목록으로 돌아가기
Qwen-Image-i2L (Image to LoRA)
Revolutionary Image to LoRA model that takes images as input and outputs trained LoRA models, enabling instant style transfer and content preservation
Image to LoRA
Style Transfer
LoRA Generation
DiffSynth Studio
Multi-Model System
개요
Qwen-Image-i2L is an innovative model that takes images as input and directly outputs LoRA weights trained on those images. The system includes four specialized models: i2L-Style for style transfer, i2L-Coarse and i2L-Fine for content preservation, and i2L-Bias for output alignment with Qwen-Image aesthetics.
기능
- Instant LoRA generation from input images without traditional training
- Qwen-Image-i2L-Style: 2.4B parameters for effective style extraction and transfer
- Qwen-Image-i2L-Coarse: 7.9B parameters for content preservation with SigLIP2, DINOv3, Qwen-VL encoders
- Qwen-Image-i2L-Fine: 7.6B parameters with 1024x1024 resolution for detail capture
- Qwen-Image-i2L-Bias: 30M static LoRA for Qwen-Image style alignment
- Combined Coarse+Fine+Bias mode for high-fidelity content and detail preservation
- Supports style transfer with minimal input images
- Can serve as initialization weights for accelerated LoRA training
이미지
Input image for abstract vector style transfer
Generated image using i2L-Style model with abstract vector style
Input image for black and white sketch style transfer
Generated image using i2L-Style model with sketch style
설치
Download models from HuggingFace and use with DiffSynth-Studio framework or compatible diffusion pipelines
사용법
For style transfer, use i2L-Style with a few unified style images. For content preservation, combine i2L-Coarse, i2L-Fine, and i2L-Bias models. All showcase examples use random seed 0.
요구사항
- Python 3.8+
- PyTorch with CUDA support
- DiffSynth-Studio or compatible framework
- 8GB+ VRAM for Style model, 16GB+ for Coarse+Fine+Bias combination