| Installing xFormers | |
| We recommend the use of 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. | |
| Starting from version 0.0.16 of xFormers, released on January 2023, installation can be easily performed using pre-built pip wheels: | |
| Copied | |
| pip install xformers | |
| The xFormers PIP package requires the latest version of PyTorch (1.13.1 as of xFormers 0.0.16). If you need to use a previous version of PyTorch, then we recommend you install xFormers from source using the project instructions. | |
| After xFormers is installed, you can use enable_xformers_memory_efficient_attention() for faster inference and reduced memory consumption, as discussed here. | |
| According to this issue, xFormers v0.0.16 cannot be used for training (fine-tune or Dreambooth) in some GPUs. If you observe that problem, please install a development version as indicated in that comment. | |