## 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 scipts, make sure to install the library's training dependencies: ```bash pip install diffusers[training] accelerate datasets ``` ### Unconditional Flowers The command to train a DDPM UNet model on the Oxford Flowers dataset: ```bash accelerate launch train_unconditional.py \ --dataset="huggan/flowers-102-categories" \ --resolution=64 \ --output_dir="ddpm-ema-flowers-64" \ --train_batch_size=16 \ --num_epochs=100 \ --gradient_accumulation_steps=1 \ --learning_rate=1e-4 \ --lr_warmup_steps=500 \ --mixed_precision=no \ --push_to_hub ``` An example trained model: https://huggingface.co/anton-l/ddpm-ema-flowers-64 A full training run takes 2 hours on 4xV100 GPUs.

### Unconditional Pokemon The command to train a DDPM UNet model on the Pokemon dataset: ```bash accelerate launch train_unconditional.py \ --dataset="huggan/pokemon" \ --resolution=64 \ --output_dir="ddpm-ema-pokemon-64" \ --train_batch_size=16 \ --num_epochs=100 \ --gradient_accumulation_steps=1 \ --learning_rate=1e-4 \ --lr_warmup_steps=500 \ --mixed_precision=no \ --push_to_hub ``` An example trained model: https://huggingface.co/anton-l/ddpm-ema-pokemon-64 A full training run takes 2 hours on 4xV100 GPUs.