Qwen-Image
A 20B-parameter MMDiT image foundation model for complex text rendering and precise image editing.
Performance
Core Features
High‑fidelity Text Rendering
Accurate English and Chinese text generation with layout‑aware composition, multi‑line and paragraph‑level rendering, and faithful small‑font details.
Precise Image Editing
Enhanced multi‑task training enables style transfer, object add/remove, text editing, detail enhancement, and pose adjustment with strong edit consistency.
Benchmark‑leading Performance
SOTA‑level results on GenEval, DPG and OneIG‑Bench for generation, and strong performance on GEdit/ImgEdit/GSO for editing; excels in long‑text rendering, especially Chinese.
Multilingual & Multi‑style
From photorealistic scenes to impressionist painting, anime, and minimalist design, Qwen‑Image adapts to diverse creative prompts.
Professional Content Creation
Easily create posters, slide decks, and infographics. Lower the barrier for visual content production and unlock new applications.
Open Source and Free
Released under the Apache‑2.0 license. Free to use and suitable for commercial deployments, fostering an open and sustainable AI ecosystem.
What is Qwen-Image?
Qwen-Image is a 20B-parameter MMDiT image foundation model by Qwen (Alibaba). It delivers state-of-the-art performance in complex text rendering and precise image editing while maintaining strong general image generation across diverse styles. Qwen-Image supports both English and Chinese, producing high-fidelity typography, layout consistency, and reliable adherence to prompts. The project is open-sourced under the Apache 2.0 license with model weights available for practical use.
Learn more on the official blog, explore code on GitHub, and try the model on Hugging Face.
Key Features
- High‑fidelity text rendering: Multi‑line and paragraph‑level text in English and Chinese, layout‑aware composition, reliable small‑font rendering.
- Consistent image editing: Text edit, object add/remove, style transfer, detail enhancement, and pose adjustment with strong edit consistency.
- Versatile image generation: From photorealistic visuals to anime and minimalist design; responsive to diverse prompts and brand use‑cases.
- Benchmark leadership: SOTA‑level results across GenEval/DPG/OneIG‑Bench (generation) and GEdit/ImgEdit/GSO (editing); excels at long‑text rendering, especially Chinese.
- Open & production‑ready: Apache‑2.0 license, weights released; native ComfyUI support for node‑based workflows.
Who Is It For?
- Designers & marketers: Posters, slides, infographics, and brand visuals with accurate multilingual text.
- Creators & educators: Comics, social graphics, explainer visuals, lecture slides, and knowledge sharing.
- Product & growth teams: Landing pages, feature banners, and localized assets with precise text rendering.
- Developers & researchers: ComfyUI workflows, custom pipelines, controllable editing, and reproducible experiments.
How to Use Qwen‑Image
- Try in your browser: Use the official Hugging Face Space or Qwen Chat (choose “Image Generation”).
- ComfyUI native workflow: Load the official workflow and checkpoints; Qwen‑Image is supported natively for node‑first pipelines. See the ComfyUI guide.
- Local / programmatic use: Pull weights from Hugging Face or ModelScope; integrate via GitHub resources and inference examples.
- For text rendering, describe layout, line breaks, and font weight when relevant.
- Keep brand text within focal regions; specify relative size (e.g., “small caption” vs “title”).
- For editing, be explicit about the region, operation, and the target text/object.
What creators say about Qwen‑Image
“Finally a model that writes Chinese and English the way designers expect—multi‑line, paragraph‑level, and still readable at small sizes.”
“We replaced manual Photoshop edits for banners with Qwen‑Image. Edits stay consistent across iterations and cut our time by 60%.”
“The ComfyUI native workflow just works. I built a localization pipeline that swaps bilingual text on the fly.”
“For lecture slides and infographics, adherence is key. Qwen‑Image keeps layout and typography intact, even with long captions.”
“Open‑source under Apache‑2.0 sealed the deal. We can ship it in production and keep our stack clean.”
“Compared with other models, text rendering is the clear differentiator—especially Chinese. It also edits reliably without drifting.”
FAQ
What is Qwen‑Image’s license?
Apache 2.0. It is suitable for commercial use with proper attribution where applicable.
Does it really handle complex text?
Yes. It supports multi‑line, paragraph‑level, and small‑size text in both English and Chinese with strong prompt adherence and layout consistency.
How does it compare to other models?
Qwen‑Image delivers SOTA‑level results in text rendering and competitive image editing while being fully open‑source and accessible.
Can I use it for bilingual content?
Yes. It handles Chinese/English mixed text naturally and maintains layout and typography consistency.
Is there a web demo?
Yes. Try the official Hugging Face Space or Qwen Chat and choose “Image Generation”.
Is ComfyUI supported?
Yes. Qwen‑Image is natively supported with a ready‑to‑use workflow. See the ComfyUI documentation.
Where can I get the weights?
Weights are available on Hugging Face and ModelScope; code and resources on GitHub.
Any safety or moderation guidance?
Follow the platform’s policies and local regulations. If deploying at scale, implement your own content moderation and logging.
Hardware requirements for local inference?
Requirements vary by precision and batch size. For best performance, use modern NVIDIA GPUs with sufficient VRAM.