Stable Diffusion XL produces stunning images but can be painfully slow for production use. Here's how we achieved a 6.7x speedup while maintaining image quality through systematic optimization.
Baseline Performance
Our starting point was a standard SDXL 1.0 setup running on an RTX 4090:
- Generation time: 8.2 seconds (50 steps)
- Memory usage: 11.2GB VRAM
- Batch size: 1 (memory limited)
Optimization Techniques
1. Model Quantization
We applied INT8 quantization to the UNet model, reducing memory usage by 40% with minimal quality loss. The key was using calibration data that matched our target use cases.
2. TensorRT Acceleration
Converting the UNet to TensorRT provided the biggest performance gain. We optimized for specific input dimensions (1024x1024) and batch sizes to maximize throughput.
TensorRT Results
- UNet inference: 4.2x faster
- Memory usage: Reduced by 35%
- Warmup time: 45 seconds (one-time cost)
3. Memory Management
Aggressive memory optimization allowed us to increase batch size and reduce memory fragmentation:
- Model offloading between pipeline stages
- Gradient checkpointing for VAE
- Custom CUDA memory pool management
Final Results
After all optimizations:
- Generation time: 1.2 seconds (25 steps)
- Memory usage: 7.8GB VRAM
- Batch size: 4 images simultaneously
- Quality: 98% similarity to original (LPIPS metric)
These optimizations enabled real-time image generation for our production applications, opening up new possibilities for interactive AI experiences while maintaining the high quality that makes SDXL special.