First commit

Dockerfile

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+# syntax=docker/dockerfile:1.4
+
+# Step 1: Setup backend environment with Miniconda
+FROM continuumio/miniconda3:latest AS backend
+
+# Create a non-root user for security purposes
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Set the working directory for the backend
+WORKDIR /api
+
+# Copy the environment file and install dependencies
+COPY --chown=user api/environment.yml ./
+RUN conda env create -f environment.yml && conda clean -a -y
+
+# Ensure conda is initialized and available for subsequent RUN commands
+SHELL ["conda", "run", "-n", "mgd", "/bin/bash", "-c"]
+
+# Copy the backend application code
+COPY --chown=user api/ .
+
+# Expose port 7860 for the backend service
+EXPOSE 7860
+
+# Set default command to run the backend model using the specified port
+CMD ["conda", "run", "-n", "mgd", "python", "app.py", "--port", "7860"]

api/Dockerfile_backend

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+# import conda
+FROM continuumio/miniconda3:latest
+
+RUN conda create -n mgd -y python=3.9
+RUN conda activate mgd
+RUN pip install torch==1.12.1 torchmetrics==0.11.0 opencv-python==4.7.0.68 diffusers==0.12.0 transformers==4.25.1 accelerate==0.15.0 clean-fid==0.1.35 torchmetrics[image]==0.11.0 flask
+
+
+WORKDIR /app
+#COPY requirements.txt requirements.txt
+#RUN pip install -r requirements.txt
+COPY . .
+
+CMD ["python", "app.py"]

api/app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+#import base64
+#from flask_limiter import Limiter
+#from flask_limiter.util import get_remote_address
+
+app = Flask(__name__)
+#CORS(app, resources={r"/generate-design": {"origins": "https://uomolepre-github-io.vercel.app"}})
+
+CORS(app, resources={r"/generate-design": {"origins": "*"}})
+
+'''
+limiter = Limiter(
+    get_remote_address,
+    app=app,
+    default_limits=["10 per minute"]  # Limita a 10 richieste al minuto per ogni IP
+)'''
+
+@app.route('/generate-design', methods=['POST'])
+#@limiter.limit("10 per minute")
+
+def generate_design():
+    '''
+    data = request.json
+
+    # Extract image and json
+    image_data = data.get('image')
+    design_data = data.get('designData')
+
+    ## Extract image
+    if image_data:
+        image_bytes = base64.b64decode(image_data.split(',')[1])  # Decodifica la parte base64
+        with open("received_design.png", "wb") as image_file:
+            image_file.write(image_bytes)
+    
+    # Update JSON
+    design_data['status'] = 'success'
+    design_data['message'] = 'Design received and processed'
+    
+    return jsonify(design_data)
+    '''
+
+    command = [
+        'python', 'app/model/src/eval.py',
+        '--dataset_path', './assets/data/vitonhd',
+        '--batch_size', '1',
+        '--mixed_precision', 'fp16',
+        '--output_dir', '../FINALE',
+        '--save_name', 'generato_paired',
+        '--num_workers_test', '4',
+        '--sketch_cond_rate', '0.2',
+        '--dataset', 'vitonhd',
+        '--start_cond_rate', '0.0',
+        '--test_order', 'paired'
+    ]
+    
+    # Esegue il comando
+    try:
+        subprocess.run(command, check=True)
+        return jsonify({"status": "success", "message": "Command executed successfully"})
+    except subprocess.CalledProcessError as e:
+        return jsonify({"status": "error", "message": str(e)}), 500
+ 
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)

