Qwen/Qwen-Image-2.0-Pro-Edit
Qwen Image 2.0 Pro edit model. Multi-image conditioning (1β5 URLs).
api_example.sh
Pricing
Pay-per-use, no commitments
Technical Specifications
Model Architecture & Performance
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| n | number | 1 | Number of output images to generate (1β6 for qwen-image-2.0 series). |
| size | string | 1024*1024 | Output image resolution in WIDTH*HEIGHT format. Default output is ~1024*1024 with aspect ratio similar to the last input image (when omitted). |
| negative_prompt | string | Describe what you do not want in the edited image. | |
| prompt_extend | boolean | true | Enable prompt rewriting to improve results when prompts lack detail. |
| watermark | boolean | false | Adds a βQwen-Imageβ watermark to the bottom-right corner. |
| seed | number | 0 | Random seed for reproducibility. If omitted, a random seed is used. |
| response_format | string | url | Response format: 'url' for image URL, 'b64_json' for base64 |
Explore the full request and response schema in our external API documentation
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| 1β5 reference images Instruction-following | Requires input images |
Use cases
Recommended applications for this model
Build with Qwen/Qwen-Image-2.0-Pro-Edit faster
Get deployment recipes, benchmark alerts, and GPU pricing updates for Qwen/Qwen-Image-2.0-Pro-Edit (Qwen Image 2.0 Pro Edit) and other image models straight from the Qubrid team.
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
Don't let your AI control you. Control your AI the Qubrid way!
Have questions? Want to Partner with us? Looking for larger deployments or custom fine-tuning? Let's collaborate on the right setup for your workloads.
"Qubrid AI reduced our document processing time by over 60% and significantly improved retrieval accuracy across our RAG workflows."
Enterprise AI Team
Document Intelligence Platform
