Multimodal image generation model QWEN2VL-Flux, using Qwen2VL visual language ability to enhance Flux, can be integrated with ControlNet

#News ·2025-01-09

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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

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Related link

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

Model architecture

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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:

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Image mixing

Seamlessly merge multiple images with smart style conversion:

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Text-guided image mixing

Control image generation with text prompts:

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Style transfer based on grid

Apply fine-grained style control to grid attention:

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