The main repo contains the merged HF safetensors. This repo contains the quant ladder for the ComfyUI-Z-Engineer node, LM Studio, ComfyUI CLIPLoaderGGUF, llama.cpp-style loaders, and local prompt-enhancement workflows.
Z-Image-Engineer V6 simple A/B with rewrites
What is this?
Z-Image-Engineer V6 is a SMART DoRA fine-tuned 4B Qwen text encoder from Tongyi-MAI/Z-Image-Turbo.
Use these GGUF files when you want:
ComfyUI Z-Image text-encoder replacement and in-ComfyUI prompt enhancement through ComfyUI-Z-Engineer (no external server needed)
LM Studio prompt enhancement
ComfyUI text-encoder loading through plain CLIPLoaderGGUF
smaller local files than the merged HF safetensors
the same V6 prompt style and conditioning behavior in a quantized format
Quantization Ladder
Filename
Size
Target Use Case
Z-Image-Engineer-V6-F16.gguf
7.498 GiB
Full precision reference.
Z-Image-Engineer-V6-Q8_0.gguf
3.986 GiB
Near-lossless; used for local A/B testing.
Z-Image-Engineer-V6-Q6_K.gguf
3.079 GiB
High-fidelity balanced footprint.
Z-Image-Engineer-V6-Q5_K_M.gguf
2.697 GiB
Daily-driver performance-to-size ratio.
Z-Image-Engineer-V6-Q4_K_M.gguf
2.331 GiB
Reliable 4-bit standard.
Z-Image-Engineer-V6-Q3_K_M.gguf
1.933 GiB
Lightweight option for tighter setups.
Z-Image-Engineer-V6-MXFP4.gguf
2.101 GiB
Alternative compact quantization.
Full recursive validation hashes are in HASHES.sha256.
Quick Start
LM Studio
Download a GGUF quant, load it, and prompt it directly:
Enhance this image prompt for Z-Image Turbo: a unicorn
The comparison examples were generated from direct LM Studio user requests like this, with no separate system prompt. V6_SYSTEM_PROMPT.md is included only as an optional preset for people who want a stricter prompt-only chat setup.
Place a GGUF file into ComfyUI/models/text_encoders/.
Add Z-Engineer CLIP Loader (GGUF) and pick the quant - use the clip output where the stock Z-Image Qwen text encoder would normally go.
Optional: add Z-Engineer Prompt Enhancer (Local) with the same clip to rewrite seed prompts in-process, previewed on the node. No LM Studio or external server required.
A ready-made workflow ships with the node repo: example_workflows/z_image_turbo_z_engineer.json. With ComfyUI-GGUF installed the quant stays quantized in VRAM.
Alternative without the node: add a plain CLIPLoaderGGUF node (ComfyUI-GGUF), set model type to lumina2, and use it as the text encoder only.
V6 was trained with BennyDaBall's SMART DoRA system:
DoRA for direction/magnitude-separated adapter updates.
Entropic regularization for less repetition and broader output variety.
Holographic regularization for cleaner depth-wise feature structure.
Topological regularization for more coherent latent trajectories.
Manifold regularization for stable weight behavior during refinement.
The final V6 build used master-corpus SMART DoRA training, retention pressure, SceneClean SFT32 style restoration, AntiRepeat Binary24 refinement, and a 25% style-restoration / 75% anti-repeat DoRA blend.