api/environment.yml

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+name: mgd
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - ca-certificates=2023.08.22=h06a4308_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - libffi=3.4.4=h6a678d5_0
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - ncurses=6.4=h6a678d5_0
+  - openssl=3.0.11=h7f8727e_2
+  - pip=23.2.1=py39h06a4308_0
+  - python=3.9.18=h955ad1f_0
+  - readline=8.2=h5eee18b_0
+  - setuptools=68.0.0=py39h06a4308_0
+  - sqlite=3.41.2=h5eee18b_0
+  - tk=8.6.12=h1ccaba5_0
+  - tzdata=2023c=h04d1e81_0
+  - wheel=0.38.4=py39h06a4308_0
+  - xz=5.4.2=h5eee18b_0
+  - zlib=1.2.13=h5eee18b_0
+  - pip:
+      - accelerate==0.15.0
+      - certifi==2023.7.22
+      - charset-normalizer==3.2.0
+      - clean-fid==0.1.35
+      - diffusers==0.12.0
+      - filelock==3.12.4
+      - flask
+      - flask-cors
+      - fsspec==2023.9.1
+      - huggingface-hub==0.17.2
+      - idna==3.4
+      - importlib-metadata==6.8.0
+      - lpips==0.1.4
+      - numpy==1.26.0
+      - opencv-python==4.7.0.68
+      - packaging==23.1
+      - pillow==10.0.1
+      - psutil==5.9.5
+      - pyyaml==6.0.1
+      - regex==2023.8.8
+      - requests==2.31.0
+      - scipy==1.11.2
+      - tokenizers==0.13.3
+      - torch==1.12.1
+      - torch-fidelity==0.3.0
+      - torchmetrics==0.11.0
+      - torchmetrics[image]==0.11.0
+      - torchvision==0.13.1
+      - tqdm==4.66.1
+      - transformers==4.25.1
+      - typing-extensions==4.8.0
+      - urllib3==2.0.5
+      - zipp==3.17.0

api/model/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

api/model/.gitignore

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+# Created by https://www.toptal.com/developers/gitignore/api/pycharm,visualstudiocode,opencv,python,git
+# Edit at https://www.toptal.com/developers/gitignore?templates=pycharm,visualstudiocode,opencv,python,git
+
+### Git ###
+# Created by git for backups. To disable backups in Git:
+# $ git config --global mergetool.keepBackup false
+*.orig
+
+# Created by git when using merge tools for conflicts
+*.BACKUP.*
+*.BASE.*
+*.LOCAL.*
+*.REMOTE.*
+*_BACKUP_*.txt
+*_BASE_*.txt
+*_LOCAL_*.txt
+*_REMOTE_*.txt
+
+### OpenCV ###
+#OpenCV for Mac and Linux
+#build and release folders
+*/CMakeFiles
+*/CMakeCache.txt
+*/Makefile
+*/cmake_install.cmake
+.DS_Store
+
+### PyCharm ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### PyCharm Patch ###
+# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721
+
+# *.iml
+# modules.xml
+# .idea/misc.xml
+# *.ipr
+
+# Sonarlint plugin
+# https://plugins.jetbrains.com/plugin/7973-sonarlint
+.idea/**/sonarlint/
+
+# SonarQube Plugin
+# https://plugins.jetbrains.com/plugin/7238-sonarqube-community-plugin
+.idea/**/sonarIssues.xml
+
+# Markdown Navigator plugin
+# https://plugins.jetbrains.com/plugin/7896-markdown-navigator-enhanced
+.idea/**/markdown-navigator.xml
+.idea/**/markdown-navigator-enh.xml
+.idea/**/markdown-navigator/
+
+# Cache file creation bug
+# See https://youtrack.jetbrains.com/issue/JBR-2257
+.idea/$CACHE_FILE$
+
+# CodeStream plugin
+# https://plugins.jetbrains.com/plugin/12206-codestream
+.idea/codestream.xml
+
+# Azure Toolkit for IntelliJ plugin
+# https://plugins.jetbrains.com/plugin/8053-azure-toolkit-for-intellij
+.idea/**/azureSettings.xml
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
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+
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+.pybuilder/
+target/
+
+# Jupyter Notebook
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+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
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+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+
+# pdm
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+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
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+ENV/
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+
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+
+# Rope project settings
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+
+# mkdocs documentation
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+
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+.dmypy.json
+dmypy.json
+
+# Pyre type checker
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+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
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+!.vscode/*.code-snippets
+
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+
+
+images/
+
+# End of https://www.toptal.com/developers/gitignore/api/pycharm,visualstudiocode,opencv,python,git

api/model/LICENSE

@@ -0,0 +1,407 @@
+Attribution-NonCommercial 4.0 International
+
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api/model/README_git.md

