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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> | |