AI BriefingHow to Optimize Flux LoRA Inference Speed by 2.3x
Key point
A method using Diffusers and PEFT was released that improves LoRA inference speed for Flux models by about 2.3x.
Details
To solve the inference latency problem that occurs when using LoRA adapters with the Flux.1-Dev model, an optimization recipe is proposed that provides about a 2.3x speed improvement.
The main optimization components are as follows:
- Applying Flash Attention 3 (FA3)
- Compilation optimization via torch.compile
- FP8 quantization using TorchAO (increasing memory and speed efficiency)
- A design that prevents recompilation issues that occur during LoRA hotswapping
This method can work not only on NVIDIA GPUs but also on AMD GPUs, and has a structure that can be generally applied to models other than Flux as well.
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