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Author SHA1 Message Date
Alex Butler
1dc76f39eb
Merge cb213aee66 into 4a8cf359fe 2026-03-13 06:57:41 +00:00
Alex Butler
cb213aee66 Expand AMD ROCm Tips readme section
Add suggestion to disable online tuning
Add miopen info
Add flash attention info
Add vram oom suggestion
2026-02-07 18:03:16 +00:00

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@ -367,7 +367,11 @@ You can enable experimental memory efficient attention on recent pytorch in Comf
```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
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.
You can also try:
* 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.
* 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.
* 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.
* If you are encountering VRAM OOMs `PYTORCH_NO_HIP_MEMORY_CACHING=1` may help.
# Notes