Skip to content

[docs] minor updates to bitsandbytes docs. #11509

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
May 6, 2025
Merged

[docs] minor updates to bitsandbytes docs. #11509

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
f9dae2a
minor updates to bitsandbytes docs.
sayakpaul May 6, 2025
53674b4
Apply suggestions from code review
sayakpaul May 6, 2025
2f703cd
Merge branch 'main' into bnb-docs-updates
sayakpaul May 6, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 9 additions & 3 deletions docs/source/en/quantization/bitsandbytes.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ For Ada and higher-series GPUs. we recommend changing `torch_dtype` to `torch.bf
```py
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig

import torch
from diffusers import AutoModel
from transformers import T5EncoderModel

Expand Down Expand Up @@ -88,6 +88,8 @@ Setting `device_map="auto"` automatically fills all available space on the GPU(s
CPU, and finally, the hard drive (the absolute slowest option) if there is still not enough memory.

```py
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=transformer_8bit,
Expand Down Expand Up @@ -132,7 +134,7 @@ For Ada and higher-series GPUs. we recommend changing `torch_dtype` to `torch.bf
```py
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig

import torch
from diffusers import AutoModel
from transformers import T5EncoderModel

Expand Down Expand Up @@ -171,6 +173,8 @@ Let's generate an image using our quantized models.
Setting `device_map="auto"` automatically fills all available space on the GPU(s) first, then the CPU, and finally, the hard drive (the absolute slowest option) if there is still not enough memory.

```py
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=transformer_4bit,
Expand Down Expand Up @@ -214,6 +218,8 @@ Check your memory footprint with the `get_memory_footprint` method:
print(model.get_memory_footprint())
```

Note that this only tells you the memory footprint of the model params and does _not_ estimate the inference memory requirements.

Quantized models can be loaded from the [`~ModelMixin.from_pretrained`] method without needing to specify the `quantization_config` parameters:

```py
Expand Down Expand Up @@ -413,4 +419,4 @@ transformer_4bit.dequantize()
## Resources

* [End-to-end notebook showing Flux.1 Dev inference in a free-tier Colab](https://gist.github.com/sayakpaul/c76bd845b48759e11687ac550b99d8b4)
* [Training](https://gist.github.com/sayakpaul/05afd428bc089b47af7c016e42004527)
* [Training](https://github.com/huggingface/diffusers/blob/8c661ea586bf11cb2440da740dd3c4cf84679b85/examples/dreambooth/README_hidream.md#using-quantization)