gimly13/z-image-cyberrealistic-v4-gguf

CyberRealistic Z-Image Turbo v4.0 — GGUF FULL Bundle

Quantized GGUF version of cyberdelia/cyberrealistic_zimage Converted for use with ComfyUI and with stable-diffusion.cpp directly.

🖼️ Examples

All generated locally on a GTX 1070 (8 GB VRAM), 1024×1024.

💡 Tip: These PNGs have the ComfyUI workflow and prompt embedded! Don't just copy the image from the browser preview. Go to the Files and versions tab, open the image there, and click "Download" to get the original file with all metadata intact.

Fantasy character — 1024×1024, 6 steps	Wet hair, rain — 1024×1024, 6 steps
Interior scene with steam — 1024×1024, 6 steps	Golden hour portrait — 1024×1024, 6 steps
Text + cyberpunk atmosphere — 1024×1024, 6 steps	Product + text rendering — 1024×1024, 6 steps

📦 What's in this repository

Component	File	Size	Notes
Diffusion model (light)	CyberRealistic_zit_v4.0-Q3_K.gguf	4.31 GB	Q3_K quant — for very tight VRAM budgets
Diffusion model (balanced)	CyberRealistic_zit_v4.0-Q4_K.gguf	5.15 GB	Q4_K quant — recommended for 8 GB VRAM
Diffusion model (compact)	CyberRealistic_zit_v4.0-Q4_K_S.gguf	4.83 GB	Q4_K_S — slightly smaller than Q4_K, good middle ground
Diffusion model (quality)	CyberRealistic_zit_v4.0-Q5_K.gguf	5.68 GB	Q5_K quant — better detail, still fits 8 GB
Diffusion model (high)	CyberRealistic_zit_v4.0-Q6_K.gguf	6.05 GB	Q6_K quant — for 12+ GB cards
Diffusion model (max)	CyberRealistic_zit_v4.0-Q8_0.gguf	7.34 GB	Q8_0 quant — near-lossless, needs 12+ GB
Text encoder (light)	clip/Qwen3-4B-Q3_K_M.gguf	2.08 GB	If you're very tight on VRAM
Text encoder (best)	clip/Qwen3-4B-Q4_K_M.gguf	2.50 GB	Optimal balance for 8 GB cards
Text encoder (quality)	clip/Qwen3-4B-Q6_K.gguf	3.31 GB	For 12+ GB cards
VAE	vae/ae.safetensors	335 MB	16-channel Z-Image VAE

For a GTX 1070 (8 GB), the recommended combo is: diffusion Q4_K + text encoder Q4_K_M + VAE — total ~8 GB, fits with room to spare.

Note on quantization naming: Z-Image (Lumina2 architecture) does not benefit from mixed _S/_M/_L quantization recipes — llama-quantize produces byte-identical files to the base _K types. This repository therefore publishes only base quants: Q3_K, Q4_K, Q5_K, Q6_K, Q8_0.

Why this file

The original checkpoint is distributed in FP16 (safetensors, ~11.5 GB), which does not fit into 8 GB VRAM. This Q4_K quantization brings the diffusion model down to ~3.2 GB, leaving room for the text encoder and VAE on mid-range GPUs.

What you need besides this file

Z-Image Turbo is a composite model. This GGUF contains only the diffusion model. To generate images you also need:

1. Text encoder: Qwen3-4B-Q4_K_M.gguf (or the official qwen_3_4b.safetensors — both work with Power Nodes).
2. VAE: ae.safetensors from the original Z-Image release (16-channel VAE).

🚀 ComfyUI workflow

Important: the standard ComfyUI GGUF nodes (UnetLoader (GGUF), CLIPLoader (GGUF)) do not work with Z-Image diffusion models converted via convert.py + llama-quantize. You'll get: RuntimeError: Error(s) in loading state_dict for NextDiT: size mismatch for x_pad_token: copying a param with shape torch.Size([3840]) ... size mismatch for cap_pad_token: copying a param with shape torch.Size([3840]) ...

The working path is martin-rizzo/ComfyUI-ZImagePowerNodes. It loads Z-Image GGUF directly and applies the correct latent/conditioning shapes.

Required nodes

Install the Power Nodes pack:

bash cd ComfyUI/custom_nodes git clone https://github.com/martin-rizzo/ComfyUI-ZImagePowerNodes.git

Recommended sampler settings steps: 4 (Turbo is distilled; anything above ~8 starts to burn)

cfg: 1.0

sampler: euler

turbo_creativity: no

old_scheduler: no

noise_injection: no

alternative_refiner: no

width/height: 1024 (must be multiples of 32)

Alternative: standard ComfyUI GGUF nodes If you really want to use the built-in UnetLoader (GGUF) instead of Power Nodes, you need city96/ComfyUI-GGUF on PR #392 or later:

cd ComfyUI/custom_nodes/ComfyUI-GGUF git fetch origin pull/392/head:pr392 git checkout pr392

That fixes the x_pad_token shape mismatch. In practice, though, Power Nodes is more reliable for Z-Image Turbo, and that's what's been verified here

🙏 Credits Original model: cyberdelia/cyberrealistic_zimage

Base architecture: Z-Image / Z-Image Turbo by Tongyi Lab (Alibaba)

ComfyUI nodes: martin-rizzo/ComfyUI-ZImagePowerNodes

Conversion toolchain: city96/ComfyUI-GGUF + llama.cpp fork

Runtime: leejet/stable-diffusion.cpp

📄 License All components in this repository are distributed under Apache 2.0:

Original diffusion checkpoint (cyberrealistic_zimage) — Apache 2.0

Qwen3-4B text encoder — Apache 2.0 (Alibaba)

Z-Image VAE (ae.safetensors) — Apache 2.0 (Tongyi Lab)

See cyberdelia/cyberrealistic_zimage for details on the original model.