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[docs] Improve LoRA docs (#3311) 

* update docs

* add to toctree

* apply feedback
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docs/source/en/_toctree.yml
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......@@ -60,6 +60,8 @@
- sections:
- local: training/overview
title: Overview
- local: training/create_dataset
title: Create a dataset for training
- local: training/unconditional_training
title: Unconditional image generation
- local: training/text_inversion
......
docs/source/en/training/controlnet.mdx
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......@@ -69,6 +69,8 @@ The original dataset is hosted in the ControlNet [repo](https://huggingface.co/l
Our training examples use [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) because that is what the original set of ControlNet models was trained on. However, ControlNet can be trained to augment any compatible Stable Diffusion model (such as [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [`stabilityai/stable-diffusion-2-1`](https://huggingface.co/stabilityai/stable-diffusion-2-1).
To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
## Training
Download the following images to condition our training with:
......@@ -79,7 +81,9 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
```
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
The training script creates and saves a `diffusion_pytorch_model.bin` file in your repository.
```bash
export MODEL_DIR="runwayml/stable-diffusion-v1-5"
......
docs/source/en/training/create_dataset.mdx 0 → 100644
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# Create a dataset for training
There are many datasets on the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads) to train a model on, but if you can't find one you're interested in or want to use your own, you can create a dataset with the 🤗 [Datasets](hf.co/docs/datasets) library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation.
This guide will show you two ways to create a dataset to finetune on:
- provide a folder of images to the `--train_data_dir` argument
- upload a dataset to the Hub and pass the dataset repository id to the `--dataset_name` argument
<Tip>
💡 Learn more about how to create an image dataset for training in the [Create an image dataset](https://huggingface.co/docs/datasets/image_dataset) guide.
</Tip>
## Provide a dataset as a folder
For unconditional generation, you can provide your own dataset as a folder of images. The training script uses the [`ImageFolder`](https://huggingface.co/docs/datasets/en/image_dataset#imagefolder) builder from 🤗 Datasets to automatically build a dataset from the folder. Your directory structure should look like:
```bash
data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png
```
Pass the path to the dataset directory to the `--train_data_dir` argument, and then you can start training:
```bash
accelerate launch train_unconditional.py \
--train_data_dir <path-to-train-directory> \
<other-arguments>
```
## Upload your data to the Hub
<Tip>
💡 For more details and context about creating and uploading a dataset to the Hub, take a look at the [Image search with 🤗 Datasets](https://huggingface.co/blog/image-search-datasets) post.
</Tip>
Start by creating a dataset with the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) feature, which creates an `image` column containing the PIL-encoded images.
You can use the `data_dir` or `data_files` parameters to specify the location of the dataset. The `data_files` parameter supports mapping specific files to dataset splits like `train` or `test`:
```python
from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# 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)
dataset = load_dataset(
"imagefolder",
data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip",
)
# example 4: providing several splits
dataset = load_dataset(
"imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]}
)
```
Then use the [`~datasets.Dataset.push_to_hub`] method to upload the dataset to the Hub:
```python
# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")
# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```
Now the dataset is available for training by passing the dataset name to the `--dataset_name` argument:
```bash
accelerate launch --mixed_precision="fp16" train_text_to_image.py \
--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
--dataset_name="name_of_your_dataset" \
<other-arguments>
```
## Next steps
Now that you've created a dataset, you can plug it into the `train_data_dir` (if your dataset is local) or `dataset_name` (if your dataset is on the Hub) arguments of a training script.
For your next steps, feel free to try and use your dataset to train a model for [unconditional generation](uncondtional_training) or [text-to-image generation](text2image)!
\ No newline at end of file
docs/source/en/training/custom_diffusion.mdx
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......@@ -67,7 +67,7 @@ write_basic_config()
```
### Cat example 😺
Now let's get our dataset. Download dataset from [here](https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip) and unzip it.
Now let's get our dataset. Download dataset from [here](https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip) and unzip it. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
We also collect 200 real images using `clip-retrieval` which are combined with the target images in the training dataset as a regularization. This prevents overfitting to the the given target image. The following flags enable the regularization `with_prior_preservation`, `real_prior` with `prior_loss_weight=1.`.
The `class_prompt` should be the category name same as target image. The collected real images are with text captions similar to the `class_prompt`. The retrieved image are saved in `class_data_dir`. You can disable `real_prior` to use generated images as regularization. To collect the real images use this command first before training.
......@@ -79,6 +79,8 @@ python retrieve.py --class_prompt cat --class_data_dir real_reg/samples_cat --nu
**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
The script creates and saves model checkpoints and a `pytorch_custom_diffusion_weights.bin` file in your repository.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export OUTPUT_DIR="path-to-save-model"
......
docs/source/en/training/dreambooth.mdx
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......@@ -64,6 +64,8 @@ snapshot_download(
)
```
To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
## Finetuning
<Tip warning={true}>
......@@ -76,7 +78,7 @@ DreamBooth finetuning is very sensitive to hyperparameters and easy to overfit.
