8e963d1c2a
* 7529 do not disable autocast for cuda devices * Remove typecasting error check for non-mps platforms, as a correct autocast implementation makes it a non-issue * add autocast fix to other training examples * disable native_amp for dreambooth (sdxl) * disable native_amp for pix2pix (sdxl) * remove tests from remaining files * disable native_amp on huggingface accelerator for every training example that uses it * convert more usages of autocast to nullcontext, make style fixes * make style fixes * style. * Empty-Commit --------- Co-authored-by: bghira <bghira@users.github.com> Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
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.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.