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Qwen2vl-flux is an advanced multimodal image generation model that enhances FLUX with Qwen2VL's visual language understanding. The model excels at generating high-quality images based on text prompts and visual references, providing superior multimodal understanding and control. Make FLUX's multimodal image understanding and prompt word understanding very strong.

Qwen2vl-Flux has the following characteristics:

• Generate images directly based on images without text images;
• Similar to IPA, pictures and text are combined to generate pictures of corresponding styles;
• GridDot control panel, detailed style extraction;
• ControlNet integration with Depth and canny support

Related link

• Code: https://github.com/erwold/qwen2vl-flux
• Model: https://huggingface.co/Djrango/Qwen2vl-Flux

Model architecture

The model integrates Qwen2VL's visual language capabilities into the FLUX framework for more accurate, context-aware image generation. Key components include:

• Visual Language Understanding Module (Qwen2VL)
• Enhanced FLUX backbone
• Multimodal generation pipeline
• Structural control integration

trait

• Enhanced visual language understanding: Achieve superior multimodal understanding with Qwen2VL
• Multiple generation modes: Support for variation, img2img, repair, and controlnet guided generation
• Structure control: Integrated depth estimation and line detection for accurate structure guidance
• Flexible attention mechanism: Supports focus generation through spatial attention control
• High resolution output: various aspect ratios up to 1536x1024 are supported

Build example

Image change

Create variety while maintaining the essence of the original image:

Image mixing

Seamlessly merge multiple images with smart style conversion:

Text-guided image mixing

Control image generation with text prompts:

Style transfer based on grid

Apply fine-grained style control to grid attention: