safetensors is a different format
from the classic
.bin which uses Pytorch which uses pickle. It contains the
exact same data, which is just the model weights (or tensors).
Pickle is notoriously unsafe which allow any malicious file to execute arbitrary code. The hub itself tries to prevent issues from it, but it’s not a silver bullet.
safetensors first and foremost goal is to make loading machine learning models safe
in the sense that no takeover of your computer can be done.
Hence the name.
Safety can be one reason, if you’re attempting to use a not well known model and you’re not sure about the source of the file.
And a secondary reason, is the speed of loading. Safetensors can load models much faster than regular pickle files. If you spend a lot of times switching models, this can be a huge timesave.
Numbers taken AMD EPYC 7742 64-Core Processor
from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1") # Loaded in safetensors 0:00:02.033658 # Loaded in Pytorch 0:00:02.663379
This is for the entire loading time, the actual weights loading time to load 500MB:
Safetensors: 3.4873ms PyTorch: 172.7537ms
Performance in general is a tricky business, and there are a few things to understand:
- If you’re using the model for the first time from the hub, you will have to download the weights. That’s extremely likely to be much slower than any loading method, therefore you will not see any difference
- If you’re loading the model for the first time (let’s say after a reboot) then your machine will have to actually read the disk. It’s likely to be as slow in both cases. Again the speed difference may not be as visible (this depends on hardware and the actual model).
- The best performance benefit is when the model was already loaded previously on your computer and you’re switching from one model to another. Your OS, is trying really hard not to read from disk, since this is slow, so it will keep the files around in RAM, making it loading again much faster. Since safetensors is doing zero-copy of the tensors, reloading will be faster than pytorch since it has at least once extra copy to do.
If you have
safetensors installed, and all the weights are available in
safetensors format, \
then by default it will use that instead of the pytorch weights.
If you are really paranoid about this, the ultimate weapon would be disabling
import torch def _raise(): raise RuntimeError("I don't want to use pickle") torch.load = lambda *args, **kwargs: _raise()
Just go to this space.
This will create a new PR with the weights, let’s say
This space will download the pickled version, convert it, and upload it on the hub as a PR. If anything bad is contained in the file, it’s Huggingface hub that will get issues, not your own computer. And we’re equipped with dealing with it.
Then in order to use the model, even before the branch gets accepted by the original author you can do:
from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", revision="refs/pr/22")
or you can test it directly online with this space.
And that’s it !
Anything unclear, concerns, or found a bugs ? Open an issue