# Qwen (T2I)

> Qwen (T2I) is available on Artifio.ai — a unified AI workspace with a single pay-as-you-go wallet.

- Canonical page: https://artifio.ai/models/qwen-text-to-image
- Run it: https://artifio.ai/create/qwen-text-to-image
- Modality: prompt to image
- Model brand: Qwen
- Pricing: from about $0.005 per generation (exact cost shown in the workspace before you confirm; varies with selected options)
- Typical generation time: ~25s (catalogue estimate)
- Full pricing table: https://artifio.ai/pricing/models

## About Qwen (T2I)

Qwen (T2I) runs Qwen-Image, the 20B-parameter MMDiT image foundation model that Alibaba's Qwen team released under Apache 2.0 on August 4, 2025. It was built for one problem in particular: putting legible, correctly spelled text inside generated images, from single-word signage to multi-line paragraph layouts, in both English and Chinese. The technical report pairs the diffusion backbone with a Qwen2.5-VL text encoder and a curriculum that trains from simple captions up to paragraph-level descriptions.

The same repository line later received the Qwen-Image-2512 refresh (December 31, 2025), which Alibaba's changelog says improved human realism and texture quality. On Artifio the model sits in one workspace beside 100+ others, paid per generation from a single wallet with no per-model subscription.

## Features

- 20B-parameter MMDiT architecture with a Qwen2.5-VL encoder, released with open Apache 2.0 weights on Hugging Face and ModelScope
- Complex text rendering inside images: multi-line layouts, paragraph-level semantics, and fine-grained typographic detail in English and Chinese
- Seven official aspect-ratio presets, from 1328x1328 (1:1) through 1664x928 (16:9), 928x1664 (9:16), 4:3, 3:4, 3:2, and 2:3
- Style range covering photorealism, anime, and illustration from the same checkpoint
- Training recipe documented in a public technical report (arXiv 2508.02324), including the data pipeline for text-rendering supervision
- Family refresh Qwen-Image-2512 (December 2025) targeting human realism and texture quality

## What people use it for

- Posters, slides, and infographics where the headline and body text must come out readable on the first pass
- Bilingual marketing assets that mix Chinese and English typography in one image
- Product and brand mockups with embedded labels, signage, or packaging text
- General text-to-image work across photoreal and anime styles without switching checkpoints
- Storyboard and comic frames where captions belong inside the picture

## Reported strengths

- Chinese text generation outperformed existing state-of-the-art models by a significant margin at release, per the launch report
- Benchmark results across GenEval, DPG, and OneIG-Bench placed it ahead of contemporary open models on general generation as well as on text
- Apache 2.0 licensing keeps the weights inspectable and commercially usable
- One model covers a wide stylistic spread, which cuts the need to route prompts between specialist checkpoints

## Reported limitations

- Official generation presets top out around 1.4 megapixels (1328x1328 at 1:1); native 2K output only arrived with the separate Qwen-Image-2.0 generation in February 2026
- Alibaba's own Qwen-Image-2512 release notes list enhanced human realism and texture among the fixes, which points at soft spots in the original August 2025 weights for faces and skin
- At 20 billion parameters the checkpoint is one of the heavier open image models, so full-precision local inference needs high-end GPU memory

## Sources

1. Qwen-Image: Crafting with Native Text Rendering (Qwen (Alibaba)): https://qwenlm.github.io/blog/qwen-image/
2. Qwen/Qwen-Image model card (Hugging Face (Qwen)): https://huggingface.co/Qwen/Qwen-Image
3. Qwen-Image Technical Report (arXiv): https://arxiv.org/abs/2508.02324
4. QwenLM/Qwen-Image repository (GitHub (QwenLM)): https://github.com/QwenLM/Qwen-Image

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