diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md

Training examples

Creating a training image set is described in a different document.

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:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .

Then cd in the example folder and run

pip install -r requirements.txt

And initialize an 🤗Accelerate environment with:

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:

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.