moonbox-dev/Z-Image-Turbo-GGUF

Z-Image-Turbo GGUF

GGUF builds of Z-Image-Turbo, made by Moonbox for on-device use. Each precision is one file at the root of this repository; pick one by the memory you have. Only the model's own weights are converted: the other parts of the pipeline (text encoder, VAE, tokenizer) come from the original repository unless the section below says otherwise.

Files

The last column is how far each file is from the original BF16 weights: the relative RMS error of its dequantized weights, averaged over all parameters. Image-quality scores against BF16 outputs are added as each precision passes the image eval.

Precision	File	Size	sha256	Checked against BF16
F16	z-image-turbo-F16.gguf	12.31 GB	996ed5b13fc1	0.00000007 % (7.0e-10)
BF16	z-image-turbo-BF16.gguf	12.31 GB	9ccc4fb8b845	exact
Q8_0	z-image-turbo-Q8_0.gguf	7.22 GB	f163d60b0eb4	0.50 %
Q6_K	z-image-turbo-Q6_K.gguf	5.91 GB	a30c83a16c1a	1.6 %
Q5_K_M	z-image-turbo-Q5_K_M.gguf	5.52 GB	9f2b67af63ca	2.5 %
Q4_K_M	z-image-turbo-Q4_K_M.gguf	4.98 GB	2226ff5614e3	4.7 %

How to run

These files are the Z-Image-Turbo diffusion transformer only. Text encoder (Qwen3-4B) and VAE are separate: qwen_3_4b.safetensors from Comfy-Org/z_image_turbo (split_files/text_encoders), ae.safetensors from Comfy-Org/z_image_turbo (split_files/vae) = FLUX.1's VAE.

stable-diffusion.cpp (the runtime our benchmark uses, commit 465adf3):

sd-cli --diffusion-model z-image-turbo-Q8_0.gguf --llm qwen_3_4b.safetensors --vae ae.safetensors \
  -p "a lovely cat holding a sign" --steps 8 --cfg-scale 1.0 --sampling-method euler -W 1024 -H 1024

Z-Image-Turbo is distilled: 8 steps, CFG 1.

ComfyUI with ComfyUI-GGUF: ComfyUI-GGUF loads it as lumina2.

Which file: Q8_0 is the closest to the original at about half the size; Q6_K and Q5_K_M trade a little fidelity for memory; Q4_K_M is the smallest. BF16 / F16 are the unquantized weights.

License. Apache-2.0 (in this repository).

How these were made

From Z-Image-Turbo at revision f332072aa7: the original BF16 weights converted to GGUF F16 / BF16, then quantized with llama.cpp's llama-quantize. Every file was checked against the original before upload; the sha256 column above is what the files must hash to.

License

Apache-2.0. These files are a derivative of the original model and carry its license: it is copied in this repository, and it governs your use of these files. The original model card follows unchanged.

---

Original model card

⚡️- Image
_{^{An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer}}

                 

Welcome to the official repository for the Z-Image（造相）project!

✨ Z-Image

Z-Image is a powerful and highly efficient image generation model family with 6B parameters. Currently there are four variants:

• 🚀 Z-Image-Turbo – A distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers ⚡️sub-second inference latency⚡️ on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.

• 🎨 Z-Image – The foundation model behind Z-Image-Turbo. Z-Image focuses on high-quality generation, rich aesthetics, strong diversity, and controllability, well-suited for creative generation, fine-tuning, and downstream development. It supports a wide range of artistic styles, effective negative prompting, and high diversity across identities, poses, compositions, and layouts.

• 🧱 Z-Image-Omni-Base – The versatile foundation model capable of both generation and editing tasks. By releasing this checkpoint, we aim to unlock the full potential for community-driven fine-tuning and custom development, providing the most "raw" and diverse starting point for the open-source community.

• ✍️ Z-Image-Edit – A variant fine-tuned on Z-Image specifically for image editing tasks. It supports creative image-to-image generation with impressive instruction-following capabilities, allowing for precise edits based on natural language prompts.

📥 Model Zoo

Model	Pre-Training	SFT	RL	Step	CFG	Task	Visual Quality	Diversity	Fine-Tunability	Hugging Face	ModelScope
Z-Image-Omni-Base	✅	❌	❌	50	✅	Gen. / Editing	Medium	High	Easy	To be released	To be released
Z-Image	✅	✅	❌	50	✅	Gen.	High	Medium	Easy
Z-Image-Turbo	✅	✅	✅	8	❌	Gen.	Very High	Low	N/A
Z-Image-Edit	✅	✅	❌	50	✅	Editing	High	Medium	Easy	To be released	To be released

🖼️ Showcase

📸 Photorealistic Quality: Z-Image-Turbo delivers strong photorealistic image generation while maintaining excellent aesthetic quality.

📖 Accurate Bilingual Text Rendering: Z-Image-Turbo excels at accurately rendering complex Chinese and English text.

