miccunifi / ladi-vton

[ACM MM 2023] - LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On
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acmmm acmmm2023 dresscode fashionai generative-model latent-diffusion-models stable-diffusion textual-inversion virtual-tryon viton-hd

LaDI-VTON (ACM Multimedia 2023)

Latent Diffusion Textual-Inversion Enhanced Virtual Try-On

Davide Morelli*, Alberto Baldrati*, Giuseppe Cartella, Marcella Cornia, Marco Bertini, Rita Cucchiara

* Equal contribution.

arXiv GitHub Stars

πŸ”₯πŸ”₯ [05/09/2023] Release of the training code

This is the official repository for the paper "LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On".

Overview

Abstract:
The rapidly evolving fields of e-commerce and metaverse continue to seek innovative approaches to enhance the consumer experience. At the same time, recent advancements in the development of diffusion models have enabled generative networks to create remarkably realistic images. In this context, image-based virtual try-on, which consists in generating a novel image of a target model wearing a given in-shop garment, has yet to capitalize on the potential of these powerful generative solutions. This work introduces LaDI-VTON, the first Latent Diffusion textual Inversion-enhanced model for the Virtual Try-ON task. The proposed architecture relies on a latent diffusion model extended with a novel additional autoencoder module that exploits learnable skip connections to enhance the generation process preserving the model's characteristics. To effectively maintain the texture and details of the in-shop garment, we propose a textual inversion component that can map the visual features of the garment to the CLIP token embedding space and thus generate a set of pseudo-word token embeddings capable of conditioning the generation process. Experimental results on Dress Code and VITON-HD datasets demonstrate that our approach outperforms the competitors by a consistent margin, achieving a significant milestone for the task

Citation

If you make use of our work, please cite our paper:

@inproceedings{morelli2023ladi,
  title={{LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On}},
  author={Morelli, Davide and Baldrati, Alberto and Cartella, Giuseppe and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita},
  booktitle={Proceedings of the ACM International Conference on Multimedia},
  year={2023}
}

Getting Started

We recommend using the [**Anaconda**](https://www.anaconda.com/) package manager to avoid dependency/reproducibility problems. For Linux systems, you can find a conda installation guide [here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html). ### Installation 1. Clone the repository ```sh git clone https://github.com/miccunifi/ladi-vton ``` 2. Install Python dependencies ```sh conda env create -n ladi-vton -f environment.yml conda activate ladi-vton ``` Alternatively, you can create a new conda environment and install the required packages manually: ```sh conda create -n ladi-vton -y python=3.10 conda activate ladi-vton pip install torch==2.0.1 torchvision==0.15.2 opencv-python==4.7.0.72 diffusers==0.14.0 transformers==4.27.3 accelerate==0.18.0 clean-fid==0.1.35 torchmetrics[image]==0.11.4 wandb==0.14.0 matplotlib==3.7.1 tqdm xformers ``` ### Data Preparation #### DressCode 1. Download the [DressCode](https://github.com/aimagelab/dress-code) dataset 2. To enhance the performance of our warping module, we have discovered that using in-shop images with a white background yields better results. To facilitate this process, we now offer pre-extracted masks that can be used to remove the background from the images. You can download the masks from the following link: [here](https://github.com/miccunifi/ladi-vton/releases/download/weights/dresscode_masks.zip). Once downloaded, please extract the mask files and place them in the dataset folder alongside the corresponding images. Once the dataset is downloaded, the folder structure should look like this: ``` β”œβ”€β”€ DressCode | β”œβ”€β”€ test_pairs_paired.txt | β”œβ”€β”€ test_pairs_unpaired.txt | β”œβ”€β”€ train_pairs.txt β”‚ β”œβ”€β”€ [dresses | lower_body | upper_body] | | β”œβ”€β”€ test_pairs_paired.txt | | β”œβ”€β”€ test_pairs_unpaired.txt | | β”œβ”€β”€ train_pairs.txt β”‚ β”‚ β”œβ”€β”€ images β”‚ β”‚ β”‚ β”œβ”€β”€ [013563_0.jpg | 013563_1.jpg | 013564_0.jpg | 013564_1.jpg | ...] β”‚ β”‚ β”œβ”€β”€ masks β”‚ β”‚ β”‚ β”œβ”€β”€ [013563_1.png| 013564_1.png | ...] β”‚ β”‚ β”œβ”€β”€ keypoints β”‚ β”‚ β”‚ β”œβ”€β”€ [013563_2.json | 013564_2.json | ...] β”‚ β”‚ β”œβ”€β”€ label_maps β”‚ β”‚ β”‚ β”œβ”€β”€ [013563_4.png | 013564_4.png | ...] β”‚ β”‚ β”œβ”€β”€ skeletons β”‚ β”‚ β”‚ β”œβ”€β”€ [013563_5.jpg | 013564_5.jpg | ...] β”‚ β”‚ β”œβ”€β”€ dense β”‚ β”‚ β”‚ β”œβ”€β”€ [013563_5.png | 013563_5_uv.npz | 013564_5.png | 013564_5_uv.npz | ...] ``` #### VITON-HD 1. Download the [VITON-HD](https://github.com/shadow2496/VITON-HD) dataset Once the dataset is downloaded, the folder structure should look like this: ``` β”œβ”€β”€ VITON-HD | β”œβ”€β”€ test_pairs.txt | β”œβ”€β”€ train_pairs.txt β”‚ β”œβ”€β”€ [train | test] | | β”œβ”€β”€ image β”‚ β”‚ β”‚ β”œβ”€β”€ [000006_00.jpg | 000008_00.jpg | ...] β”‚ β”‚ β”œβ”€β”€ cloth β”‚ β”‚ β”‚ β”œβ”€β”€ [000006_00.jpg | 000008_00.jpg | ...] β”‚ β”‚ β”œβ”€β”€ cloth-mask β”‚ β”‚ β”‚ β”œβ”€β”€ [000006_00.jpg | 000008_00.jpg | ...] β”‚ β”‚ β”œβ”€β”€ image-parse-v3 β”‚ β”‚ β”‚ β”œβ”€β”€ [000006_00.png | 000008_00.png | ...] β”‚ β”‚ β”œβ”€β”€ openpose_img β”‚ β”‚ β”‚ β”œβ”€β”€ [000006_00_rendered.png | 000008_00_rendered.png | ...] β”‚ β”‚ β”œβ”€β”€ openpose_json β”‚ β”‚ β”‚ β”œβ”€β”€ [000006_00_keypoints.json | 000008_00_keypoints.json | ...] ```

