AI BriefingLCM LoRA Dramatically Speeds Up SDXL Generation
Key point
With LCM LoRA, SDXL models can generate images quickly using just 4-8 steps without any separate retraining.
Details
LCM LoRA is a new methodology that dramatically reduces the image generation steps of Stable Diffusion (SDXL) models from the existing 25-50 steps down to 4-8 steps.
The existing Latent Consistency Models (LCM) approach required distilling the entire model, which was costly and time-consuming, but LCM LoRA solves this problem by training only a small adapter (LoRA layers).
Key Features and Advantages:
- Versatility: Using the trained LoRA, it can be immediately applied to any SDXL fine-tuned model without a separate distillation process.
- Overwhelming Speed: On an RTX 3090, generation time is reduced from 7 seconds to around 1 second, and on Mac it shows approximately 10x faster performance.
- Efficiency: High-quality, fast inference becomes possible with fewer computing resources, increasing accessibility on lower-spec hardware and reducing service costs.
Users can select an appropriate teacher model from the hub to train the LCM LoRA, then combine it with the existing SDXL model and the LCM scheduler for use.
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