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LatestQwen · Alibaba1.0
Qwen · AlibabaText → Image

Alibaba's Qwen Team Releases Z-Image Diffusion Model

The makers of the popular Qwen language models have published their first open-source text-to-image generator with a permissive Apache 2.0 license.

Jan 23, 2026
NotableApache 2.0
Z-Image

Alibaba's Tongyi lab, the research group behind the well-regarded Qwen family of large language models, has released Z-Image, its first open-source text-to-image model. This release marks the team's expansion from open-source text generation into the competitive field of image synthesis.

The new model is a diffusion-based generator, a common architecture for creating high-fidelity images from text prompts. While the team has not disclosed specific architectural details or training data, the model is positioned as a new, capable foundation for developers and artists working with open-source tools.

Perhaps most significantly, Z-Image is available under the permissive Apache 2.0 license. This allows for broad use, including commercial applications, which could foster a new ecosystem of tools and services built on the model. It represents another major technology player contributing a commercially viable model to the open-source community, following a trend that has accelerated innovation in the space.

The model weights and usage instructions are now available on the Hugging Face Hub. As a new entrant from a team known for high-quality LLMs, Z-Image is a notable addition for anyone tracking the rapidly evolving landscape of generative AI.

Sources

  • Tongyi-MAI/Z-Image

    Hugging Face

    Visit
OlderLuxTTS Delivers Lightweight, Open-Source Speech SynthesisYatharthS · Text → Speech · 9 months agoNewerLingbot-World Animates Images with Camera Controlrobbyant · Image → Video · 9 months ago

Get the model

Hugging Face

Specs

Resolution512×512 to 2048×2048
Size12.3 GB
PrecisionBF16
ArchitectureZImagePipeline
LicenseAPACHE-2.0
Downloads42.9K
Likes1.3K

Can you run it?

Runs on a laptop — about 7.2 GB at FP8.

  • BF16 (as published)

    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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