A complete program for accelerating inference performance
The following optimization strategies can be used to address the generation speed bottleneck:
- Enable Flash Attention: Execute at the time of installation
pip install flash-attn --no-build-isolationThis technology increases 30% inference speed (requires RTX 30/40 series or newer graphics cards) - Optimized Configuration of Graphics Memory: Settings
--enable_xformers Trueparameter, in conjunction with thetorch.backends.cuda.enable_flash_sdp(True)Enabling in-memory efficient computation - Hardware-level acceleration: on FP8 Tensor Core-enabled GPUs such as the NVIDIA H100, using the
--precision fp8Parameters get a 2x speed boost
Test data shows that the 512 x 512 image generation time can be reduced from 5 seconds to 2.8 seconds on the H800 graphics card (after using all optimization measures)
This answer comes from the articleStep1X-Edit: An Open Source Tool for Editing Images with Natural Language InstructionsThe
May not be reproduced without permission:AI productivity tools " How to optimize Step1X-Edit's inference speed?
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