<pt>
Set the `INSTANCE_DIR` environment variable to the path of the directory containing the dog images.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`] argument. The `instance_prompt` argument is a text prompt that contains a unique identifier, such as `sks`, and the class the image belongs to, which in this example is `a photo of a sks dog`.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
......@@ -111,7 +113,7 @@ Before running the script, make sure you have the requirements installed:
pip install -U -r requirements.txt
```
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`] argument. The `instance_prompt` argument is a text prompt that contains a unique identifier, such as `sks`, and the class the image belongs to, which in this example is `a photo of a sks dog`.
Now you can launch the training script with the following command:
......
docs/source/en/training/instructpix2pix.mdx
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......@@ -77,16 +77,16 @@ write_basic_config()
### Toy example
As mentioned before, we'll use a [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) for training. The dataset
is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper.
is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument. You'll also need to specify the dataset name in `DATASET_ID`:
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument. You'll also need to specify the dataset name in `DATASET_ID`:
```bash
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATASET_ID="fusing/instructpix2pix-1000-samples"
```
Now, we can launch training:
Now, we can launch training. The script saves all the components (`feature_extractor`, `scheduler`, `text_encoder`, `unet`, etc) in a subfolder in your repository.
```bash
accelerate launch --mixed_precision="fp16" train_instruct_pix2pix.py \
......
docs/source/en/training/lora.mdx
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......@@ -17,8 +17,7 @@ specific language governing permissions and limitations under the License.
<Tip warning={true}>
Currently, LoRA is only supported for the attention layers of the [`UNet2DConditionalModel`]. We also
support LoRA fine-tuning of the text encoder for DreamBooth in a limited capacity. For more details on how we support
LoRA fine-tuning of the text encoder, refer to the discussion on [this PR](https://github.com/huggingface/diffusers/pull/2918).
support fine-tuning the text encoder for DreamBooth with LoRA in a limited capacity. Fine-tuning the text encoder for DreamBooth generally yields better results, but it can increase compute usage.
</Tip>
......@@ -52,7 +51,7 @@ Finetuning a model like Stable Diffusion, which has billions of parameters, can
Let's finetune [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset to generate your own Pokémon.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument. You'll also need to set the `DATASET_NAME` environment variable to the name of the dataset you want to train on.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument. You'll also need to set the `DATASET_NAME` environment variable to the name of the dataset you want to train on. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
The `OUTPUT_DIR` and `HUB_MODEL_ID` variables are optional and specify where to save the model to on the Hub:
......@@ -69,7 +68,7 @@ There are some flags to be aware of before you start training:
* `--report_to=wandb` reports and logs the training results to your Weights & Biases dashboard (as an example, take a look at this [report](https://wandb.ai/pcuenq/text2image-fine-tune/runs/b4k1w0tn?workspace=user-pcuenq)).
* `--learning_rate=1e-04`, you can afford to use a higher learning rate than you normally would with LoRA.
Now you're ready to launch the training (you can find the full training script [here](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)):
Now you're ready to launch the training (you can find the full training script [here](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)). Training takes about 5 hours on a 2080 Ti GPU with 11GB of RAM, and it'll create and save model checkpoints and the `pytorch_lora_weights` in your repository.
```bash
accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
......@@ -159,9 +158,9 @@ pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.
### Training[[dreambooth-training]]
Let's finetune [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) with DreamBooth and LoRA with some 🐶 [dog images](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ). Download and save these images to a directory.
Let's finetune [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) with DreamBooth and LoRA with some 🐶 [dog images](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ). Download and save these images to a directory. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
To start, specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument. You'll also need to set `INSTANCE_DIR` to the path of the directory containing the images.
To start, specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument. You'll also need to set `INSTANCE_DIR` to the path of the directory containing the images.
The `OUTPUT_DIR` variables is optional and specifies where to save the model to on the Hub:
......@@ -177,7 +176,11 @@ There are some flags to be aware of before you start training:
* `--report_to=wandb` reports and logs the training results to your Weights & Biases dashboard (as an example, take a look at this [report](https://wandb.ai/pcuenq/text2image-fine-tune/runs/b4k1w0tn?workspace=user-pcuenq)).
* `--learning_rate=1e-04`, you can afford to use a higher learning rate than you normally would with LoRA.
Now you're ready to launch the training (you can find the full training script [here](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py)):
Now you're ready to launch the training (you can find the full training script [here](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py)). The script creates and saves model checkpoints and the `pytorch_lora_weights.bin` file in your repository.
