Renders accurate English and Chinese typography in images, including multi-line layouts, infographics, and professional poster text — with over 90% accuracy in benchmark testing
Generates images at up to 3584×3584 pixels natively, with no post-processing upscale required; Qwen Image 2.0 produces 2048×2048 with microscopic detail
Qwen Image 2.0 consolidates text-to-image generation and image editing into a single 7B-parameter model, supporting style transfer, object manipulation, and scene transformation
Just 2 credits per generation
Qwen Image is a 20-billion-parameter image generation foundation model built by Alibaba's Qwen team on a Multimodal Diffusion Transformer (MMDiT) architecture. Released in August 2025 and updated through early 2026, it addresses one of the most persistent failures of AI image generation: rendering legible, correctly formed text inside images. Most competing models garble words, mix up letters, and fail entirely with non-Latin scripts. Qwen Image achieves over 90% accuracy in bilingual text editing benchmarks — handling complex typography, multi-line layouts, paragraph-level text, and mixed English-Chinese content with high fidelity. This makes it uniquely suited for marketing materials, infographics, posters, and any output where in-image text must be readable.
| Feature | Qwen Image v1 | Qwen Image 2.0 (Feb 2026) |
|---|---|---|
| Parameters | 20 billion | 7 billion |
| Max native resolution | 3584×3584 px | 2048×2048 px |
| Generation + editing | Separate modes | Unified single model |
| Max prompt length | Standard | Up to 1,000 tokens |
| Architecture | MMDiT | MMDiT (encoder: Qwen3-VL) |
| License | Apache 2.0 | Apache 2.0 |
Qwen Image 2.0 cuts the parameter count from 20B to 7B without sacrificing quality — it is faster and more efficient while maintaining competitive benchmark performance. The key architectural upgrade is a dual-encoding mechanism for image editing: Qwen2.5-VL handles semantic encoding (high-level content and relationships), while a Variational Autoencoder (VAE) handles reconstructive encoding (low-level textures and details). This balance means edits change only what you specify while preserving the rest of the image faithfully.
The model separates understanding from generation: the encoder (Qwen3-VL, a vision-language model) processes both text prompts and input images to extract semantic meaning, while a diffusion-based decoder generates the actual pixel output. This design enables the unified generation-and-editing workflow that is central to Qwen Image 2.0.
Text rendering capability comes from a progressive curriculum learning strategy during training:
The training corpus is approximately 55% nature images, 27% design content, 13% human portraits, and 5% synthetic text rendering data. This mix explains the model's strengths in photorealistic natural scenes alongside precise typographic output.
2
クレジット
1クレジット = $0.012
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