create a script to train autoencoderkl

examples/research_projects/autoencoderkl/README.md

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+# AutoencoderKL training example
+
+## 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:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install .
+```
+
+Then cd in the example folder  and run
+```bash
+pip install -r requirements.txt
+```
+
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+## Training on CIFAR10
+
+```bash
+accelerate launch train_autoencoderkl.py \
+    --pretrained_model_name_or_path stabilityai/sd-vae-ft-mse \
+    --dataset_name=cifar10 \
+    --image_column=img \
+    --validation_image images/bird.jpg images/car.jpg images/dog.jpg images/frog.jpg \
+    --num_train_epochs 100 \
+    --gradient_accumulation_steps 2 \
+    --learning_rate 4.5e-6 \
+    --lr_scheduler cosine \
+    --report_to wandb \
+```
+
+## Training on ImageNet
+
+```bash
+accelerate launch train_autoencoderkl.py \
+    --pretrained_model_name_or_path stabilityai/sd-vae-ft-mse \
+    --num_train_epochs 100 \
+    --gradient_accumulation_steps 2 \
+    --learning_rate 4.5e-6 \
+    --lr_scheduler cosine \
+    --report_to wandb \
+    --mixed_precision bf16 \
+    --train_data_dir /path/to/ImageNet/train \
+    --validation_image ./image.png \
+    --decoder_only
+```

examples/research_projects/autoencoderkl/requirements.txt

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+accelerate>=0.16.0
+bitsandbytes
+datasets
+diffusers
+huggingface_hub
+lpips
+numpy
+packaging
+Pillow
+taming_transformers
+torch
+torchvision
+tqdm
+transformers
+wandb
+xformers