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## Training examples
Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
#### Use ONNXRuntime to accelerate training
In order to leverage onnxruntime to accelerate training, please use train_unconditional_ort.py
The command to train a DDPM UNet model on the Oxford Flowers dataset with onnxruntime:
```bash
accelerate launch train_unconditional_ort.py \
--dataset_name="huggan/flowers-102-categories" \
--resolution=64 --center_crop --random_flip \
--output_dir="ddpm-ema-flowers-64" \
--use_ema \
--train_batch_size=16 \
--num_epochs=1 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=fp16
```
Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
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