Overview
Qwen-Image is a 20-billion-parameter MMDiT-based image foundation model family released by QwenLM (Alibaba). It targets two main capabilities: high-fidelity text-to-image generation and robust, identity-preserving image editing. The project provides pretrained checkpoints, Diffusers/HuggingFace pipelines, demos, and a set of engineering and community tools for efficient inference and deployment.
Key Features
- Foundation model (20B MMDiT): designed for both generation and editing tasks.
- Outstanding text rendering: high accuracy and layout fidelity for text in images, with especially strong support for Chinese text.
- Precise image editing: multi-image editing pipelines (e.g., Qwen-Image-Edit-2511) that improve identity preservation and multi-person consistency.
- Multiple releases / checkpoints: iterative monthly releases and major upgrades (examples include Qwen-Image, Qwen-Image-Edit-2509, Qwen-Image-Layered, Qwen-Image-Edit-2511, Qwen-Image-2512) with targeted improvements (human realism, texture detail, text rendering).
- Ecosystem & integrations: first-class support on HuggingFace (Diffusers pipelines and Spaces demos), ModelScope, community acceleration projects (vLLM-Omni, LightX2V, LeMiCa, cache-dit), ComfyUI integration, and LoRA compatibility.
- Deployment & engineering: example multi-GPU API server, Gradio demo, and recommended inference practices (FP16/BF16, generator seeding, prompt enhancement tools).
- Open license: Apache-2.0.
Typical Usage
Qwen-Image ships ready-to-use Diffusers pipelines (e.g., QwenImagePipeline, QwenImageEditPlusPipeline). Example usage patterns include: loading pipeline from HuggingFace, setting torch dtype (bfloat16 on CUDA), running text-to-image generation with true_cfg_scale/guidance, and image editing flows that accept single or multiple images.
Notable Releases & Improvements
- Improved human realism and facial/detail rendering (reducing “AI look”).
- Enhanced natural texture and detail (landscapes, fur, water).
- Stronger geometric reasoning and multi-person editing consistency in the Edit line.
- Continuous community-driven acceleration and Day-0 support from projects like LightX2V and vLLM-Omni.
Examples & Demos
The repository README links to live demos on HuggingFace Spaces for both T2I and Edit, blog posts and technical reports (ArXiv tech report), and sample code for Quick Start and advanced prompt-enhancement utilities.
License & Citation
Qwen-Image is released under the Apache-2.0 license. The project provides a technical report (ArXiv) and a suggested BibTeX citation for academic use.
Who should use it
Researchers and engineers building image generation or editing systems who need:
- a strong open-source baseline for text-in-image composition and Chinese text rendering,
- editable, multi-image editing pipelines,
- integration with the HuggingFace/Diffusers ecosystem and community accelerators.
Links & Community
The project links to HuggingFace model pages, ModelScope, blog posts, ArXiv technical report, and community channels (Discord, WeChat) for support, demos, and further details.
