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* create a script to train vae * update main.py * update train_autoencoderkl.py * update train_autoencoderkl.py * add a check of --pretrained_model_name_or_path and --model_config_name_or_path * remove the comment, remove diffusers in requiremnets.txt, add validation_image ote * update autoencoderkl.py * quality --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
# AutoencoderKL training example
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## Installing the dependencies
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Before running the scripts, make sure to install the library's training dependencies:
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**Important**
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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:
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```bash
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git clone https://github.com/huggingface/diffusers
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cd diffusers
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pip install .
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```
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Then cd in the example folder and run
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```bash
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pip install -r requirements.txt
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```
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And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
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```bash
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accelerate config
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```
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## Training on CIFAR10
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Please replace the validation image with your own image.
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```bash
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accelerate launch train_autoencoderkl.py \
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--pretrained_model_name_or_path stabilityai/sd-vae-ft-mse \
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--dataset_name=cifar10 \
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--image_column=img \
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--validation_image images/bird.jpg images/car.jpg images/dog.jpg images/frog.jpg \
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--num_train_epochs 100 \
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--gradient_accumulation_steps 2 \
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--learning_rate 4.5e-6 \
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--lr_scheduler cosine \
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--report_to wandb \
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```
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## Training on ImageNet
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```bash
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accelerate launch train_autoencoderkl.py \
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--pretrained_model_name_or_path stabilityai/sd-vae-ft-mse \
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--num_train_epochs 100 \
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--gradient_accumulation_steps 2 \
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--learning_rate 4.5e-6 \
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--lr_scheduler cosine \
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--report_to wandb \
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--mixed_precision bf16 \
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--train_data_dir /path/to/ImageNet/train \
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--validation_image ./image.png \
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--decoder_only
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```
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