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README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+  - image-to-image
+  - style-transfer
+  - pytorch
+  - beginner
+  - fast-inference
+pipeline_tag: image-to-image
+datasets:
+  - coco
+metrics:
+  - perceptual-loss
+---
+
+# 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](https://arxiv.org/abs/1603.08155).
+
+---
+
+## 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
+
+```python
+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:
+
+```bash
+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](https://arxiv.org/abs/1603.08155)
+- Gatys, L., Ecker, A., & Bethge, M. (2015). [A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)
+
+---
+
+*Built as a learning project. Feedback and suggestions welcome!*

model.py

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+"""
+mini-style-transfer — PyTorch style filter model
+Author: your-username
+HuggingFace: huggingface.co/your-username/mini-style-transfer
+
+Architecture: Feed-forward CNN (Johnson et al. 2016)
+- No slow per-image optimisation — runs in under 1 second
+- One model file per style (starry, mosaic, candy, sketch)
+"""
+
+import torch
+import torch.nn as nn
+
+
+# ── Residual Block ────────────────────────────────────────────────────────────
+# The core building block. Learns fine style details without losing content.
+
+class ResidualBlock(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.block = nn.Sequential(
+            nn.ReflectionPad2d(1),          # padding that avoids edge artifacts
+            nn.Conv2d(channels, channels, kernel_size=3),
+            nn.InstanceNorm2d(channels),    # normalise per-image (better for style)
+            nn.ReLU(inplace=True),
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(channels, channels, kernel_size=3),
+            nn.InstanceNorm2d(channels),
+        )
+
+    def forward(self, x):
+        return x + self.block(x)           # skip connection — keeps original content
+
+
+# ── StyleNet ──────────────────────────────────────────────────────────────────
+# Full model: Encoder → Residual blocks → Decoder
+# Input:  (B, 3, H, W)  — any image size
+# Output: (B, 3, H, W)  — same size, styled
+
+class StyleNet(nn.Module):
+    def __init__(self, num_residual_blocks=5):
+        super().__init__()
+
+        # Encoder: shrinks image, learns features
+        self.encoder = nn.Sequential(
+            nn.ReflectionPad2d(4),
+            nn.Conv2d(3, 32, kernel_size=9, stride=1),   # 32 feature maps
+            nn.InstanceNorm2d(32),
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),  # downsample
+            nn.InstanceNorm2d(64),
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # downsample
+            nn.InstanceNorm2d(128),
+            nn.ReLU(inplace=True),
+        )
+
+        # Residual blocks: learn style patterns at compressed resolution (fast!)
+        self.residuals = nn.Sequential(
+            *[ResidualBlock(128) for _ in range(num_residual_blocks)]
+        )
+
+        # Decoder: upscale back to original resolution with style applied
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
+            nn.InstanceNorm2d(64),
+            nn.ReLU(inplace=True),
+
+            nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
+            nn.InstanceNorm2d(32),
+            nn.ReLU(inplace=True),
+
+            nn.ReflectionPad2d(4),
+            nn.Conv2d(32, 3, kernel_size=9, stride=1),  # back to 3 colour channels
+            nn.Sigmoid(),                                 # pixel values → 0–1 range
+        )
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.residuals(x)
+        x = self.decoder(x)
+        return x
+
+
+# ── Quick test ────────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    model = StyleNet()
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"StyleNet ready — {total_params:,} parameters ({total_params/1e6:.1f}M)")
+
+    # Test with a dummy 512x512 image
+    dummy = torch.randn(1, 3, 512, 512)
+    with torch.no_grad():
+        out = model(dummy)
+    print(f"Input:  {tuple(dummy.shape)}")
+    print(f"Output: {tuple(out.shape)}")
+    print("Model works correctly!")

