README.md

---
title: SpectraGAN
emoji: 🖼️
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
license: apache-2.0
---

# 🖼️ SpectraGAN — Multi-Model Upscaler Comparison

A Gradio web app that lets you upscale an image with **multiple SR models simultaneously** and compare results side by side.

Supported models:

| Model | Architecture | Scale |
|-------|-------------|-------|
| Real-ESRGAN ×2 | GAN (residual-in-residual dense block) | ×2 |
| Real-ESRGAN ×4 | GAN (residual-in-residual dense block) | ×4 |
| SRCNN ×4 | Shallow 3-layer CNN | ×4 |
| HResNet ×4 | Deep residual network (EDSR-style) | ×4 |
| SR3 *(stub)* | Diffusion model | ×4 — see note below |

---

## 📑 Table of Contents

1. [Features](#features)
2. [Project Structure](#project-structure)
3. [Prerequisites](#prerequisites)
4. [Installation](#installation)
5. [Adding Your ONNX Models](#adding-your-onnx-models)
6. [Running Locally](#running-locally)
7. [SR3 Integration Guide](#sr3-integration-guide)
8. [Contributing](#contributing)
9. [License](#license)

---

## ✨ Features

- **Side-by-side comparison** — run up to 4 models at once, results displayed in a 4-panel grid.
- **Selective execution** — toggle any model on/off before running; unchecked models are skipped.
- **×8 post-resize** — optionally apply a bicubic ×2 pass on top of any ×4 result.
- **Tile-based inference** — large images are split into tiles matching each model's fixed input size, then stitched back together seamlessly.
- **Per-result download** — each panel has its own PNG download button.
- **Graceful degradation** — if a model file is missing (e.g. Drive ID not yet set), that panel is skipped without crashing the others.

---

## 📁 Project Structure

```
spectragan/
├── model/
│   ├── Real-ESRGAN_x2plus.onnx   # auto-downloaded
│   ├── Real-ESRGAN-x4plus.onnx   # auto-downloaded
│   ├── SRCNN_x4.onnx              # you provide — see below
│   └── HResNet_x4.onnx            # you provide — see below
├── app.py
├── requirements.txt
└── README.md
```

---

## ⚙️ Prerequisites

- Python 3.10+
- `git`
- A terminal / command prompt

---

## 🔧 Installation

```bash
git clone https://github.com/ParamAhuja/SpectraGAN.git
cd SpectraGAN
python -m venv .venv
source .venv/bin/activate      # Linux/macOS
# .venv\Scripts\activate       # Windows
pip install -r requirements.txt
```

---

## 🗂️ Adding Your ONNX Models

The Real-ESRGAN weights are downloaded automatically from Google Drive on first run.

For **SRCNN** and **HResNet** you need to:

1. Export your trained PyTorch model to ONNX:

```python
import torch

# SRCNN example
from srcnn import SRCNN
model = SRCNN()
model.load_state_dict(torch.load("srcnn.pth"))
model.eval()

dummy = torch.randn(1, 3, 128, 128)
torch.onnx.export(
    model, dummy, "SRCNN_x4.onnx",
    input_names=["input"], output_names=["output"],
    dynamic_axes={"input": {2: "H", 3: "W"}, "output": {2: "H", 3: "W"}}
)
```

2. Upload the `.onnx` file to Google Drive and set **"Anyone with the link can view"**.

3. Copy the file ID from the share URL and update `DRIVE_IDS` in `app.py`:

```python
DRIVE_IDS = {
    ...
    "srcnn_x4":   "YOUR_SRCNN_DRIVE_FILE_ID_HERE",
    "hresnet_x4": "YOUR_HRESNET_DRIVE_FILE_ID_HERE",
}
```

---

## 🚀 Running Locally

```bash
python app.py
```

Open `http://127.0.0.1:7860` in your browser.

---

## 🌀 SR3 Integration Guide

SR3 (Super-Resolution via Repeated Refinement) is a **diffusion model** — it cannot be exported to a static ONNX graph because its inference involves a variable-length denoising loop.

To add SR3:

1. Clone the reference implementation:
   ```bash
   git clone https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
   ```

2. Place your trained checkpoint at `model/sr3_x4.pth`.

3. Add `torch` and `torchvision` to `requirements.txt`.

4. Write a wrapper in `app.py`:
   ```python
   def run_sr3(input_img: Image.Image) -> Image.Image:
       # load config + model, run the denoising loop, return result
       ...
   ```

5. Add `"sr3_x4"` to the `PANEL_KEYS` list and wire `run_sr3` into `compare_models`.

---

## 🤝 Contributing

Pull requests welcome. Please open an issue first to discuss significant changes.

---

## 📄 License

Apache 2.0 — see `LICENSE`.

---

## 👤 Author & Credits

- Real-ESRGAN by [xinntao](https://github.com/xinntao/Real-ESRGAN)
- SRCNN by Dong et al. (2014)
- HResNet / EDSR by Lim et al. (2017)
- SR3 by Ho et al. (2022) — [paper](https://arxiv.org/abs/2104.07636)