文章LaDI-VTON的源码

# LaDI-VTON ### Latent Diffusion Textual-Inversion Enhanced Virtual Try-On [**Davide Morelli**](https://scholar.google.com/citations?user=UJ4D3rYAAAAJ&hl=en)**\***, [**Alberto Baldrati**](https://scholar.google.com/citations?hl=en&user=I1jaZecAAAAJ)**\***, [**Giuseppe Cartella**](https://scholar.google.com/citations?hl=en&user=0sJ4VCcAAAAJ), [**Marcella Cornia**](https://scholar.google.com/citations?hl=en&user=DzgmSJEAAAAJ), [**Marco Bertini**](https://scholar.google.com/citations?user=SBm9ZpYAAAAJ&hl=en), [**Rita Cucchiara**](https://scholar.google.com/citations?hl=en&user=OM3sZEoAAAAJ) **\*** Equal contribution. [](https://arxiv.org/abs/2305.13501) [](https://github.com/miccunifi/ladi-vton) This is the **official repository** for the [**paper**](https://arxiv.org/abs/2305.13501) "*LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On*". ## Overview  >**Abstract**: <br> > Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly focused on the virtual try-on of garments, we propose the task of multimodal-conditioned fashion image editing, guiding the generation of human-centric fashion images by following multimodal prompts, such as text, human body poses, and garment sketches. We tackle this problem by proposing a new architecture based on latent diffusion models, an approach that has not been used before in the fashion domain. Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner. Experimental results on these new datasets demonstrate the effectiveness of our proposal, both in terms of realism and coherence with the given multimodal inputs. ## Citation If you make use of our work, please cite our paper: ```bibtex @article{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}, journal={arXiv preprint arXiv:2305.13501}, 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: ``` ├── DressCode | ├── test_pairs_paired.txt | ├── test_pairs_unpaired.txt | ├── train_pairs.txt │ ├── [dresses | lower_body | upper_body] | | ├── test_pairs_paired.txt | | ├── test_pairs_unpaired.txt | | ├── train_pairs.txt │ │ ├── images │ │ │ ├── [013563_0.jpg | 013563_1.jpg | 013564_0.jpg | 013564_1.jpg | ...] │ │ ├── masks │ │ │ ├── [013563_1.png| 013564_1.png | ...] │ │ ├── keypoints │ │ │ ├── [013563_2.json | 013564_2.json | ...] │ │ ├── label_maps │ │ │ ├── [013563_4.png | 013564_4.png | ...] │ │ ├── skeletons │ │ │ ├── [013563_5.jpg | 013564_5.jpg | ...] │ │ ├── dense │ │ │ ├── [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: ``` ├── VITON-HD | ├── test_pairs.txt | ├── train_pairs.txt │ ├── [train | test] | | ├── image │ │ │ ├── [000006_00.jpg | 000008_00.jpg | ...] │ │ ├── cloth │ │ │ ├── [000006_00.jpg | 000008_00.jpg | ...] │ │ ├── cloth-mask │ │ │ ├── [000006_00.jpg | 000008_00.jpg | ...] │ │ ├── image-parse-v3 │ │ │ ├── [000006_00.png | 000008_00.png | ...] │ │ ├── openpose_img │ │ │ ├── [000006_00_rendered.png | 000008_00_rendered.png | ...] │ │ ├── openpose_json │ │ │ ├── [000006_00_keypoints.json | 000008_00_keypoints.json | ...] ``` ## Inference 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 <path> --vitonhd_dataroot <path> --output_dir <path> --test_order [paired | unpaired] --category [all | lower_body | upper_body | dresses ] --batch_size <int> --mixed_precision [no | fp16 | bf16] --enable_xformers_memory_efficient_attention <store_true> --num_workers <int> --use_png <store_true> --compute_metrics <store_true> ``` ``` --dataset dataset to use (dresscode or vitonhd) --dresscode_dataroot dataroot of dresscode dataset (required when dataset=dresscode) --vitonhd_dataroot dataroot of vitonhd dataset (required when dataset=vitonhd) --test_order test setting (paired or unpaired) --category category to test (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) --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 <path> --dataset [dresscode | vitonhd] --dresscode_dataroot <p