unsloth/Z-Image-Turbo
text-to-image model by unsloth
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
⚡️- ImageAn Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer [](https://tongyi-mai.github.io/Z-Image-blog/)  [](https://github.com/Tongyi-MAI/Z-Image)  [](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo)  [](https://huggingface.co/spaces/Tongyi-MAI/Z-Image-Turbo)  [](https://huggingface.co/spaces/akhaliq/Z-Image-Turbo)  [](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)  [](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=469191&modelType=Checkpoint&sdVersion=ZIMAGETURBO&modelUrl=modelscope%3A%2F%2FTongyi-MAI%2FZ-Image-Turbo%3Frevision%3Dmaster)  [](assets/Z-Image-Gallery.pdf)  [](https://modelscope.cn/studios/Tongyi-MAI/Z-Image-Gallery/summary)  Welcome to the official repository for the Z-Image（造相）project! Z-Image is a powerful and highly efficient image generation model family with 6B parameters. Currently there are four variants: 📸 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. We adopt a Scalable Single-Stream…
Read the full model card ↗ · Preview checked 2026-10-29T03:22:47.290Z
Inside the original model card — Document outline
- ✨ Z-Image
- 📥 Model Zoo
- 🖼️ Showcase
- 🏗️ Model Architecture
- 📈 Performance
- 🚀 Quick Start
- 🔬 Decoupled-DMD: The Acceleration Magic Behind Z-Image
- 🤖 DMDR: Fusing DMD with Reinforcement Learning
- ⏬ Download
- 📜 Citation
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
unsloth/Z-Image-Turbo is a text-to-image model repository published by unsloth on Hugging Face. The source reports the diffusers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-to-image
- Library
- diffusers
- Recent downloads (30 days)
- 227,979
- Cumulative likes
- 6
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 6,154,908,736
- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-08-06T14:04:40.000Z
- Last modified
- 2026-08-06T14:05:29.000Z
Compatibility and lineage
Reported languages: en
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.
Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:31.475Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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