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Description
MAI-Image-2.5-Pro represents Microsoft AI’s most advanced image generation model, tailored specifically for projects where visual excellence, precision, and control are essential. This innovative model produces stunning, photorealistic images that are ready for design applications, transforming basic text descriptions or uploaded images into high-quality visuals featuring realistic lighting, true-to-life skin tones, and intricate material textures ideal for professional use. It excels in creating standout imagery for branding, product representation, commercial design, and other tasks that necessitate a refined finish with minimal need for post-editing. Users benefit from its sophisticated editing tools, enabling them to implement changes through natural language while maintaining the image's overall coherence, layout, and composition, as well as allowing for seamless adjustments of objects or settings in context. Additionally, MAI-Image-2.5-Pro boasts exceptional object consistency, enhanced visual reasoning, and a greater understanding of the world, ensuring that both edits and new creations remain logically consistent, even within intricate scenes. This model not only enhances creative workflows but also empowers professionals to achieve their vision with greater ease and accuracy.
Description
Qwen-Image-2.1 is an advanced model for text-to-image creation and image modification, part of the Qwen series, engineered to effectively balance the quality of generated images, the efficiency of inference, and overall adaptability. With a visual generation architecture comprising 7 billion parameters and utilizing 32 Single-Stream DiT layers, it features a streamlined design that integrates mixed-granularity attention alongside prefix KV cache reuse, enabling high-quality image outputs while minimizing computational demands. This model offers native capabilities for creating both standard and transparent RGBA images, facilitating transparent-layer editing and allowing for subject extraction from images, all integrated within a single framework. For editing purposes, it accommodates up to ten reference images for complex multi-subject arrangements, takes local editing commands via circles, painted notes, or distinct masks, and maintains the integrity of individuals and products throughout the process. Enhancements in typography, portrait illumination, realistic textures, and intricate details have been implemented to yield results that are not only more polished but also visually striking. Additionally, this model’s versatility in handling various image generation tasks sets it apart in the realm of image synthesis technology.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Bing
Yes
GitHub Copilot
Yes
Happy Shrimp 1.0
No
Microsoft Azure
Yes
Microsoft Dynamics 365
Yes
Microsoft Excel
Yes
Microsoft Foundry
Yes
Microsoft OneDrive
Yes
Microsoft PowerPoint
Yes
Qwen
No
Integrations
Bing
No
GitHub Copilot
No
Happy Shrimp 1.0
Yes
Microsoft Azure
No
Microsoft Dynamics 365
No
Microsoft Excel
No
Microsoft Foundry
No
Microsoft OneDrive
No
Microsoft PowerPoint
No
Qwen
Yes
Pricing Details
$5 per 1M text input tokens
$5 per 1M text input tokens, $8 per 1M image input tokens, and $47 per 1M image output tokens
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
microsoft.ai
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
github.com/QwenLM/Qwen-Image-2.1