Buckets:
xFormers
We recommend xFormers for both inference and training. In our tests, the optimizations performed in the attention blocks allow for both faster speed and reduced memory consumption.
Install xFormers from pip:
pip install xformers
The xFormers
pippackage requires the latest version of PyTorch. If you need to use a previous version of PyTorch, then we recommend installing xFormers from the source.
After xFormers is installed, you can use it with set_attention_backend() as shown in the Attention backends guide.
According to this issue, xFormers
v0.0.16cannot be used for training (fine-tune or DreamBooth) in some GPUs. If you observe this problem, please install a development version as indicated in the issue comments.
Xet Storage Details
- Size:
- 1.1 kB
- Xet hash:
- 21ceb0545d831e07991fb039628b1eebebc60481ef8a576ab9487a7bc968969f
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.