coolthor/comfyui-zimage-sulphur-nvfp4

Z-Image Turbo — NVFP4 pack for ComfyUI

An NVFP4-quantized build of Z-Image Turbo plus everything ComfyUI needs to run it, packaged for Blackwell GPUs (RTX 5090, DGX Spark / GB10) where NVFP4 runs natively.

The full writeup — how it was quantized and how it actually performs — is at ai-muninn.com.

What's in the pack

File	Role	Size
z_image_turbo_nvfp4.safetensors	Z-Image Turbo diffusion model — NVFP4	4.5 GB
ae.safetensors	VAE (autoencoder)	335 MB
qwen_3_4b.safetensors	Qwen3-4B text encoder (bf16)	8.0 GB
qwen_3_4b_fp8_mixed.safetensors	Qwen3-4B text encoder (FP8 mixed)	5.6 GB
gemma_3_12B_it_fp4_mixed.safetensors	Gemma-3-12B-IT text encoder (FP4 mixed)	9.4 GB
Z-Image-Turbo-Fun-Controlnet-Union.safetensors	ControlNet Union	3.1 GB

Why NVFP4

NVFP4 is Blackwell's native 4-bit format. On a 5090 / GB10 it roughly halves the diffusion model's VRAM versus fp8/bf16 while keeping the tensor cores fed — so Z-Image Turbo's few-step generation stays fast without spilling to system RAM. FP8 / FP4 text-encoder variants are included so you can trade quality vs. footprint on the encoder side too.

Use in ComfyUI

Drop the files into your ComfyUI model folders:

• z_image_turbo_nvfp4.safetensors → models/diffusion_models/
• ae.safetensors → models/vae/
• qwen_3_4b*.safetensors / gemma_3_12B_it_fp4_mixed.safetensors → models/text_encoders/
• Z-Image-Turbo-Fun-Controlnet-Union.safetensors → models/controlnet/

Credits & licensing

• Base model — Tongyi-MAI/Z-Image-Turbo · Apache-2.0
• ControlNet — alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union · Apache-2.0
• Text encoders — Qwen3-4B (Apache-2.0). The bundled gemma_3_12B_it_fp4_mixed file is Google Gemma-3, covered by the Gemma Terms of Use, not Apache-2.0 — use that file under those terms.

NVFP4 quantization & packaging by ai-muninn. This repackaging is Apache-2.0; the Gemma component keeps its own license as noted above.