mini-style-transfer

A small, fast artistic style transfer model built with PyTorch as a learning project.
Applies 4 artistic styles to any photo in under 1 second on CPU.

Based on Johnson et al. (2016) — Perceptual Losses for Real-Time Style Transfer.

---

What it does

Input photo	+ Style painting	→ Output
Any photo (any size)	Starry Night / Mosaic / Candy / Sketch	Stylised version

---

Styles available

File	Style
starry_night.pth	Van Gogh — Starry Night
mosaic.pth	Classic mosaic tile pattern
candy.pth	Bright candy colours
sketch.pth	Pencil sketch look

---

Quick start

import torch
from torchvision import transforms
from PIL import Image
from model import StyleNet

# 1. Load model
model = StyleNet()
model.load_state_dict(torch.load("starry_night.pth", map_location="cpu"))
model.eval()

# 2. Prepare your image
img = Image.open("my_photo.jpg").convert("RGB")
to_tensor = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225]),
])
tensor = to_tensor(img).unsqueeze(0)

# 3. Run inference
with torch.no_grad():
    output = model(tensor).squeeze(0).clamp(0, 1)

# 4. Save result
result = transforms.ToPILImage()(output)
result.save("styled_output.jpg")
print("Done! Open styled_output.jpg")

Or use the included run.py script:

python run.py --model starry_night.pth --input my_photo.jpg --output result.jpg

---

Model details

Property	Value
Architecture	Feed-forward CNN (Encoder → 5× ResBlock → Decoder)
Parameters	~450K
Model size	~1.7 MB per style
Input	Any RGB image, any resolution
Output	Same size as input, styled
Framework	PyTorch 2.x
Normalisation	ImageNet mean/std

---

Training details

Property	Value
Content dataset	MS-COCO train2017 (subset)
Style images	4 artwork images
Epochs	2 per style
Batch size	4
Image size (training)	256 × 256
Optimizer	Adam, lr=1e-3
Loss	Perceptual (VGG16) — content + style
Content weight	1.0
Style weight	1e5
Training time	~45 min per style (GPU)

---

Repository structure

mini-style-transfer/
├── model.py            ← StyleNet architecture
├── train.py            ← Training script
├── run.py              ← Inference script
├── starry_night.pth    ← Trained weights (starry night style)
├── mosaic.pth          ← Trained weights (mosaic style)
├── candy.pth           ← Trained weights (candy style)
├── sketch.pth          ← Trained weights (sketch style)
└── README.md           ← This file

---

Limitations

• Each style is a separate model file — there is no single multi-style model yet
• Works best on natural photos (landscapes, portraits, cities)
• Cartoons, diagrams, and text-heavy images may give unexpected results
• Training images were 256×256; very high-resolution outputs may look slightly blurry
• Not suitable for commercial use without further evaluation

---

What I learned building this

• How convolutional encoders and decoders work together
• What Instance Normalisation does vs Batch Normalisation
• How Gram matrices capture texture and style
• What perceptual loss is and why pixel-level loss looks bad for style transfer
• How to use a pretrained VGG network as a feature extractor without training it

---

References

• Johnson, J., Alahi, A., & Fei-Fei, L. (2016). Perceptual Losses for Real-Time Style Transfer and Super-Resolution
• Gatys, L., Ecker, A., & Bethge, M. (2015). A Neural Algorithm of Artistic Style

---

Built as a learning project. Feedback and suggestions welcome!