@@ -0,0 +1,219 @@
+# Multimodal Garment Designer (ICCV 2023)
+### Human-Centric Latent Diffusion Models for Fashion Image Editing
+[**Alberto Baldrati**](https://scholar.google.com/citations?hl=en&user=I1jaZecAAAAJ)**\***,
+[**Davide Morelli**](https://scholar.google.com/citations?user=UJ4D3rYAAAAJ&hl=en)**\***,
+[**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.
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2304.02051)
+[![GitHub Stars](https://img.shields.io/github/stars/aimagelab/multimodal-garment-designer?style=social)](https://github.com/aimagelab/multimodal-garment-designer)
+
+This is the **official repository** for the [**paper**](https://arxiv.org/abs/2304.02051) "*Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing*".
+
+🔥🔥 **[21/03/2024] If you are interested in multimodal fashion image editing take a look at our most recent work [Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing](https://arxiv.org/abs/2403.14828). We introduce Ti-MGD a novel approach that also integrates fabric texture conditioning [![Repo](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/aimagelab/Ti-MGD)**
+
+## Overview
+
+<p align="center">
+    <img src="images/1.gif" style="max-width:500px">
+</p>
+
+>**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
+@inproceedings{baldrati2023multimodal,
+  title={Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing},
+  author={Baldrati, Alberto and Morelli, Davide and Cartella, Giuseppe and Cornia, Marcella and Bertini, Marco and Cucchiara, Rita},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  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/aimagelab/multimodal-garment-designer
+```
+
+2. Install Python dependencies
+
+```sh
+conda env create -n mgd -f environment.yml
+conda activate mgd
+```
+
+Alternatively, you can create a new conda environment and install the required packages manually:
+
+```sh
+conda create -n mgd -y python=3.9
+conda activate mgd
+pip install torch==1.12.1 torchmetrics==0.11.0 opencv-python==4.7.0.68 diffusers==0.12.0 transformers==4.25.1 accelerate==0.15.0 clean-fid==0.1.35 torchmetrics[image]==0.11.0
+```
+
+## Inference
+
+To run the inference please use the following:
+
+```
+python src/eval.py --dataset_path <path> --batch_size <int> --mixed_precision fp16 --output_dir <path> --save_name <string> --num_workers_test <int> --sketch_cond_rate 0.2 --dataset <dresscode|vitonhd> --start_cond_rate 0.0 --test_order <paired|unpaired>
+```
+
+- ```dataset_path``` is the path to the dataset (change accordingly to the dataset parameter)
+- ```dataset``` dataset name to be used
+- ```output_dir``` path to the output directory
+- ```save_name``` name of the output dir subfolder where the generated images are saved
+- ```start_cond_rate``` rate {0.0,1.0} of denoising steps that will be used as offset to start sketch conditioning
+- ```sketch_cond_rate``` rate {0.0,1.0} of denoising steps in which sketch cond is applied
+- ```test_order``` test setting (paired | unpaired)
+
+Note that we provide a few sample images to test MGD simply by cloning this repo (*i.e.*, assets/data). To execute the code set 
+- Dress Code Multimodal dataset
+    - ```dataset_path``` to ```../assets/data/dresscode```
+    - ```dataset``` to ```dresscode```
+- Viton-HD Multimodal dataset
+    - ```dataset_path``` to ```../assets/data/vitonhd```
+    - ```dataset``` to ```vitonhd```
+
+It is possible to run the inference on the whole Dress Code Multimodal or Viton-HD Multimodal dataset simply changing the ```dataset_path``` and ```dataset``` according with the downloaded and prepared datasets (see sections below).
