4.2 KiB
Few-Shot Example: Showcase — Qwen-Image Text to Image
This example demonstrates the ideal output for a showcase-type content page.
INPUT: Template Metadata
| Field | Value |
|---|---|
| Name | qwen_image |
| Title | Qwen-Image Text to Image |
| Description | Generate high-quality images with excellent multilingual text rendering using Qwen-Image 20B model. |
| Media Type | image |
| Tags | Text to Image, Image |
| Models | Qwen-Image |
OUTPUT: Generated Content
extendedDescription
Qwen-Image generates stunning, detailed images with remarkably accurate text rendering across multiple languages in Hanzo Studio. Built on a 20-billion parameter MMDiT architecture by Alibaba's Qwen team, this workflow produces crisp visuals with a level of typographic precision that sets it apart — rendering clean, legible text in English, Chinese, Korean, and Japanese directly within generated images.
What makes this workflow special is its combination of multilingual text accuracy and artistic versatility. Qwen-Image handles everything from photorealistic scenes to stylized illustrations, and its text rendering remains sharp and coherent even at smaller font sizes. The included 8-step acceleration LoRA dramatically reduces generation time, while the FP8 quantized diffusion model keeps VRAM usage manageable at roughly 86% on an RTX 4090. A distilled variant is also available for faster iteration on lower-end hardware.
Whether you're a designer creating multilingual marketing assets, a content creator producing visuals with embedded typography, or an artist exploring text-integrated compositions, Qwen-Image delivers professional-grade output. Create posters with perfectly rendered headlines, social media graphics with clean captions, or concept art with in-scene signage — all generated directly from a text prompt.
howToUse
- Open the Workflow: Load the Qwen-Image Text to Image template from the Hanzo Studio workflow browser.
- Enter Your Prompt: Describe the image you want to create in the positive prompt field. Include any text you want rendered in the image, specifying the language if needed.
- Set Negative Prompts: Add terms like "blurry text, misspelled, distorted letters" to guide the model away from common text-rendering artifacts.
- Choose Your Resolution: Select an output resolution that matches your use case. Higher resolutions produce sharper text but require more VRAM.
- Adjust the Sampler Settings: The default 8-step LoRA configuration balances speed and quality. Increase steps for finer detail on complex compositions.
- Generate Your Image: Click Queue Prompt and let the model render your image. Expect crisp, detailed output with legible embedded text.
- Iterate and Refine: Adjust your prompt wording or sampler settings and regenerate to dial in the exact look you want.
faq
Q: What languages can Qwen-Image render as text within generated images? A: Qwen-Image excels at rendering text in English, Chinese, Korean, and Japanese. The 20B parameter architecture was specifically trained on multilingual text data, producing clean, legible characters across these languages even at smaller sizes within complex scenes.
Q: How does Qwen-Image compare to other text-to-image models for typography? A: Qwen-Image is one of the strongest open models for in-image text rendering. While models like SDXL and Flux can produce text, Qwen-Image generates noticeably more consistent and accurate letterforms — particularly for non-Latin scripts where other models tend to produce garbled or distorted characters.
Q: What hardware do I need to run Qwen-Image at full quality? A: The FP8-quantized workflow uses approximately 86% VRAM on an RTX 4090 (about 20GB). An RTX 4090 or equivalent is recommended for the full model. For lower-VRAM setups, the distilled variant offers a lighter alternative with a modest trade-off in detail.
Q: Can I use Qwen-Image for commercial projects? A: Yes. Qwen-Image is released under the Apache 2.0 license, which permits commercial use, modification, and redistribution. This makes it suitable for professional design work, client projects, and product pipelines without licensing concerns.