---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- real_time_neural_style_transfer
- style_transfer
- fast_neural_style_transfer
---

### Apply picasso art to any image : [https://real-time-nst-app.streamlit.app/]

This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [https://github.com/HajarHAMDOUCH01/Fast-neural-style-transfer]
- Paper: [https://arxiv.org/abs/2008.03706]

This is a PyTorch implementation of the paper : Perceptual Losses for Real-Time Style Transfer
 and Super-Resolution , it applies a picasso art style on any chosen content image, it takes a second in CPU and less than a second in GPU.

``` python
import torch
from huggingface_hub import hf_hub_download
import sys, os
import torchvision.transforms as transforms
from PIL import Image

# Download model file
model_file = hf_hub_download(
    repo_id="hajar001/fast-neural-style-transfer",
    filename="style_transfer_model.py"
)
sys.path.insert(0, os.path.dirname(model_file))
from style_transfer_model import StyleTransferNet
transform = transforms.Compose([
    transforms.Resize((256, 256)),  
    transforms.ToTensor(),
])

# Load model
model = StyleTransferNet.from_pretrained("hajar001/fast-neural-style-transfer")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()

with torch.no_grad():
    # Load and preprocess test image
    test_image = Image.open("/content/content_image.jpg").convert("RGB")
    test_tensor = transform(test_image).unsqueeze(0).to(device)
    
    # Generate stylized image
    stylized_tensor = model(test_tensor)
    
    # Denormalize and convert to PIL
    # Reverse the normalization
    denorm = transforms.Normalize(
        mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
        std=[1/0.229, 1/0.224, 1/0.225]
    )
    stylized_tensor = denorm(stylized_tensor[0])
    stylized_tensor = torch.clamp(stylized_tensor, 0, 1)
    
    # Convert to PIL and save
    stylized_img = transforms.ToPILImage()(stylized_tensor.cpu())
    stylized_img.save("/content/stylized_image.jpg")
```