Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
MOSS-Image represents a versatile family of open-weight models designed for transforming natural-language prompts and structured layout specifications into images. Both its Base and fine-tuned SFT checkpoints utilize a unified inference interface and accommodate prompts with a maximum length of 4,096 tokens. Users have the flexibility to either articulate a scene in written form or supply JSON layout data that dictates the placement of objects and text within the image. The project offers comprehensive resources, including Python and Gradio workflows, a Diffusers pipeline, model weights, and detailed guidance for local deployment and fine-tuning processes. This model is capable of producing a variety of visual content, such as illustrations, photorealistic images, posters, and other designed compositions that can integrate specific text elements. Additionally, developers have the option to leverage supported image-generation runtimes or a hosted API to enhance their projects. Aimed at developers, researchers, and creative individuals, the software facilitates the generation and modification of images with precise layout control, making it a valuable tool in various creative endeavors. As users explore MOSS-Image, they can unlock new possibilities in visual storytelling and design innovation.
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
ComfyUI
Yes
Happy Shrimp 1.0
No
Qwen
No
Qwen Studio
No
QwenCloud
No
Integrations
ComfyUI
No
Happy Shrimp 1.0
Yes
Qwen
Yes
Qwen Studio
Yes
QwenCloud
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
Yes
Linux
Yes
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
No
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
OpenMOSS
Country
China
Website
openmoss.ai/MOSS-Image/
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
github.com/QwenLM/Qwen-Image-2.1