Inference with Pre-trained Models

To run the inference on the Dress Code or VITON-HD dataset, run the following command: ```sh python src/inference.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --output_dir --test_order [paired | unpaired] --category [all | lower_body | upper_body | dresses ] --mixed_precision [no | fp16 | bf16] --enable_xformers_memory_efficient_attention --use_png --compute_metrics ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --test_order test setting, options: ['paired', 'unpaired'] --category category to test, options: ['all', 'lower_body', 'upper_body', 'dresses'] (default=all) --output_dir output directory --batch_size batch size (default=8) --mixed_precision mixed precision (no, fp16, bf16) (default=no) --enable_xformers_memory_efficient_attention enable memory efficient attention in xformers (default=False) --allow_tf32 allow TF32 on Ampere GPUs (default=False) --num_workers number of workers (default=8) --use_png use png instead of jpg (default=False) --compute_metrics compute metrics at the end of inference (default=False) ``` Since we release the pre-trained models via torch.hub, the models will be automatically downloaded when running the inference script. ### Metrics computation Once you have run the inference script and extracted the images, you can compute the metrics by running the following command: ```sh python src/utils/val_metrics.py --gen_folder --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --test_order [paired | unpaired] --category [all | lower_body | upper_body | dresses ] ``` ``` --gen_folder Path to the generated images folder. --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --test_order test setting, options: ['paired', 'unpaired'] --category category to test, options: ['all', 'lower_body', 'upper_body', 'dresses'] (default=all) --batch_size batch size (default=32) --workers number of workers (default=8) ```