+
+
+## Pre-trained models
+The model and checkpoints are available via torch.hub.
+
+Load the MGD denoising UNet model using the following code:
+
+```
+import torch
+unet = torch.hub.load(
+    dataset=<dataset>, 
+    repo_or_dir='aimagelab/multimodal-garment-designer', 
+    source='github', 
+    model='mgd', 
+    pretrained=True
+    )
+```
+
+- ```dataset``` dataset name (dresscode | vitonhd)
+
+Use the denoising network with our custom diffusers pipeline as follow:
+
+```
+from src.mgd_pipelines.mgd_pipe import MGDPipe
+from diffusers import AutoencoderKL, DDIMScheduler
+from transformers import CLIPTextModel, CLIPTokenizer
+
+pretrained_model_name_or_path = "runwayml/stable-diffusion-inpainting"
+
+text_encoder = CLIPTextModel.from_pretrained(
+    pretrained_model_name_or_path, 
+    subfolder="text_encoder"
+    )
+
+vae = AutoencoderKL.from_pretrained(
+    pretrained_model_name_or_path, 
+    subfolder="vae"
+    )
+
+tokenizer = CLIPTokenizer.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="tokenizer",
+    )
+
+val_scheduler = DDIMScheduler.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="scheduler"
+    )
+val_scheduler.set_timesteps(50)
+
+mgd_pipe = MGDPipe(
+    text_encoder=text_encoder,
+    vae=vae,
+    unet=unet,
+    tokenizer=tokenizer,
+    scheduler=val_scheduler,
+    )
+```
+
+For an extensive usage case see the file ```eval.py``` in the main repo.
+
+## Datasets
+We do not hold rights on the original Dress Code and Viton-HD datasets. Please refer to the original papers for more information.
+
+Start by downloading the original datasets from the following links:
+- Viton-HD **[[link](https://github.com/shadow2496/VITON-HD)]**
+- Dress Code **[[link](https://github.com/aimagelab/dress-code)]**
+
+
+Download the Dress Code Multimodal and Viton-HD Multimodal additional data annotations from here.
+
+- Dress Code Multimodal **[[link](https://drive.google.com/file/d/1dwnAi1CNmmF_YdbIDfm8078dGh4ci47L/view?usp=sharing)]**
+- Viton-HD Multimodal **[[link](https://drive.google.com/file/d/1Z2b9YkyBPA_9ZDC54Y5muW9Q8yfAqWSH/view?usp=sharing)]**
+
+### Dress Code Multimodal Data Preparation
+Once data is downloaded prepare the dataset folder as follows:
+
+<pre>
+Dress Code
+| <b>fine_captions.json</b>
+| <b>coarse_captions.json</b>
+| test_pairs_paired.txt
+| test_pairs_unpaired.txt
+| train_pairs.txt
+| <b>test_stitch_map</b>
+|---- [category]
+|-------- images
+|-------- keypoints
+|-------- skeletons
+|-------- dense
+|-------- <b>im_sketch</b>
+|-------- <b>im_sketch_unpaired</b>
+...
+</pre>
+
+### Viton-HD Multimodal Data Preparation
+Once data is downloaded prepare the dataset folder as follows:
+
+<pre>
+Viton-HD
+| <b>captions.json</b>
+|---- train
+|-------- image
+|-------- cloth
+|-------- image-parse-v3
+|-------- openpose_json
+|-------- <b>im_sketch</b>
+|-------- <b>im_sketch_unpaired</b>
+...
+|---- test
+...
+|-------- <b>im_sketch</b>
+|-------- <b>im_sketch_unpaired</b>
+...
+</pre>
+
+
+## TODO
+- [ ] training code
+
+## Acknowledgements
+This work has partially been supported by the PNRR project “Future Artificial Intelligence Research (FAIR)”, by the PRIN project “CREATIVE: 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
+<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />All material is available under [Creative Commons BY-NC 4.0](https://creativecommons.org/licenses/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** you've made.

