Qwen-Image fine-tunes

Which models are the fastest?

Most Qwen-Image fine-tunes run at similar speeds since they’re based on the same qwen/qwen-image backbone.
If you’re experimenting or testing concepts, smaller fine-tunes like fofr/qwen-2004 or ccchot-osk103/pai_qwen_21102568 load quickly and return results fast, making them great for iteration and creative exploration.

Which models offer the best balance of cost and quality?

Models like fofr/qwen-fantasy-art and fofr/qwen-dark-art offer strong stylistic fidelity without requiring heavy compute.
For portrait or realistic outputs, wuzoobia/bruna-portrait and fofr/qwen-william-blake are fine-tuned to deliver detailed, high-quality images in distinct styles.

What works best for artistic or stylized generations?

If you want artistic results, there’s a range of thematic fine-tunes:

• fofr/qwen-fantasy-art — for vivid, cinematic fantasy scenes.
• fofr/qwen-william-blake — for intricate, painterly detail inspired by Blake’s art.
• fofr/qwen-my-subconscious — for surreal, abstract, or dreamlike imagery.
• fofr/qwen-midjourney-v3 — for aesthetic, high-contrast renders reminiscent of Midjourney’s style.

What works best for realistic or photo-based generations?

For lifelike or photo-inspired results:

• wuzoobia/bruna-portrait creates corporate-style headshots and photorealistic portraits.
• ccchot-osk103/moneycat and ccchot-osk103/buacat are tuned for animal photos and produce sharp, charming pet portraits.
• fofr/qwen-2004 intentionally reproduces the look of older digital photos from the early 2000s.

What’s the difference between Qwen fine-tunes and the base Qwen-image model?

Fine-tunes are specialized versions of the qwen/qwen-image model trained on smaller, curated datasets to achieve specific aesthetics or subjects.
The base model is general-purpose and broad, while fine-tunes let you emphasize a particular look—like classical art, portraits, or fantasy scenes—without retraining from scratch.

What kinds of outputs can I expect from these models?

All models output generated images that reflect both your text prompt and the fine-tuned style.
Outputs typically mirror the domain of the training data—e.g., fantasy scenes, portraits, animals, or artistic compositions—while maintaining Qwen’s strong prompt-following abilities.

How can I create my own fine-tune?

You can train your own model using qwen/qwen-image-lora-trainer.
Upload your image dataset, configure training parameters, and generate a LoRA adapter that customizes the base Qwen model to your visual style. This approach is much lighter than full model retraining.

Can I use these models for commercial work?

Many Qwen-image fine-tunes can be used commercially, but always review the License section on each model page.
Some creators release their fine-tunes for research or personal use only, so confirm before deploying or redistributing outputs commercially.

How do I use or run these models?

Select a model, enter a prompt that matches its theme, and click Run.
For example, try “a glowing knight in misty mountains” on fofr/qwen-fantasy-art or “a professional headshot in natural light” on wuzoobia/bruna-portrait.
Each model’s README often includes example prompts for inspiration.

What should I know before running a job in this collection?

• Fine-tunes are optimized for style, not broad realism—choose one that fits your project’s aesthetic.
• You can adjust the creativity or guidance parameters to control how closely the output follows your prompt versus the fine-tuned look.
• For consistent results (like character portraits), use the same seed or LoRA settings across runs.

Any other collection-specific tips or considerations?

• Fine-tunes can be combined: try layering LoRA adapters or chaining generations between multiple Qwen variants.
• If you’re building a brand or visual identity, training your own LoRA via qwen/qwen-image-lora-trainer ensures full creative control.
• Use descriptive, detailed prompts—these fine-tunes respond well to specific composition or lighting cues.
• Experiment with contrasting themes (e.g., using a fantasy model for portraits) to discover unique visual hybrids.