This is a simplified guide to an AI model called Qwen-Image-Edit-Plus maintained by Qwen. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Model overview
The qwen-image-edit-plus represents the latest iteration in Qwen's image editing foundation model series, building upon the core qwen-image-edit with enhanced multi-image editing capabilities and improved single-image consistency. This model introduces native ControlNet support, extending beyond its predecessor's semantic and appearance editing functions. The architecture combines Qwen2.5-VL for semantic understanding with a VAE encoder for visual fidelity, enabling dual-encoding mechanisms that balance content preservation with visual quality. Unlike the standard qwen-image generation model, this editing variant focuses on precise modifications to existing images while maintaining structural coherence.
Model inputs and outputs
The model accepts multiple input images along with text prompts describing desired edits, supporting various aspect ratios from square to widescreen formats. Users can control generation speed, output quality, and safety filtering through configurable parameters.
Inputs
- image: Array of reference images in JPEG, PNG, GIF, or WebP formats
- prompt: Text instruction describing the desired image edits
- aspect_ratio: Output dimensions including 1:1, 16:9, 9:16, 4:3, 3:4, or matching input
- go_fast: Boolean toggle for optimized inference speed
- seed: Optional integer for reproducible results
- output_format: Choice between WebP, JPG, or PNG formats
- output_quality: Integer from 0-100 controlling compression quality
- disable_safety_checker: Boolean to bypass content filtering
Outputs
- images: Array of edited images in the specified format and quality
Capabilities
This model excels at both semantic edi...
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