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LatestQwen · AlibabaQwen-Image
Qwen · AlibabaText → Image

Qwen releases open model for text-in-image generation

The new Apache 2.0 diffusion model from Alibaba's Qwen team focuses on accurately rendering both English and Chinese characters within generated images.

Aug 2, 2025
Major releaseApache 2.0
Qwen-Image

The Qwen team at Alibaba has released Qwen-Image, a new open-source model for generating images from text prompts. Released under the permissive Apache 2.0 license, the model aims to solve one of the most common frustrations with AI image generation: rendering legible text.

While many popular diffusion models struggle to create coherent letters and words, Qwen-Image is specifically trained to produce readable text within its creations. The model demonstrates a strong capability for rendering characters in both English and Chinese, a significant challenge given the complexity of the scripts.

This focus on typography is a practical step forward for generative AI. The ability to reliably create images with accurate text opens up new possibilities for designers, marketers, and developers building tools for ad copy mockups, product designs, or social media content where text is a critical component.

The model is based on a standard diffusion architecture, pairing a text encoder with an image generator to interpret prompts. The full model weights and usage instructions are available for download on the Qwen team's Hugging Face repository.

Sources

  • Qwen/Qwen-Image

    Hugging Face

    Visit
  • Qwen-Image

    civitai

    Visit
OlderQwen Releases Compact 30B MoE for Coding AgentsQwen · Alibaba · Code · last yearNewerNVIDIA Releases 600M Parakeet for Speech RecognitionNVIDIA · Speech → Text · last year

Get the model

Hugging Facecivitai

Specs

Parameters20B
Resolution1328×1328
Size40.9 GB
PrecisionBF16
ArchitectureQwenImagePipeline
LicenseAPACHE-2.0
Downloads324.4K
Likes2.7K

Can you run it?

Runs on a 24 GB GPU at FP8.

  • BF16 (as published)

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    41.9 GB
  • FP8

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    21.4 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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