Training

In this section, you'll find instructions on how to train all the components of our model from scratch. ### 1. Train Warping Module First of all, we need to train the warping module. To do so, run the following command: ```sh python src/train_tps.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --checkpoints_dir --exp_name ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot dataroot of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot dataroot of vitonhd dataset (required when dataset=vitonhd) --checkpoints_dir checkpoints directory --exp_name experiment name --batch_size batch size (default=16) --workers number of workers (default=10) --height height of the input images (default=512) --width width of the input images (default=384) --lr learning rate (default=1e-4) --const_weight weight for the TPS constraint loss (default=0.01) --wandb_log log training on wandb (default=False) --wandb_project wandb project name (default=LaDI_VTON_tps) --dense use dense uv map instead of keypoints (default=False) --only_extraction only extract the images using the trained networks without training (default=False) --vgg_weight weight for the VGG loss (refinement network) (default=0.25) --l1_weight weight for the L1 loss (refinement network) (default=1.0) --epochs_tps number of epochs for the TPS training (default=50) --epochs_refinement number of epochs for the refinement network training (default=50) ``` At the end of the training, the warped cloth images will be saved in the `data/warped_cloths` and `data/warped_cloths_unpaired` folders. To save computation time, in the following steps, we will use the pre-extracted warped cloth images. ### 2. Train EMASC To train the EMASC module, run the following command: ```sh python src/train_emasc.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --output_dir ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --output_dir output directory where the model predictions and checkpoints will be written --pretrained_model_name_or_path model identifier from huggingface.co/models (default=stabilityai/stable-diffusion-2-inpainting) --seed seed for reproducible training (default=1234) --train_batch_size batch size for training (default=16) --test_batch_size batch size for testing (default=16) --num_train_epochs number of training epochs (default=100) --max_train_steps maximum number of training steps. If provided, overrides num_train_epochs (default=40k) --gradient_accumulation_steps number of update steps to accumulate before performing a backward/update pass (default=1) --learning_rate learning rate (default=1e-5) --lr_scheduler learning rate scheduler, options: ['linear', 'cosine', 'cosine_with_restarts', 'polynomial', 'constant', 'constant_with_warmup'] (default=constant_with_warmup) --lr_warmup_steps number of warmup steps for learning rate scheduler (default=500) --allow_tf32 allow TF32 on Ampere GPUs (default=False) --adam_beta1 value of beta_1 for Adam optimizer (default=0.9) --adam_beta2 value of beta_2 for Adam optimizer (default=0.999) --adam_weight_decay value of weight decay for Adam optimizer (default=1e-2) --adam_epsilon value of epsilon for Adam optimizer (default=1e-8) --max_grad_norm maximum value of gradient norm for gradient clipping (default=1.0) --mixed_precision mixed precision training, options: ['no', 'fp16', 'bf16'] (default=fp16) --report_to where to report metrics, options: ['wandb', 'tensorboard', 'comet_ml'] (default=wandb) --checkpointing_steps number of steps between each checkpoint (default=10000) --resume_from_checkpoint whether training should be resumed from a previous checkpoint. Use a "latest" to automatically select the last available checkpoint. (default=None) --num_workers number of workers (default=8) --num_workers_test number of workers for test dataloader (default=8) --test_order test setting, options: ['paired', 'unpaired'] (default=paired) --emasc_type type of EMASC, options: ['linear', 'nonlinear'] (default=nonlinear) --vgg_weight weight for the VGG loss (default=0.5) --emasc_kernel kernel size for the EMASC module (default=3) --emasc_padding padding for the EMASC module (default=1) ``` At the end of the training, the EMASC checkpoints will be saved in the `output_dir` folder. ### 2.5 (Optional) Extract clip cloth embeddings To accelerate the training process for subsequent steps, consider pre-computing the CLIP cloth embeddings for each image in the dataset. To do so, run the following command: ```bash python src/utils/compute_cloth_clip_features.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --pretrained_model_name_or_path model identifier from huggingface.co/models (default=stabilityai/stable-diffusion-2-inpainting) --batch_size batch size (default=16) --num_workers number of workers (default=8) ``` The computed features will be saved in the `data/clip_cloth_embeddings` folder. In the following steps, to use the pre-computed features, make sure to use the `--use_clip_cloth_features` flag. ### 3. Pre-train the inversion adapter To pre-train the inversion adapter, run the following command: ```sh python src/train_inversion_adapter.