrot_nvfp4.safetensors | Unstable Revolution | v3.0 FP16 (nv90/nv100) | CreativeML Open RAIL++-M |
gonzalomoZpop_insta2_hswq_hybrid_nv80_convrot_nvfp4.safetensors | gonzalomo Z-Pop | insta2 (nv80/nv100) | CreativeML Open RAIL++-M |
divingZImageTurbo_v70Fp16_hswq_hybrid_nv90_convrot_nvfp4.safetensors | Diving Z-Image Turbo | v7.0 FP16 (nv90/nv99) | CreativeML Open RAIL++-M |
beyondREALITY_V30_hswq_hybrid_nv100_convrot_nvfp4.safetensors | Beyond REALITY | v3.0 (nv100) | CreativeML Open RAIL++-M |
2127ZImageAsianUtopian_v40Turbo_hswq_hybrid_nv100_convrot_nvfp4.safetensors | 2127 Z-Image Asian Utopian | v4.0 Turbo (nv100) | CreativeML Open RAIL++-M |
copaxTimeless_xplusZ13_hswq_hybrid_nv100_convrot_nvfp4.safetensors | Copax Timeless | xplus Z13 (nv100) | CreativeML Open RAIL++-M |
unstablebastard_v14_hswq_hybrid_nv100_convrot_nvfp4.safetensors | Unstable Bastard | v1.4 (nv100) | CreativeML Open RAIL++-M |
zimageTurboByStable_2602BF16_hswq_hybrid_nv100_convrot_nvfp4.safetensors | Z-Image Turbo | 2602 BF16 (nv99/nv100) | CreativeML Open RAIL++-M |
📜 Credits & License
🏆 Special Acknowledgement
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
Base Models
These models are derivatives of their respective creators. All credit for aesthetic tuning and model training belongs to the original creators.
- Moody Pro Mix / Moody Real Mix / Dark Beast: Created by catlover1937.
- Unstable Revolution / Unstable Bastard: Created by Yamer.
- gonzalomo Z-Pop: Created by gonzalomo.
- Diving Z-Image Turbo: Created by Diving.
- Beyond REALITY: Created by Lykon.
- 2127 Z-Image Asian Utopian: Created by 2127.
- Copax Timeless: Created by Copax.
- Z-Image Turbo: Created by Tongyi-MAAS / Civitai Community.
Disclaimer: These models are provided for optimization and research purposes. Please adhere to the original licenses of the base models.
- Task
- Text-to-Image
- Library
- nunchaku
- License
- other
- Created
- Aug 1, 2026
- Updated
- Aug 30, 2026