Hybrid-Sensitivity-Weighted-Quantization (HSWQ)

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

πŸ“¦ 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

πŸ“œ Credits & License

πŸ† 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.

Base Models

These models are derivatives of their respective creators. All credit for aesthetic tuning and model training belongs to the original creators.


Disclaimer: These models are provided for optimization and research purposes. Please adhere to the original licenses of the base models.

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