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --output_dir --gradient_checkpointing --enable_xformers_memory_efficient_attention --use_clip_cloth_features ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --output_dir output directory where the model predictions and checkpoints will be written --pretrained_model_name_or_path model identifier from huggingface.co/models (default=stabilityai/stable-diffusion-2-inpainting) --seed seed for reproducible training (default=1234) --train_batch_size batch size for training (default=16) --test_batch_size batch size for testing (default=16) --num_train_epochs number of training epochs (default=100) --max_train_steps maximum number of training steps. If provided, overrides num_train_epochs (default=200k) --gradient_accumulation_steps number of update steps to accumulate before performing a backward/update pass (default=1) --gradient_checkpointing use gradient checkpointing to save memory at the expense of slower backward pass (default=False) --learning_rate learning rate (default=1e-5) --lr_scheduler learning rate scheduler, options: ['linear', 'cosine', 'cosine_with_restarts', 'polynomial', 'constant', 'constant_with_warmup'] (default=constant_with_warmup) --lr_warmup_steps number of warmup steps for learning rate scheduler (default=500) --allow_tf32 allow TF32 on Ampere GPUs (default=False) --adam_beta1 value of beta_1 for Adam optimizer (default=0.9) --adam_beta2 value of beta_2 for Adam optimizer (default=0.999) --adam_weight_decay value of weight decay for Adam optimizer (default=1e-2) --adam_epsilon value of epsilon for Adam optimizer (default=1e-8) --max_grad_norm maximum value of gradient norm for gradient clipping (default=1.0) --mixed_precision mixed precision training, options: ['no', 'fp16', 'bf16'] (default=fp16) --report_to where to report metrics, options: ['wandb', 'tensorboard', 'comet_ml'] (default=wandb) --checkpointing_steps number of steps between each checkpoint (default=50000) --resume_from_checkpoint whether training should be resumed from a previous checkpoint. Use a "latest" to automatically select the last available checkpoint. (default=None) --enable_xformers_memory_efficient_attention enable memory efficient attention in xformers (default=False) --num_workers number of workers (default=8) --num_workers_test number of workers for test dataloader (default=8) --test_order test setting, options: ['paired', 'unpaired'] (default=paired) --num_vstar number of predicted v* per image to use (default=16) --num_encoder_layers number of ViT layers to use in inversion adapter (default=1) --use_clip_cloth_features use precomputed clip cloth features instead of computing them each iteration (default=False). ``` At the end of the training, the inversion adapter checkpoints will be saved in the `output_dir` folder. **NOTE**: You can use the `--use_clip_cloth_features` flag only if you have previously computed the clip cloth features using the `src/utils/compute_cloth_clip_features.py` script (step 2.5). ### 4. Train VTO To successfully train the VTO model, ensure that you specify the correct path to the pre-trained inversion adapter checkpoint. If omitted, the inversion adapter will be trained from scratch. Additionally, don't forget to include the `--train_inversion_adapter` flag to enable the inversion adapter training during the VTO training process. To train the VTO model, run the following command: ```sh python src/train_vto.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --output_dir --inversion_adapter_dir --gradient_checkpointing --enable_xformers_memory_efficient_attention --use_clip_cloth_features --train_inversion_adapter ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --output_dir output directory where the model predictions and checkpoints will be written --inversion_adapter_dir path to the inversion adapter checkpoint directory. Should be the same as `output_dir` of the inversion adapter training script. If not specified, the inversion adapter will be trained from scratch. (default=None) --inversion_adapter_name name of the inversion adapter checkpoint. To load the latest checkpoint, use `latest`. (default=latest) --pretrained_model_name_or_path model identifier from huggingface.co/models (default=stabilityai/stable-diffusion-2-inpainting) --seed seed for reproducible training (default=1234) --train_batch_size batch size for training (default=16) --test_batch_size batch size for testing (default=16) --num_train_epochs number of training epochs (default=100) --max_train_steps maximum number of training steps. If provided, overrides num_train_epochs (default=200k) --gradient_accumulation_steps number of update steps to accumulate before performing a backward/update pass (default=1) --gradient_checkpointing use gradient checkpointing to save memory at the expense of slower backward pass (default=False) --learning_rate learning rate (default=1e-5) --lr_scheduler learning rate scheduler, options: ['linear', 'cosine', 'cosine_with_restarts', 'polynomial', 'constant', 'constant_with_warmup'] (default=constant_with_warmup) --lr_warmup_steps number of warmup steps for learning rate scheduler (default=500) --allow_tf32 allow TF32 on Ampere GPUs (default=False) --adam_beta1 value of beta_1 for Adam optimizer (default=0.9) --adam_beta2 value of beta_2 for Adam optimizer (default=0.999) --adam_weight_decay value of weight decay for Adam optimizer (default=1e-2) --adam_epsilon value of epsilon for Adam optimizer (default=1e-8) --max_grad_norm maximum value of gradient norm for gradient clipping (default=1.0) --mixed_precision mixed precision training, options: ['no', 'fp16', 'bf16'] (default=fp16) --report_to where to report metrics, options: ['wandb', 'tensorboard', 'comet_ml'] (default=wandb) --checkpointing_steps number of steps between each checkpoint (default=50000) --resume_from_checkpoint whether training should be resumed from a previous checkpoint. Use