api/model/assets/data/dresscode/dresses/keypoints/051994_2.json

@@ -0,0 +1 @@
+{"keypoints": [[205.0, 0.0, 0.7454180121421814, 0.0], [193.0, 54.0, 0.9695595502853394, 1.0], [153.0, 46.0, 0.9210028052330017, 2.0], [163.0, 133.0, 0.8196305632591248, 3.0], [182.0, 193.0, 0.8251658082008362, 4.0], [234.0, 59.0, 0.9184455871582031, 5.0], [252.0, 133.0, 0.9567239880561829, 6.0], [186.0, 139.0, 0.8934434652328491, 7.0], [180.0, 199.0, 0.803178071975708, 8.0], [164.0, 323.0, 0.9721584916114807, 9.0], [140.0, 438.0, 0.8884657025337219, 10.0], [230.0, 199.0, 0.7910050749778748, 11.0], [223.0, 322.0, 0.8632205128669739, 12.0], [211.0, 436.0, 0.8701340556144714, 13.0], [197.0, 0.0, 0.5216715931892395, 14.0], [214.0, 0.0, 0.610032856464386, 15.0], [186.0, 0.0, 0.5596458911895752, 16.0], [224.0, 0.0, 0.6544457077980042, 17.0]]}

api/model/assets/data/dresscode/dresses/keypoints/052012_2.json

@@ -0,0 +1 @@
+{"keypoints": [[209.0, 0.0, 0.5854654312133789, 0.0], [194.0, 46.0, 0.9134835004806519, 1.0], [152.0, 37.0, 0.9346101880073547, 2.0], [137.0, 118.0, 0.8949743509292603, 3.0], [142.0, 188.0, 0.9262046813964844, 4.0], [235.0, 53.0, 0.9443597793579102, 5.0], [242.0, 134.0, 0.8160827159881592, 6.0], [249.0, 208.0, 0.7443605065345764, 7.0], [167.0, 194.0, 0.8199771046638489, 8.0], [160.0, 319.0, 0.907497763633728, 9.0], [143.0, 447.0, 0.8496435284614563, 10.0], [224.0, 195.0, 0.7766034007072449, 11.0], [214.0, 307.0, 0.8796323537826538, 12.0], [195.0, 430.0, 0.9041213393211365, 13.0], [201.0, 0.0, 0.36648842692375183, 14.0], [217.0, 0.0, 0.44042539596557617, 15.0], [187.0, 0.0, 0.40841901302337646, 16.0], [227.0, 0.0, 0.2826925814151764, 17.0]]}

api/model/assets/data/dresscode/dresses/test_pairs_paired.txt

@@ -0,0 +1 @@
+052012_0.jpg    052012_1.jpg

api/model/assets/data/dresscode/dresses/test_pairs_unpaired.txt

@@ -0,0 +1 @@
+052012_0.jpg    052033_1.jpg

api/model/assets/data/dresscode/lower_body/keypoints/050855_2.json

@@ -0,0 +1 @@
+{"keypoints": [[183.0, 0.0, 0.5823147296905518, 0.0], [208.0, 53.0, 0.9465193152427673, 1.0], [166.0, 48.0, 0.9355934262275696, 2.0], [149.0, 138.0, 0.8791153430938721, 3.0], [142.0, 213.0, 0.9196943640708923, 4.0], [249.0, 54.0, 0.9031239151954651, 5.0], [257.0, 143.0, 0.8434740900993347, 6.0], [269.0, 214.0, 0.9049280881881714, 7.0], [181.0, 196.0, 0.7393415570259094, 8.0], [162.0, 321.0, 0.8804817795753479, 9.0], [125.0, 435.0, 0.8968222141265869, 10.0], [235.0, 199.0, 0.7837328314781189, 11.0], [233.0, 322.0, 0.927562415599823, 12.0], [217.0, 439.0, 0.8323052525520325, 13.0], [176.0, 0.0, 0.2889324426651001, 14.0], [190.0, 0.0, 0.395557701587677, 15.0], [-1.0, -1.0, 0.0, -1.0], [213.0, 0.0, 0.4816431403160095, 16.0]]}

