Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* 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
+style_images/Scream.jpg filter=lfs diff=lfs merge=lfs -text
+style_images/StarryNight.jpg filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.gitignore

@@ -0,0 +1,165 @@
+# Mac-specific
+.DS_Store
+
+# 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
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# 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:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#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/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# 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
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# 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
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Jai Amin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,7 @@
 ---
-title: Style Transfer
-emoji: 📊
-colorFrom: purple
-colorTo: yellow
+title: style-transfer
+app_file: app.py
 sdk: gradio
 sdk_version: 4.44.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# style-transfer

app.py

@@ -0,0 +1,129 @@
+from PIL import Image
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+import torchvision.models as models
+import torchvision.utils as utils
+import gradio as gr
+
+device = 'cpu'
+if torch.backends.mps.is_available():
+    device = 'mps'
+if torch.cuda.is_available():
+    device = 'cuda'
+print('DEVICE:', device)
+
+class VGG_19(nn.Module):
+    def __init__(self):
+        super(VGG_19, self).__init__()
+        self.model = models.vgg19(pretrained=True).features[:30]
+        
+        for i, _ in enumerate(self.model):
+            if i in [4, 9, 18, 27]:
+                self.model[i] = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+                
+    def forward(self, x):
+        features = []
+        
+        for i, layer in enumerate(self.model):
+            x = layer(x)
+            if i in [0, 5, 10, 19, 28]:
+                features.append(x)
+        return features
+    
+model = VGG_19().to(device)
+for param in model.parameters():
+    param.requires_grad = False
+
+def load_img(img: Image, img_size):
+    original_size = img.size
+    
+    transform = transforms.Compose([
+        transforms.Resize((img_size, img_size)),
+        transforms.ToTensor()
+    ])
+    img = transform(img).unsqueeze(0)
+    return img, original_size
+
+def load_img_from_path(path_to_image, img_size):
+    img = Image.open(path_to_image)
+    original_size = img.size
+    
+    transform = transforms.Compose([
+        transforms.Resize((img_size, img_size)),
+        transforms.ToTensor()
+    ])
+    img = transform(img).unsqueeze(0)
+    return img, original_size
+
+def save_img(img, original_size):
+    img = img.cpu().clone()
+    img = img.squeeze(0)
+    
+    # address tensor value scaling and quantization
+    img = torch.clamp(img, 0, 1)
+    img = img.mul(255).byte()
+    
+    unloader = transforms.ToPILImage()
+    img = unloader(img)
+    
+    img = img.resize(original_size, Image.Resampling.LANCZOS)
+    
+    return img
+
+
+def transfer_style(content_image):
+    style_img_filename = 'StarryNight.jpg'
+    img_size = 512
+    content_img, original_size = load_img(content_image, img_size)
+    content_img = content_img.to(device)
+    style_img = load_img_from_path(f'./style_images/{style_img_filename}', img_size)[0].to(device)
+
+    iters = 100
+    lr = 1e-1
+    alpha = 1
+    beta = 1
+
+    generated_img = content_img.clone().requires_grad_(True)
+    optimizer = optim.Adam([generated_img], lr=lr)
+
+    for iter in range(iters+1):
+        generated_features = model(generated_img)
+        content_features = model(content_img)
+        style_features = model(style_img)
+        
+        content_loss = 0
+        style_loss = 0
+        
+        for generated_feature, content_feature, style_feature in zip(generated_features, content_features, style_features):
+
+            batch_size, n_feature_maps, height, width = generated_feature.size()
+            
+            content_loss += (torch.mean((generated_feature - content_feature) ** 2))
+            
+            G = torch.mm((generated_feature.view(batch_size * n_feature_maps, height * width)), (generated_feature.view(batch_size * n_feature_maps, height * width)).t())
+            A = torch.mm((style_feature.view(batch_size * n_feature_maps, height * width)), (style_feature.view(batch_size * n_feature_maps, height * width)).t())
+            
+            E_l = ((G - A) ** 2)
+            w_l = 1/5
+            style_loss += torch.mean(w_l * E_l)
+            
+        total_loss = alpha * content_loss + beta * style_loss
+        optimizer.zero_grad()
+        total_loss.backward()
+        optimizer.step()
+
+        yield save_img(generated_img, original_size)
+        
+    yield save_img(generated_img, original_size)
+
+
+interface = gr.Interface(
+    fn=transfer_style, 
+    inputs=[gr.Image(type='pil')], 
+    outputs='image',
+    title="Starry Night Style Transfer",
+    api_name='style',
+    allow_flagging='never'
+).launch()