Run Z-Image-Engineer-V6 fully inside ComfyUI: load the Qwen3-4B prompt model from sharded HuggingFace safetensors or GGUF quants, use it as the Z-Image Turbo text encoder (CLIP), and use the same loaded model as a local prompt enhancer with a live preview on the node. No LM Studio or external server required.

Search for ComfyUI Z-Engineer in ComfyUI Manager, or:

comfy node install comfyui-z-engineer

cd ComfyUI/custom_nodes
git clone https://github.com/BennyDaBall930/ComfyUI-Z-Engineer.git
pip install -r ComfyUI-Z-Engineer/requirements.txt

Restart ComfyUI.

Put the model under ComfyUI/models/text_encoders/:

Sharded safetensors (the published V6 release):

ComfyUI/models/text_encoders/Z-Image-Engineer-V6/
├── model-00001-of-00003.safetensors
├── model-00002-of-00003.safetensors
├── model-00003-of-00003.safetensors
└── model.safetensors.index.json

Download from BennyDaBall/Z-Image-Engineer-V6. The folder shows up in the loader dropdown as Z-Image-Engineer-V6/.

GGUF (recommended for low VRAM):

ComfyUI/models/text_encoders/Z-Image-Engineer-V6-Q4_K_M.gguf

Download any quant from BennyDaBall/Z-Image-Engineer-V6-GGUF (Q4_K_M is a good default; F16 for maximum fidelity). Installing ComfyUI-GGUF is strongly recommended — the quant then stays quantized in VRAM.

1. Add Z-Engineer CLIP Loader (GGUF) (or the Safetensors/Shards loader) and pick the model.
2. Wire clip into your normal CLIP Text Encode for the Z-Image Turbo pipeline — the V6 model doubles as the Qwen3-4B text encoder.
3. Add Z-Engineer Prompt Enhancer (Local), wire the same clip in, and type your raw seed prompt.
4. Wire the enhancer's prompt output into the CLIP Text Encode text input.
5. Queue — the enhanced prompt appears right on the enhancer node.

[Z-Engineer CLIP Loader] ──clip──┬──> [Z-Engineer Prompt Enhancer (Local)] ──prompt──> [CLIP Text Encode] ──> ...
                                 └────────────────────────────────clip───────────────────────^

• temperature: 0.20, top_p: 0.9, top_k: 40, min_p: 0.03
• repetition_penalty: 1.05
• max_tokens: 320
• enforce_seed_terms: true — deterministically re-appends seed phrases (counts, colors, quoted text) if the model drops them
• strip_reasoning / sanitize_output: true

Put LoRA trigger words (or any phrase that must survive verbatim) into the optional keep_terms input, separated by commas: m4rty style, neon glow. The model is instructed to weave them in unchanged, and any it still drops are deterministically re-appended to the final prompt — exact casing preserved. Available on both the Local and API enhancer nodes.

Each multiline box on the enhancer nodes (seed prompt, system prompt, previews) can be resized vertically on its own with the grip in its bottom-right corner — no need to grow the whole node. Box heights are saved with the workflow; double-click the grip corner to reset a box to automatic sizing.

Enable batch_mode to process several seed prompts in one call (split by batch_separator, default \n---\n, falling back to lines). Outputs are joined with the same separator and each one is shown in the preview.

• GGUF Q4_K_M with ComfyUI-GGUF: ~3-4 GB during generation.
• Sharded/single safetensors (FP16): ~9 GB during generation.
• Without ComfyUI-GGUF, GGUFs are dequantized to FP16 at load (full FP16 footprint).
• Loading never spikes VRAM: weights stay on the offload device until first use, and the model participates in ComfyUI's normal model management (it unloads like any other model).

• ComfyUI new enough to ship native Z-Image support (comfy/text_encoders/z_image.py, v0.3.75+)
• requests (API node), gguf (GGUF fallback path)
• Optional but recommended: ComfyUI-GGUF

MIT