api/model/assets/data/dresscode/lower_body/keypoints/050915_2.json

@@ -0,0 +1 @@
+{"keypoints": [[204.0, 0.0, 0.7293938398361206, 0.0], [199.0, 62.0, 0.9325352907180786, 1.0], [152.0, 67.0, 0.9000401496887207, 2.0], [144.0, 146.0, 0.9149616956710815, 3.0], [132.0, 213.0, 0.8797067999839783, 4.0], [246.0, 59.0, 0.8685039281845093, 5.0], [279.0, 135.0, 0.9230703115463257, 6.0], [228.0, 163.0, 0.908068835735321, 7.0], [160.0, 218.0, 0.8140541315078735, 8.0], [160.0, 327.0, 0.9113076329231262, 9.0], [162.0, 432.0, 0.8555663228034973, 10.0], [220.0, 218.0, 0.7834039926528931, 11.0], [227.0, 327.0, 0.8965063691139221, 12.0], [225.0, 434.0, 0.876929759979248, 13.0], [190.0, 0.0, 0.510018527507782, 14.0], [211.0, 0.0, 0.48887312412261963, 15.0], [172.0, 0.0, 0.6903271079063416, 16.0], [219.0, 0.0, 0.19232189655303955, 17.0]]}

api/model/assets/data/dresscode/lower_body/test_pairs_paired.txt

@@ -0,0 +1,2 @@
+050855_0.jpg    050855_1.jpg
+050915_0.jpg    050915_1.jpg

api/model/assets/data/dresscode/lower_body/test_pairs_unpaired.txt

@@ -0,0 +1,2 @@
+050855_0.jpg    050908_1.jpg
+050915_0.jpg    051078_1.jpg

api/model/assets/data/dresscode/upper_body/keypoints/048462_2.json

@@ -0,0 +1 @@
+{"keypoints": [[141.0, 0.0, 0.5669732689857483, 0.0], [138.0, 52.0, 0.9372020363807678, 1.0], [97.0, 51.0, 0.9566869735717773, 2.0], [91.0, 127.0, 0.9176412224769592, 3.0], [92.0, 196.0, 0.9124826192855835, 4.0], [175.0, 52.0, 0.9164174795150757, 5.0], [190.0, 126.0, 0.9348884224891663, 6.0], [200.0, 192.0, 0.9007474780082703, 7.0], [121.0, 186.0, 0.8528367280960083, 8.0], [131.0, 298.0, 0.9579567909240723, 9.0], [147.0, 408.0, 0.9156394600868225, 10.0], [171.0, 184.0, 0.8514549732208252, 11.0], [188.0, 299.0, 0.98090660572052, 12.0], [207.0, 421.0, 0.8863993287086487, 13.0], [133.0, 0.0, 0.38882675766944885, 14.0], [148.0, 0.0, 0.43583664298057556, 15.0], [120.0, 0.0, 0.44555413722991943, 16.0], [159.0, 0.0, 0.3003842532634735, 17.0]]}

api/model/assets/data/dresscode/upper_body/keypoints/048466_2.json

@@ -0,0 +1 @@
+{"keypoints": [[200.0, 0.0, 0.688823401927948, 0.0], [201.0, 49.0, 0.9461612105369568, 1.0], [162.0, 46.0, 0.9666376709938049, 2.0], [151.0, 113.0, 0.938896894454956, 3.0], [146.0, 177.0, 0.9722222685813904, 4.0], [238.0, 52.0, 0.9425340890884399, 5.0], [247.0, 117.0, 0.9355710744857788, 6.0], [246.0, 180.0, 0.9147509932518005, 7.0], [170.0, 173.0, 0.8584168553352356, 8.0], [159.0, 281.0, 0.938974916934967, 9.0], [156.0, 372.0, 0.9198458790779114, 10.0], [221.0, 173.0, 0.8431934714317322, 11.0], [219.0, 280.0, 0.9141676425933838, 12.0], [214.0, 367.0, 0.944513201713562, 13.0], [192.0, 0.0, 0.4936670660972595, 14.0], [208.0, 0.0, 0.5329393148422241, 15.0], [182.0, 0.0, 0.4606328308582306, 16.0], [219.0, 0.0, 0.460784375667572, 17.0]]}

api/model/assets/data/dresscode/upper_body/test_pairs_paired.txt

@@ -0,0 +1 @@
+048462_0.jpg    048462_1.jpg

api/model/assets/data/dresscode/upper_body/test_pairs_unpaired.txt

@@ -0,0 +1 @@
+048462_0.jpg    049951_1.jpg