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@ -367,7 +367,11 @@ You can enable experimental memory efficient attention on recent pytorch in Comf
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```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
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You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
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You can also try:
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* Tunable ops: Setting `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial runs. After running online tuning for a while consider disabling it with `PYTORCH_TUNABLEOP_TUNING=0` to only used the tuned settings and avoid slowdowns.
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* MIOpen: Currently disabled by default. Enable with `COMFYUI_ENABLE_MIOPEN=1`. Be aware that miopen will autotune by default, consider disabling it with `MIOPEN_FIND_MODE=FAST` to avoid tuning slowdowns.
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* Flash attention: Install from [flash-attention](https://github.com/Dao-AILab/flash-attention) & enable with `FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE` and arg `--use-flash-attention`. See also notes in the repo on triton autotuning.
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* If you are encountering VRAM OOMs `PYTORCH_NO_HIP_MEMORY_CACHING=1` may help.
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# Notes
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