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## Training with Unpaired Data (CycleGAN-turbo)
Here, we show how to train a CycleGAN-turbo model using unpaired data.
We will use the [horse2zebra dataset](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/datasets.md) introduced by [CycleGAN](https://junyanz.github.io/CycleGAN/) as an example dataset.
### Step 1. Get the Dataset
- First download the horse2zebra dataset from [here](https://www.cs.cmu.edu/~img2img-turbo/data/my_horse2zebra.zip) using the command below.
```
bash scripts/download_horse2zebra.sh
```
- Our training scripts expect the dataset to be in the following format:
```
data
├── dataset_name
│ ├── train_A
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ └── ...
│ ├── train_B
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ └── ...
│ └── fixed_prompt_a.txt
| └── fixed_prompt_b.txt
|
| ├── test_A
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ └── ...
│ ├── test_B
│ │ ├── 000000.png
│ │ ├── 000001.png
│ │ └── ...
```
- The `fixed_prompt_a.txt` and `fixed_prompt_b.txt` files contain the **fixed caption** used for the source and target domains respectively.
### Step 2. Train the Model
- Initialize the `accelerate` environment with the following command:
```
accelerate config
```
- Run the following command to train the model.
```
export NCCL_P2P_DISABLE=1
accelerate launch --main_process_port 29501 src/train_cyclegan_turbo.py \
--pretrained_model_name_or_path="stabilityai/sd-turbo" \
--output_dir="output/cyclegan_turbo/my_horse2zebra" \
--dataset_folder "data/my_horse2zebra" \
--train_img_prep "resize_286_randomcrop_256x256_hflip" --val_img_prep "no_resize" \
--learning_rate="1e-5" --max_train_steps=25000 \
--train_batch_size=1 --gradient_accumulation_steps=1 \
--report_to "wandb" --tracker_project_name "gparmar_unpaired_h2z_cycle_debug_v2" \
--enable_xformers_memory_efficient_attention --validation_steps 250 \
--lambda_gan 0.5 --lambda_idt 1 --lambda_cycle 1
```
- Additional optional flags:
- `--enable_xformers_memory_efficient_attention`: Enable memory-efficient attention in the model.
### Step 3. Monitor the training progress
- You can monitor the training progress using the [Weights & Biases](https://wandb.ai/site) dashboard.
- The training script will visualizing the training batch, the training losses, and validation set L2, LPIPS, and FID scores (if specified).
<div>
<p align="center">
<img src='../assets/examples/training_evaluation.png' align="center" width=800px>
</p>
</div>
- The model checkpoints will be saved in the `<output_dir>/checkpoints` directory.
### Step 4. Running Inference with the trained models
- You can run inference using the trained model using the following command:
```
python src/inference_unpaired.py --model_path "output/cyclegan_turbo/my_horse2zebra/checkpoints/model_1001.pkl" \
--input_image "data/my_horse2zebra/test_A/n02381460_20.jpg" \
--prompt "picture of a zebra" --direction "a2b" \
--output_dir "outputs" --image_prep "no_resize"
```
- The above command should generate the following output:
<table>
<tr>
<th>Model Input</th>
<th>Model Output</th>
</tr>
<tr>
<td><img src='../assets/examples/my_horse2zebra_input.jpg' width="200px"></td>
<td><img src='../assets/examples/my_horse2zebra_output.jpg' width="200px"></td>
</tr>
</table>
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