requirements.txt

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+torch>=2.0.0
+torchvision>=0.15.0
+Pillow>=9.0.0

run.py

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+"""
+run.py — Apply your trained style to any photo
+
+Usage:
+    python run.py --model starry_night.pth --input my_photo.jpg --output result.jpg
+    python run.py --model mosaic.pth --input my_photo.jpg --output result.jpg
+
+No GPU needed — runs on CPU in under 1 second.
+"""
+
+import torch
+from torchvision import transforms
+from PIL import Image
+import argparse
+from model import StyleNet
+
+
+def stylize(model_path, input_path, output_path):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Running on: {device}")
+
+    # Load trained model
+    model = StyleNet()
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+    model.to(device)
+
+    # Load and prepare input image
+    img = Image.open(input_path).convert("RGB")
+    original_size = img.size           # save so we can restore it at the end
+    print(f"Input image: {input_path}  ({img.width}x{img.height})")
+
+    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).to(device)    # shape: [1, 3, H, W]
+
+    # Run inference
+    with torch.no_grad():
+        output = model(tensor).squeeze(0).clamp(0, 1)  # shape: [3, H, W]
+
+    # Convert back to PIL image and save
+    to_pil = transforms.ToPILImage()
+    result = to_pil(output)
+    result = result.resize(original_size, Image.LANCZOS)  # restore original size
+    result.save(output_path, quality=95)
+
+    print(f"Styled image saved to: {output_path}")
+    print("Open the file to see your result!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model",  required=True, help="Path to your .pth model file")
+    parser.add_argument("--input",  required=True, help="Path to your input photo")
+    parser.add_argument("--output", default="output.jpg", help="Where to save the result")
+    args = parser.parse_args()
+    stylize(args.model, args.input, args.output)

