1. Game-Changing Chinese Rendering Precision, Solving Industry Pain Points​

• •​Precise Glyphs Without Garbled Characters​: Powered by a 20-billion parameter MMDiT architecture (Multimodal Diffusion Transformer) and specialized Chinese training data cleaning, Qwen-Image achieves a single-character accuracy rate of up to 58.3% when generating complex Chinese text. This completely solves the traditional model issues of Chinese text rendering as "blurry pixelation" or having "missing strokes." For example, when generating calligraphic fonts, it accurately renders fine details such as cursive dry-brush strokes (feibai) and varying ink density rather than applying simple texture pastes.
• •​Multi-Line Layout and Mixed-Language Support​: Supports paragraph-level long-text automatic layout (e.g., posters with over 300 characters), achieving high semantic and layout harmony during mixed Chinese-English rendering. For example, it can simultaneously generate neat handwritten Chinese alongside stylized English titles while naturally integrating the text with background lighting and shadows.

2. Professional Design Adaptability, Empowering Typography Creation​

• •​Dynamic Structural Optimization​: The model automatically applies key typography design principles, such as: •​Density Adjustment​: Automatically thins internal lines for characters with dense, complex strokes (e.g., "墨") to avoid visual clutter; thickens outlines for characters with sparse strokes (e.g., "瀚") to enhance legibility. •​Balance of Gravity and Negative Space​: Guided by the classical philosophy of "sparse enough for a horse to run through, dense enough to keep out the wind," it uses negative space (character spacing, whitespace) to enhance overall balance, giving typography both artistic tension and readability.
• •​Stylistic Expansion​: Supports switching across dozens of styles—from minimalist sans-serif to traditional calligraphy (e.g., cursive script "奔赴山海")—and adapts font textures according to scene requirements (e.g., cinnabar seal impressions, metallic luster), satisfying diverse design scenarios like posters, logos, and presentation slides.

3. End-to-End Workflow Integration, Boosting Design Efficiency​

• •​Semantic-Driven Generation​: The model deeply comprehends the connotations of Chinese prompts. For instance, entering "well-proportioned cursive script '墨瀚' with negative space background" automatically generates font structures compliant with design standards without requiring manual stroke adjustments.
• •​Sequential Editing Consistency​: When modifying font styles or adding/removing elements, "memory anchoring" technology keeps the core subject ID stable (e.g., maintaining character structure while adjusting stroke tip thickness), preventing the classic issue in traditional models where "every edit breaks the image."
  

Recommended Use Cases​:

✅ High-precision Chinese poster/cover design

✅ Calligraphic art fonts and multi-language layout

📐 ​Recommended Aspect Ratios (Resolution):​​

• ​Landscape Cover (16:9):​​ 1664×928
• ​Content Card (4:3):​​ 1472×1140
• Square Image (1:1):​​ 1328×1328
• Vertical Poster (3:4):​​ 1140×1472
• ​Portrait Cover (9:16):​​ 928×1664

Sampler: Euler a, DPM++2M Karras

Sampling Steps: 45

Recommended Resolution: 1024*1024 (Feel free to experiment with other sizes)

CFG Scale: 4.5

Reposted from: Xianchuangyipai @ Qw-MoHan_ Chinese Typography Design Model