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LatestQwen · AlibabaTurbo
Qwen · AlibabaText → Image

Alibaba Releases Z-Image-Turbo, A Fast Open Image Model

The new text-to-image model from the team behind Qwen uses a diffusion transformer to generate high-resolution images in just a single step.

Nov 25, 2025
NotableApache 2.0
Z-Image-Turbo

Alibaba's Tongyi-MAI team, the creators of the Qwen model family, has released Z-Image-Turbo, a new open-source text-to-image model designed for speed. The model stands out for its ability to generate a 1024x1024 pixel image in a single inference step, a significant departure from the multi-step sampling process required by most diffusion models.

This efficiency is achieved by using a Diffusion Transformer (DiT) architecture. This approach, which has gained prominence in models like Sora, moves away from the U-Net architectures common in earlier Stable Diffusion versions. By building on a transformer backbone, Z-Image-Turbo can capture complex relationships in text prompts more effectively and streamline the generation process.

The combination of speed and a modern architecture makes Z-Image-Turbo a compelling new option for developers. The model and its weights are available on Hugging Face under the permissive Apache 2.0 license, which allows for commercial use.

For the open-source AI ecosystem, Z-Image-Turbo represents another powerful, commercially-friendly alternative to established models. Its single-step generation offers a practical advantage for applications where low latency is critical, such as real-time previews or interactive image creation tools.

Sources

  • Tongyi-MAI/Z-Image-Turbo

    Hugging Face

    Visit
  • Z Image Turbo

    civitai

    Visit
OlderBlack Forest Labs Releases Open-Source FLUX.2 Image ModelBlack Forest Labs · Text → Image · 11 months agoNewerFlashLabs Releases Chroma-4B, an Any-to-Any ModelFlashLabs · Any-to-Any · 11 months ago

Get the model

Hugging Facecivitai

Specs

Size24.6 GB
PrecisionFP32
ArchitectureZImagePipeline
LicenseAPACHE-2.0
Downloads747.7K
Likes5.5K

Can you run it?

Runs on a laptop — about 7.2 GB at FP8.

  • FP32 (as published)

    32 GB GPU (RTX 5090) · 48 GB Mac

    25.6 GB
  • 16-bit

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    13.3 GB
  • FP8

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    7.2 GB
Your machine
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text → Image

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