AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-27 17:22:53 +03:00
Code Activity

Fix typos and add Typo check GitHub Action (#483)

Browse Source 
* Fix typos

* Add a typo check action

* Fix a bug

* Changed to manual typo check currently

Ref: https://github.com/huggingface/diffusers/pull/483#pullrequestreview-1104468010

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Removed a confusing message

* Renamed "nin_shortcut" to "in_shortcut"

* Add memo about NIN

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
This commit is contained in:
Yuta Hayashibe
2022-09-16 22:36:51 +09:00
committed by GitHub
parent c0493723f7
commit 76d492ea49
38 changed files with 92 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
docs/source/api/schedulers.mdx
View File

@@ -44,7 +44,7 @@ To this end, the design of schedulers is such that:
The core API for any new scheduler must follow a limited structure.
- Schedulers should provide one or more `def step(...)` functions that should be called to update the generated sample iteratively.
- Schedulers should provide a `set_timesteps(...)` method that configures the parameters of a schedule function for a specific inference task.
- Schedulers should be framework-agonstic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
- Schedulers should be framework-agnostic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
with a `set_format(...)` method.
The base class [`SchedulerMixin`] implements low level utilities used by multiple schedulers.
@@ -53,7 +53,7 @@ The base class [`SchedulerMixin`] implements low level utilities used by multipl
[[autodoc]] SchedulerMixin
### SchedulerOutput
The class [`SchedulerOutput`] contains the ouputs from any schedulers `step(...)` call.
The class [`SchedulerOutput`] contains the outputs from any schedulers `step(...)` call.
[[autodoc]] schedulers.scheduling_utils.SchedulerOutput
@@ -71,7 +71,7 @@ Original paper can be found [here](https://arxiv.org/abs/2010.02502).
[[autodoc]] DDPMScheduler
#### Varience exploding, stochastic sampling from Karras et. al
#### Variance exploding, stochastic sampling from Karras et. al
Original paper can be found [here](https://arxiv.org/abs/2006.11239).

8
docs/source/quicktour.mdx
View File

@@ -86,11 +86,11 @@ just like we did before only that now you need to pass your `AUTH_TOKEN`:
>>> generator = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=AUTH_TOKEN)
```
If you do not pass your authentification token you will see that the diffusion system will not be correctly
downloaded. Forcing the user to pass an authentification token ensures that it can be verified that the
If you do not pass your authentication token you will see that the diffusion system will not be correctly
downloaded. Forcing the user to pass an authentication token ensures that it can be verified that the
user has indeed read and accepted the license, which also means that an internet connection is required.
**Note**: If you do not want to be forced to pass an authentification token, you can also simply download
**Note**: If you do not want to be forced to pass an authentication token, you can also simply download
the weights locally via:
```
@@ -98,7 +98,7 @@ git lfs install
git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
```
and then load locally saved weights into the pipeline. This way, you do not need to pass an authentification
and then load locally saved weights into the pipeline. This way, you do not need to pass an authentication
token. Assuming that `"./stable-diffusion-v1-4"` is the local path to the cloned stable-diffusion-v1-4 repo,
you can also load the pipeline as follows:

4
docs/source/training/text_inversion.mdx
View File

@@ -49,7 +49,7 @@ The `textual_inversion.py` script [here](https://github.com/huggingface/diffuser
### Installing the dependencies
Before running the scipts, make sure to install the library's training dependencies:
Before running the scripts, make sure to install the library's training dependencies:
```bash
pip install diffusers[training] accelerate transformers
@@ -68,7 +68,7 @@ You need to accept the model license before downloading or using the weights. In
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
Run the following command to autheticate your token
Run the following command to authenticate your token
```bash
huggingface-cli login

4
docs/source/training/unconditional_training.mdx
View File

@@ -18,7 +18,7 @@ distribution.
## Installing the dependencies
Before running the scipts, make sure to install the library's training dependencies:
Before running the scripts, make sure to install the library's training dependencies:
```bash
pip install diffusers[training] accelerate datasets
@@ -117,7 +117,7 @@ from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# example 2: local files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)