It's also possible to additionally fine-tune the text encoder with LoRA. This, in most cases, leads
to better results with a slight increase in the compute. To allow fine-tuning the text encoder with LoRA,
specify the `--train_text_encoder` while launching the `train_dreambooth_lora.py` script.
```bash
accelerate launch train_dreambooth_lora.py \
......@@ -198,12 +201,7 @@ accelerate launch train_dreambooth_lora.py \
--validation_epochs=50 \
--seed="0" \
--push_to_hub
```
It's also possible to additionally fine-tune the text encoder with LoRA. This, in most cases, leads
to better results with a slight increase in the compute. To allow fine-tuning the text encoder with LoRA,
specify the `--train_text_encoder` while launching the `train_dreambooth_lora.py` script.
```
### Inference[[dreambooth-inference]]
......
docs/source/en/training/text2image.mdx
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......@@ -74,7 +74,7 @@ To load a checkpoint to resume training, pass the argument `--resume_from_checkp
<pt>
Launch the [PyTorch training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) for a fine-tuning run on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset like this.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
<literalinclude>
{"path": "../../../../examples/text_to_image/README.md",
......@@ -143,7 +143,7 @@ Before running the script, make sure you have the requirements installed:
pip install -U -r requirements_flax.txt
```
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
Now you can launch the [Flax training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py) like this:
......
docs/source/en/training/text_inversion.mdx
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......@@ -81,7 +81,7 @@ To resume training from a saved checkpoint, pass the following argument to the t
## Finetuning
For your training dataset, download these [images of a cat toy](https://huggingface.co/datasets/diffusers/cat_toy_example) and store them in a directory:
For your training dataset, download these [images of a cat toy](https://huggingface.co/datasets/diffusers/cat_toy_example) and store them in a directory. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
```py
from huggingface_hub import snapshot_download
......@@ -92,9 +92,9 @@ snapshot_download(
)
```
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument, and the `DATA_DIR` environment variable to the path of the directory containing the images.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument, and the `DATA_DIR` environment variable to the path of the directory containing the images.
Now you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py):
Now you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py). The script creates and saves the following files to your repository: `learned_embeds.bin`, `token_identifier.txt`, and `type_of_concept.txt`.
<Tip>
......@@ -144,7 +144,7 @@ Before you begin, make sure you install the Flax specific dependencies:
pip install -U -r requirements_flax.txt
```
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
Then you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion_flax.py):
......
docs/source/en/training/unconditional_training.mdx
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......@@ -74,7 +74,9 @@ The full training state is saved in a subfolder in the `output_dir` every 500 st
## Finetuning
You're ready to launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) now! Specify the dataset name to finetune on with the `--dataset_name` argument and then save it to the path in `--output_dir`.
You're ready to launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) now! Specify the dataset name to finetune on with the `--dataset_name` argument and then save it to the path in `--output_dir`. To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
The training script creates and saves a `diffusion_pytorch_model.bin` file in your repository.
<Tip>
......@@ -140,82 +142,4 @@ accelerate launch --mixed_precision="fp16" --multi_gpu train_unconditional.py \
--lr_warmup_steps=500 \
--mixed_precision="fp16" \
--logger="wandb"
```
## Finetuning with your own data
There are two ways to finetune a model on your own dataset:
- provide your own folder of images to the `--train_data_dir` argument
- upload your dataset to the Hub and pass the dataset repository id to the `--dataset_name` argument.
<Tip>
💡 Learn more about how to create an image dataset for training in the [Create an image dataset](https://huggingface.co/docs/datasets/image_dataset) guide.
</Tip>
Below, we explain both in more detail.
### Provide the dataset as a folder
If you provide your own dataset as a folder, the script expects the following directory structure:
```bash
data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png
```
Pass the path to the folder containing the images to the `--train_data_dir` argument and launch the training:
```bash
accelerate launch train_unconditional.py \
--train_data_dir <path-to-train-directory> \
<other-arguments>
```
Internally, the script uses the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) to automatically build a dataset from the folder.
### Upload your data to the Hub
<Tip>
💡 For more details and context about creating and uploading a dataset to the Hub, take a look at the [Image search with 🤗 Datasets](https://huggingface.co/blog/image-search-datasets) post.
</Tip>
To upload your dataset to the Hub, you can start by creating one with the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) feature, which creates an `image` column containing the PIL-encoded images, from 🤗 Datasets:
```python
from datasets import load_dataset
# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
# 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)
dataset = load_dataset(
"imagefolder",
data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip",
)
# example 4: providing several splits
dataset = load_dataset(
"imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]}
)
```
Then you can use the [`~datasets.Dataset.push_to_hub`] method to upload it to the Hub:
```python
# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")
# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)
```
Now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the Hub.
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