a "latest" to automatically select the last available checkpoint. (default=None) --enable_xformers_memory_efficient_attention enable memory efficient attention in xformers (default=False) --num_workers number of workers (default=8) --num_workers_test number of workers for test dataloader (default=8) --test_order test setting, options: ['paired', 'unpaired'] (default=paired) --uncond_fraction fraction of unconditioned training samples (default=0.2) --text_usage text features to use, options: ['none', 'noun_chunks', 'inversion_adapter'] (default=inversion_adapter) --cloth_input_type cloth input type, options: ['none', 'warped'], (default=warped) --num_vstar number of predicted v* per image to use (default=16) --num_encoder_layers number of ViT layers to use in inversion adapter (default=1) --train_inversion_adapter train the inversion adapter during the VTO training (default=False) --use_clip_cloth_features use precomputed clip cloth features instead of computing them each iteration (default=False). ``` At the end of the training, the checkpoints will be saved in the `output_dir` folder. **NOTE**: You can use the `--use_clip_cloth_features` flag only if you have previously computed the clip cloth features using the `src/utils/compute_cloth_clip_features.py` script (step 2.5). ### 5. Inference with the trained models Before running the inference, make sure to specify the correct path to all the trained checkpoints. Make sure to also use coherent hyperparameters with the ones used during training. To run the inference on the Dress Code or VITON-HD dataset, run the following command: ```sh python src/eval.py --dataset [dresscode | vitonhd] --dresscode_dataroot --vitonhd_dataroot --output_dir --save_name --test_order [paired | unpaired] --unet_dir --inversion_adapter_dir --emasc_dir --category [all | lower_body | upper_body | dresses ] --enable_xformers_memory_efficient_attention --use_png --compute_metrics ``` ``` --dataset dataset to use, options: ['dresscode', 'vitonhd'] --dresscode_dataroot data root of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot data root of vitonhd dataset (required when dataset=vitonhd) --output_dir output directory where the generated images will be written --save_name name of the generated images folder inside `output_dir` --test_order test setting, options: ['paired', 'unpaired'] --unet_dir path to the UNet checkpoint directory. Should be the same as `output_dir` of the VTO training script --unet_name name of the UNet checkpoint. To load the latest checkpoint, use `latest`. (default=latest) --inversion_adapter_dir path to the inversion adapter checkpoint directory. Should be the same as `output_dir` of the VTO training script. Needed only if `--text_usage` is set to `inversion_adapter`. (default=None) --inversion_adapter_name name of the inversion adapter checkpoint. To load the latest checkpoint, use `latest`. (default=latest) --emasc_dir path to the EMASC checkpoint directory. Should be the same as `output_dir` of the EMASC training script. Needed when --emasc_type!=none. (default=None) --emasc_name name of the EMASC checkpoint. To load the latest checkpoint, use `latest`. (default=latest) --pretrained_model_name_or_path model identifier from huggingface.co/models (default=stabilityai/stable-diffusion-2-inpainting) --seed seed for reproducible training (default=1234) --batch_size batch size(default=8) --allow_tf32 allow TF32 on Ampere GPUs (default=False) --enable_xformers_memory_efficient_attention enable memory efficient attention in xformers (default=False) --num_workers number of workers (default=8) --category category to test, options: ['all', 'lower_body', 'upper_body', 'dresses'] (default=all) --emasc_type type of EMASC, options: ['linear', 'nonlinear'] (default=nonlinear) --emasc_kernel kernel size for the EMASC module (default=3) --emasc_padding padding for the EMASC module (default=1) --text_usage text features to use, options: ['none', 'noun_chunks', 'inversion_adapter'] (default=inversion_adapter) --cloth_input_type cloth input type, options: ['none', 'warped'], (default=warped) --num_vstar number of predicted v* per image to use (default=16) --num_encoder_layers number of ViT layers to use in inversion adapter (default=1) --use_png use png instead of jpg (default=False) --num_inference_steps number of diffusion steps at inference time (default=50) --guidance_scale guidance scale of the diffusion (default=7.5) --use_clip_cloth_features use precomputed clip cloth features instead of computing them each iteration (default=False). --compute_metrics compute metrics at the end of inference (default=False) ``` The generated images will be saved in the `output_dir/save_name_{test_order}` folder. **NOTE**: You can use the `--use_clip_cloth_features` flag only if you have previously computed the clip cloth features using the `src/utils/compute_cloth_clip_features.py` script (step 2.5).

Acknowledgements

This work has partially been supported by the PNRR project β€œFuture Artificial Intelligence Research (FAIR)”, by the PRIN project β€œCREATIVE: CRoss-modal understanding and gEnerATIon of Visual and tExtual content” (CUP B87G22000460001), both co-funded by the Italian Ministry of University and Research, and by the European Commission under European Horizon 2020 Programme, grant number 101004545 - ReInHerit.

LICENSE

Creative Commons License
All material is made available under Creative Commons BY-NC 4.0. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicate any changes that you've made.