💡 Prompt Enhancing & Reasoning: Prompt Enhancer empowers the model with reasoning capabilities, enabling it to transcend surface-level descriptions and tap into underlying world knowledge.

🧠 Creative Image Editing: Z-Image-Edit shows a strong understanding of bilingual editing instructions, enabling imaginative and flexible image transformations.

🏗️ Model Architecture

We adopt a Scalable Single-Stream DiT (S3-DiT) architecture. In this setup, text, visual semantic tokens, and image VAE tokens are concatenated at the sequence level to serve as a unified input stream, maximizing parameter efficiency compared to dual-stream approaches.

📈 Performance

According to the Elo-based Human Preference Evaluation (on Alibaba AI Arena), Z-Image-Turbo shows highly competitive performance against other leading models, while achieving state-of-the-art results among open-source models.

Click to view the full leaderboard

🚀 Quick Start

Install the latest version of diffusers, use the following command:

^{Click here for details for why you need to install diffusers from source}

We have submitted two pull requests (#12703 and #12715) to the 🤗 diffusers repository to add support for Z-Image. Both PRs have been merged into the latest official diffusers release. Therefore, you need to install diffusers from source for the latest features and Z-Image support.

pip install git+https://github.com/huggingface/diffusers

import torch
from diffusers import ZImagePipeline

# 1. Load the pipeline
# Use bfloat16 for optimal performance on supported GPUs
pipe = ZImagePipeline.from_pretrained(
    "Tongyi-MAI/Z-Image-Turbo",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=False,
)
pipe.to("cuda")

# [Optional] Attention Backend
# Diffusers uses SDPA by default. Switch to Flash Attention for better efficiency if supported:
# pipe.transformer.set_attention_backend("flash")    # Enable Flash-Attention-2
# pipe.transformer.set_attention_backend("_flash_3") # Enable Flash-Attention-3

# [Optional] Model Compilation
# Compiling the DiT model accelerates inference, but the first run will take longer to compile.
# pipe.transformer.compile()

# [Optional] CPU Offloading
# Enable CPU offloading for memory-constrained devices.
# pipe.enable_model_cpu_offload()

prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."

# 2. Generate Image
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    num_inference_steps=9,  # This actually results in 8 DiT forwards
    guidance_scale=0.0,     # Guidance should be 0 for the Turbo models
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]

image.save("example.png")

🔬 Decoupled-DMD: The Acceleration Magic Behind Z-Image

Decoupled-DMD is the core few-step distillation algorithm that empowers the 8-step Z-Image model.

Our core insight in Decoupled-DMD is that the success of existing DMD (Distributaion Matching Distillation) methods is the result of two independent, collaborating mechanisms:

• CFG Augmentation (CA): The primary engine 🚀 driving the distillation process, a factor largely overlooked in previous work.
• Distribution Matching (DM): Acts more as a regularizer ⚖️, ensuring the stability and quality of the generated output.

By recognizing and decoupling these two mechanisms, we were able to study and optimize them in isolation. This ultimately motivated us to develop an improved distillation process that significantly enhances the performance of few-step generation.

🤖 DMDR: Fusing DMD with Reinforcement Learning

Building upon the strong foundation of Decoupled-DMD, our 8-step Z-Image model has already demonstrated exceptional capabilities. To achieve further improvements in terms of semantic alignment, aesthetic quality, and structural coherence—while producing images with richer high-frequency details—we present DMDR.

Our core insight behind DMDR is that Reinforcement Learning (RL) and Distribution Matching Distillation (DMD) can be synergistically integrated during the post-training of few-step models. We demonstrate that:

• RL Unlocks the Performance of DMD 🚀
• DMD Effectively Regularizes RL ⚖️

⏬ Download

pip install -U huggingface_hub
HF_XET_HIGH_PERFORMANCE=1 hf download Tongyi-MAI/Z-Image-Turbo

📜 Citation

If you find our work useful in your research, please consider citing:

@article{team2025zimage,
  title={Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer},
  author={Z-Image Team},
  journal={arXiv preprint arXiv:2511.22699},
  year={2025}
}

@article{liu2025decoupled,
  title={Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield},
  author={Dongyang Liu and Peng Gao and David Liu and Ruoyi Du and Zhen Li and Qilong Wu and Xin Jin and Sihan Cao and Shifeng Zhang and Hongsheng Li and Steven Hoi},
  journal={arXiv preprint arXiv:2511.22677},
  year={2025}
}

@article{jiang2025distribution,
  title={Distribution Matching Distillation Meets Reinforcement Learning},
  author={Jiang, Dengyang and Liu, Dongyang and Wang, Zanyi and Wu, Qilong and Jin, Xin and Liu, David and Li, Zhen and Wang, Mengmeng and Gao, Peng and Yang, Harry},
  journal={arXiv preprint arXiv:2511.13649},
  year={2025}
}