ComfyUI Zimage Sulphur NVFP4

--- license: apache-2.0 pipeline_tag: text-to-image library_name: diffusion-single-file base_model: - Tongyi-MAI/Z-Image-Turbo - alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union tags: - comfyui - nvfp4 - quantization - text-to-image - z-image - blackwell --- # Z-Image Turbo — NVFP4 pack for ComfyUI An **NVFP4-quantized** build of [Z-Image Turbo](https://huggingface.co/Tongyi-MAI/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](https://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`](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) · Apache-2.0 - ControlNet — [`alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union`](https://huggingface.co/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](https://ai.google.dev/gemma/terms), _not_ Apache-2.0** — use that file under those terms...

--- license: apache-2.0 pipeline_tag: text-to-image base_model: - Tongyi-MAI/Z-Image-Turbo - alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union tags: - comfyui - nvfp4 - quantization - text-to-image - z-image - blackwell --- # Z-Image Turbo — NVFP4 pack for ComfyUI An **NVFP4-quantized** build of [Z-Image Turbo](https://huggingface.co/Tongyi-MAI/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](https://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`](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) · Apache-2.0 - ControlNet — [`alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union`](https://huggingface.co/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](https://ai.google.dev/gemma/terms), _not_ Apache-2.0** — use that file under those terms. NVFP4 quantization & packaging by...

Source context: 1327 downloads · 1 likes · Pipeline text-to-image · Library diffusion-single-file · Repo coolthor/comfyui-zimage-sulphur-nvfp4