train.py

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+"""
+train.py — Train your mini-style-transfer model
+
+Usage:
+    python train.py --style starry_night.jpg --output starry_night.pth
+
+What this script does:
+    1. Loads your style image (the painting)
+    2. Loops over MS-COCO images (content images — everyday photos)
+    3. For each photo: runs it through StyleNet, compares result to style
+    4. Updates model weights so outputs look more like the style painting
+    5. Saves your trained model as a .pth file
+
+Beginner tip: Think of training as teaching the model by example.
+You show it thousands of photos and say "make them look like Van Gogh".
+After enough examples, it learns to do it on its own.
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import transforms, models
+from torch.utils.data import DataLoader, Dataset
+from PIL import Image
+import os
+import argparse
+from model import StyleNet
+
+
+# ── Settings ──────────────────────────────────────────────────────────────────
+
+IMAGE_SIZE   = 256          # train on 256x256 (faster); can run inference at any size
+BATCH_SIZE   = 4
+EPOCHS       = 2            # 2 epochs is enough for a recognisable style
+LR           = 1e-3
+CONTENT_W    = 1.0          # how much to preserve original content
+STYLE_W      = 1e5          # how strongly to apply the style (very high is normal)
+DEVICE       = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+# ── Dataset ───────────────────────────────────────────────────────────────────
+
+class ImageFolderDataset(Dataset):
+    """Loads all images from a folder. Use MS-COCO train2017 images."""
+    def __init__(self, folder, transform):
+        self.paths = [
+            os.path.join(folder, f) for f in os.listdir(folder)
+            if f.lower().endswith(('.jpg', '.jpeg', '.png'))
+        ]
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.paths)
+
+    def __getitem__(self, idx):
+        img = Image.open(self.paths[idx]).convert("RGB")
+        return self.transform(img)
+
+
+# ── Perceptual Loss (VGG16) ───────────────────────────────────────────────────
+# Instead of comparing pixels directly, we compare how images "feel"
+# using a pretrained VGG network. This is what makes the style look good.
+
+class VGGLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+        vgg = models.vgg16(weights=models.VGG16_Weights.DEFAULT).features
+        # relu2_2 for content, relu1_2 + relu2_2 + relu3_3 for style
+        self.slice1 = nn.Sequential(*list(vgg)[:4]).eval()    # relu1_2
+        self.slice2 = nn.Sequential(*list(vgg)[4:9]).eval()   # relu2_2  ← content
+        self.slice3 = nn.Sequential(*list(vgg)[9:16]).eval()  # relu3_3
+        for p in self.parameters():
+            p.requires_grad = False
+
+    def forward(self, x):
+        h1 = self.slice1(x)
+        h2 = self.slice2(h1)
+        h3 = self.slice3(h2)
+        return h1, h2, h3
+
+def gram_matrix(feat):
+    """Style is captured as correlations between feature maps (Gram matrix)."""
+    B, C, H, W = feat.shape
+    feat = feat.view(B, C, H * W)
+    return torch.bmm(feat, feat.transpose(1, 2)) / (C * H * W)
+
+
+# ── Training loop ─────────────────────────────────────────────────────────────
+
+def train(style_image_path, content_folder, output_path):
+    print(f"Device: {DEVICE}")
+    print(f"Style:  {style_image_path}")
+    print(f"Output: {output_path}\n")
+
+    transform = transforms.Compose([
+        transforms.Resize(IMAGE_SIZE),
+        transforms.CenterCrop(IMAGE_SIZE),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225]),
+    ])
+
+    # Load style image and precompute its Gram matrices (done once)
+    style_img = transform(Image.open(style_image_path).convert("RGB"))
+    style_img = style_img.unsqueeze(0).to(DEVICE)
+
+    dataset   = ImageFolderDataset(content_folder, transform)
+    loader    = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
+
+    model     = StyleNet().to(DEVICE)
+    vgg       = VGGLoss().to(DEVICE)
+    optimizer = optim.Adam(model.parameters(), lr=LR)
+    mse       = nn.MSELoss()
+
+    # Precompute style Gram matrices
+    with torch.no_grad():
+        s1, s2, s3 = vgg(style_img)
+        style_grams = [gram_matrix(s1), gram_matrix(s2), gram_matrix(s3)]
+
+    print(f"Training on {len(dataset)} images for {EPOCHS} epochs...")
+    print("─" * 50)
+
+    for epoch in range(EPOCHS):
+        for i, content in enumerate(loader):
+            content = content.to(DEVICE)
+            optimizer.zero_grad()
+
+            # Forward pass
+            styled = model(content)
+
+            # Content loss — styled image should still look like the photo
+            _, c_feat, _  = vgg(content)
+            _, s_feat, _  = vgg(styled)
+            content_loss  = mse(s_feat, c_feat.detach())
+
+            # Style loss — styled image should look like the painting
+            o1, o2, o3    = vgg(styled)
+            style_loss    = (
+                mse(gram_matrix(o1), style_grams[0].expand(content.size(0), -1, -1)) +
+                mse(gram_matrix(o2), style_grams[1].expand(content.size(0), -1, -1)) +
+                mse(gram_matrix(o3), style_grams[2].expand(content.size(0), -1, -1))
+            )
+
+            loss = CONTENT_W * content_loss + STYLE_W * style_loss
+            loss.backward()
+            optimizer.step()
+
+            if i % 100 == 0:
+                print(f"Epoch {epoch+1}/{EPOCHS}  Batch {i:4d}/{len(loader)}"
+                      f"  Loss: {loss.item():.2f}"
+                      f"  (content {content_loss.item():.3f}"
+                      f"  style {style_loss.item():.2f})")
+
+    torch.save(model.state_dict(), output_path)
+    print(f"\nDone! Model saved to: {output_path}")
+    print(f"Upload to HuggingFace: huggingface-cli upload your-username/mini-style-transfer {output_path}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--style",   required=True,  help="Path to your style painting image")
+    parser.add_argument("--content", default="coco/", help="Folder of training photos (MS-COCO)")
+    parser.add_argument("--output",  default="style_model.pth", help="Output .pth file name")
+    args = parser.parse_args()
+    train(args.style, args.content, args.output)