55 lines
1.4 KiB
Markdown
55 lines
1.4 KiB
Markdown
## Training examples
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### Installing the dependencies
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Before running the scipts, make sure to install the library's training dependencies:
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```bash
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pip install diffusers[training] accelerate datasets
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```
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### Unconditional Flowers
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The command to train a DDPM UNet model on the Oxford Flowers dataset:
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```bash
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accelerate launch train_unconditional.py \
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--dataset="huggan/flowers-102-categories" \
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--resolution=64 \
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--output_dir="ddpm-ema-flowers-64" \
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--train_batch_size=16 \
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--num_epochs=100 \
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--gradient_accumulation_steps=1 \
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--learning_rate=1e-4 \
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--lr_warmup_steps=500 \
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--mixed_precision=no \
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--push_to_hub
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```
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A full training run takes 2 hours on 4xV100 GPUs.
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<img src="https://user-images.githubusercontent.com/26864830/173855866-5628989f-856b-4725-a944-d6c09490b2df.png" width="500" />
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### Unconditional Pokemon
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The command to train a DDPM UNet model on the Pokemon dataset:
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```bash
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accelerate launch train_unconditional.py \
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--dataset="huggan/pokemon" \
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--resolution=64 \
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--output_dir="ddpm-ema-pokemon-64" \
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--train_batch_size=16 \
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--num_epochs=100 \
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--gradient_accumulation_steps=1 \
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--learning_rate=1e-4 \
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--lr_warmup_steps=500 \
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--mixed_precision=no \
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--push_to_hub
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```
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A full training run takes 2 hours on 4xV100 GPUs.
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<img src="https://user-images.githubusercontent.com/26864830/173856733-4f117f8c-97bd-4f51-8002-56b488c96df9.png" width="500" />
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