A curated collection of Low-Rank Adaptation (LoRA) models for Stable Diffusion XL in FP16 precision format. LoRAs enable efficient fine-tuning and style transfer with minimal storage requirements compared to full model fine-tunes.
Model Description
This repository contains LoRA adapters for Stable Diffusion XL (SDXL) that can be applied on top of the base SDXL model to achieve specific artistic styles, concepts, or improvements. LoRAs use low-rank matrix decomposition to efficiently capture style and concept information in small files (typically 10-200 MB vs 6+ GB for full models).
Key Features
FP16 Precision: Half-precision floating point for balance between quality and efficiency
Small File Sizes: LoRAs are 30-100x smaller than full model checkpoints
Modular Design: Mix and match multiple LoRAs for combined effects
Base Model Compatible: Works with SDXL base 1.0 and derived models
SafeTensors Format: Secure, fast loading with metadata support
Repository Contents
sdxl-fp16-loras/
├── README.md # This file
└── loras/
└── sdxl/ # SDXL LoRA files (to be added)
Current Status
Repository Status: Empty / Ready for population
This repository is structured and ready to receive SDXL LoRA files. LoRA models should be placed in the loras/sdxl/ directory.
Training Method: Low-Rank Adaptation (LoRA) fine-tuning
Compatibility: Cross-compatible with other SDXL tools and frameworks
Performance Tips and Optimization
Memory Optimization
Use FP16 precision to reduce VRAM usage
Enable torch.compile() for faster inference (PyTorch 2.0+)
Use enable_model_cpu_offload() for low VRAM systems
Lower LoRA strength if generation quality is affected
Quality Optimization
LoRA Strength: Start at 0.8 and adjust based on results
Too high (>1.2): May cause artifacts or overfitting
Too low (<0.4): Minimal LoRA effect
Multiple LoRAs: Keep total strength below 3.0 to avoid conflicts
Inference Steps: 25-35 steps recommended for quality
Guidance Scale: 7-9 for balanced creativity and adherence
Best Practices
Test LoRAs individually before combining
Use descriptive filenames for easy identification
Keep LoRAs organized by style/purpose
Document LoRA trigger words and recommended settings
Back up working LoRA combinations
Adding LoRAs to This Repository
When adding new LoRA files:
Place files in loras/sdxl/ directory
Use descriptive names: style_name_v1.safetensors
Document metadata: Include trigger words, training info
Update README: Add file listing with sizes and descriptions
Verify format: Ensure SafeTensors format for safety
License Information
Repository License: OpenRAIL++
This repository follows the OpenRAIL++ license, which is the standard license for Stable Diffusion XL models. Individual LoRA files may have additional licensing terms specified by their creators.
Usage Terms
Commercial Use: Allowed under OpenRAIL++ terms
Redistribution: Allowed with attribution
Modifications: Allowed with attribution
Restrictions: See OpenRAIL++ for prohibited use cases
Important: Always verify the license of individual LoRA models before use, especially for commercial applications. Some LoRAs may have additional restrictions or requirements.
Citation
If using SDXL and LoRA models in research or publications:
bibtex
1@misc{sdxl2023,
2 title={SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis},
3 author={Podell, Dustin and English, Zion and Lacey, Kyle and Blattmann, Andreas and Dockhorn, Tim and Müller, Jonas and Penna, Joe and Rombach, Robin},
4 year={2023}
5}
67@article{hu2021lora,
8 title={LoRA: Low-Rank Adaptation of Large Language Models},
9 author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
10 journal={arXiv preprint arXiv:2106.09685},
11 year={2021}
12}
