Qwen-Image-2512: Text-Accurate Image Generation

Qwen-Image-2512: Text-Accurate Image Generation

TL;DR

Qwen-Image-2512 is a 20B-parameter Multimodal Diffusion Transformer for text-to-image generation. The update to Qwen-Image improves human realism, natural detail, text rendering, layout accuracy, and text-image composition.

With support for complex English and Chinese text, Qwen-Image-2512 is suited to typography-heavy assets such as presentation slides, infographics, posters, and interface concepts.

What the Model Is

Qwen-Image-2512, now listed on AIOZ AI, is a text-to-image foundation model developed by the Qwen team.

It builds on Qwen-Image with three main improvements:

  • More realistic people: Richer facial, age, hair, and environmental details reduce the artificial appearance common in generated portraits
  • Finer natural textures: Landscapes, water, vegetation, fur, and other materials receive more detailed rendering
  • Stronger text rendering: Text accuracy, layout quality, and the integration of text with surrounding visual content are improved

How the Model Architecture Works

It inherits the 20B-parameter Multimodal Diffusion Transformer (MMDiT) architecture of Qwen-Image.

The architecture combines three main components:

  • A frozen Qwen2.5-VL 7B model provides semantic understanding of the prompt
  • The MMDiT backbone jointly processes text and image representations during denoising
  • A Wan2.1 VAE encodes and reconstructs visual information in latent space

Qwen-Image also introduces Multimodal Scalable Rotary Position Embedding (MSRoPE). This position-encoding system helps the model represent text and image positions within a shared structure, supporting spatial relationships, varied image resolutions, and complex compositions.

Native Text Rendering

Text rendering is one of Qwen-Image-2512's defining capabilities.

The model can generate alphabetic and logographic writing, including English and Chinese, within visually structured compositions. It is designed to handle:

  • Multi-line typography
  • Dense Chinese characters
  • Text embedded within posters and graphics
  • Labels and headings inside infographics
  • Slide and interface-style layouts
  • Text-image composition with multiple visual elements

Inputs and Output

The model provides five generation inputs:

  • prompt: Natural-language description of the scene, objects, style, lighting, and composition
  • height: Output image height in pixels
  • width: Output image width in pixels
  • steps: Number of inference steps; higher values can improve quality while increasing generation time
  • cfg_scale: Controls how closely the image follows the prompt; higher values increase prompt adherence but may reduce creativity

The model returns output_image, a generated image in PNG format.

Key Technical Details

  • Model: Qwen-Image-2512
  • Model type: Text-to-image foundation model
  • Architecture: Multimodal Diffusion Transformer
  • Diffusion model scale: 20B parameters
  • Semantic encoder: Frozen Qwen2.5-VL 7B
  • Position encoding: MSRoPE
  • Visual autoencoder: Wan2.1 VAE
  • Supported text strengths: English and Chinese
  • Generation controls: Prompt, height, width, inference steps, and CFG scale
  • Output: PNG image
  • License: Apache License 2.0

Download It on AIOZ AI

20B parameters, bilingual text rendering, structured composition, and improved realism: Qwen-Image-2512 brings language-aware image generation to design-heavy workflows.

Download Qwen-Image-2512 on AIOZ AI and start generating images where text and visual structure work together.

FAQ

Q1: What is Qwen-Image-2512?

It is a 20B-parameter text-to-image model designed for complex text rendering.

Q2: Which languages can it render?

Its standout text-rendering capabilities cover English and Chinese.

Q3: What changed from the original Qwen-Image release?

The 2512 update improves human realism, natural textures, text accuracy, layout quality, and text-image composition.