# Real-ESRGAN Upscaling

Upscale and enhance images using Real-ESRGAN on GPU.

{% hint style="success" %}
All examples can be run on GPU servers rented through [CLORE.AI Marketplace](https://clore.ai/marketplace).
{% endhint %}

## Renting on CLORE.AI

1. Visit [CLORE.AI Marketplace](https://clore.ai/marketplace)
2. Filter by GPU type, VRAM, and price
3. Choose **On-Demand** (fixed rate) or **Spot** (bid price)
4. Configure your order:
   * Select Docker image
   * Set ports (TCP for SSH, HTTP for web UIs)
   * Add environment variables if needed
   * Enter startup command
5. Select payment: **CLORE**, **BTC**, or **USDT/USDC**
6. Create order and wait for deployment

### Access Your Server

* Find connection details in **My Orders**
* Web interfaces: Use the HTTP port URL
* SSH: `ssh -p <port> root@<proxy-address>`

## What is Real-ESRGAN?

Real-ESRGAN is a practical image restoration model that:

* Upscales images 2x-4x
* Removes noise and artifacts
* Enhances details
* Works on photos, anime, and art

## Model Variants

| Model                         | Best For        | Speed   |
| ----------------------------- | --------------- | ------- |
| RealESRGAN\_x4plus            | General photos  | Medium  |
| RealESRGAN\_x4plus\_anime\_6B | Anime/drawings  | Medium  |
| RealESRGAN\_x2plus            | 2x upscale      | Fast    |
| RealESRNet\_x4plus            | Fast processing | Fastest |

## Quick Deploy

**Docker Image:**

```
pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
```

**Ports:**

```
22/tcp
7860/http
```

**Command:**

```bash
pip install realesrgan gradio && \
python -c "
import gradio as gr
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
import numpy as np
from PIL import Image
import torch

# Load model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(
    scale=4,
    model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
    model=model,
    tile=0,
    tile_pad=10,
    pre_pad=0,
    half=True
)

def upscale(image, scale):
    img = np.array(image)
    output, _ = upsampler.enhance(img, outscale=scale)
    return Image.fromarray(output)

demo = gr.Interface(
    fn=upscale,
    inputs=[gr.Image(type='pil'), gr.Slider(2, 4, value=4, step=1, label='Scale')],
    outputs=gr.Image(type='pil'),
    title='Real-ESRGAN Upscaler'
)
demo.launch(server_name='0.0.0.0', server_port=7860)
"
```

## Accessing Your Service

After deployment, find your `http_pub` URL in **My Orders**:

1. Go to **My Orders** page
2. Click on your order
3. Find the `http_pub` URL (e.g., `abc123.clorecloud.net`)

Use `https://YOUR_HTTP_PUB_URL` instead of `localhost` in examples below.

## CLI Usage

### Installation

```bash
pip install realesrgan
```

### Basic Upscaling

```bash

# Upscale single image
python -m realesrgan -i input.jpg -o output.jpg -n RealESRGAN_x4plus -s 4

# Upscale folder
python -m realesrgan -i ./inputs -o ./outputs -n RealESRGAN_x4plus -s 4
```

### Options

```bash
python -m realesrgan \
    -i input.jpg \           # Input
    -o output.jpg \          # Output
    -n RealESRGAN_x4plus \   # Model name
    -s 4 \                   # Scale factor
    --face_enhance \         # Enable face enhancement
    --fp32 \                 # Use FP32 (more VRAM, better quality)
    --tile 400               # Tile size for large images
```

## Python API

### Basic Usage

```python
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
import cv2

# Setup model
model = RRDBNet(
    num_in_ch=3,
    num_out_ch=3,
    num_feat=64,
    num_block=23,
    num_grow_ch=32,
    scale=4
)

upsampler = RealESRGANer(
    scale=4,
    model_path='RealESRGAN_x4plus.pth',
    model=model,
    tile=0,
    tile_pad=10,
    pre_pad=0,
    half=True  # Use FP16
)

# Upscale
img = cv2.imread('input.jpg', cv2.IMREAD_UNCHANGED)
output, _ = upsampler.enhance(img, outscale=4)
cv2.imwrite('output.jpg', output)
```

### With Face Enhancement

```python
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
from gfpgan import GFPGANer
import cv2

# Real-ESRGAN model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth', model=model, half=True)

# GFPGAN for faces
face_enhancer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=4,
    arch='clean',
    channel_multiplier=2,
    bg_upsampler=upsampler
)

# Process
img = cv2.imread('portrait.jpg')
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
cv2.imwrite('output.jpg', output)
```

### Anime Upscaling

```python
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
import cv2

# Anime model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)

upsampler = RealESRGANer(
    scale=4,
    model_path='RealESRGAN_x4plus_anime_6B.pth',
    model=model,
    half=True
)

img = cv2.imread('anime.png')
output, _ = upsampler.enhance(img, outscale=4)
cv2.imwrite('anime_upscaled.png', output)
```

## Batch Processing

### Process Folder

```python
import os
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
import cv2
from tqdm import tqdm

# Setup
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth', model=model, half=True)

input_dir = './inputs'
output_dir = './outputs'
os.makedirs(output_dir, exist_ok=True)

# Process all images
for filename in tqdm(os.listdir(input_dir)):
    if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
        img_path = os.path.join(input_dir, filename)
        img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)

        output, _ = upsampler.enhance(img, outscale=4)

        output_path = os.path.join(output_dir, f"upscaled_{filename}")
        cv2.imwrite(output_path, output)
```

### Shell Script

```bash
#!/bin/bash
INPUT_DIR=$1
OUTPUT_DIR=$2

mkdir -p $OUTPUT_DIR

for file in $INPUT_DIR/*.{jpg,png,jpeg}; do
    if [ -f "$file" ]; then
        filename=$(basename "$file")
        python -m realesrgan -i "$file" -o "$OUTPUT_DIR/upscaled_$filename" -n RealESRGAN_x4plus -s 4
        echo "Processed: $filename"
    fi
done
```

## Tiled Processing (Large Images)

For images that don't fit in VRAM:

```python
upsampler = RealESRGANer(
    scale=4,
    model_path='RealESRGAN_x4plus.pth',
    model=model,
    tile=400,      # Process in 400px tiles
    tile_pad=10,   # Overlap between tiles
    pre_pad=0,
    half=True
)
```

### Tile Size Recommendations

| VRAM | Max Tile Size |
| ---- | ------------- |
| 4GB  | 200           |
| 6GB  | 300           |
| 8GB  | 400           |
| 12GB | 600           |
| 24GB | 0 (no tiling) |

## Video Upscaling

### Using Real-ESRGAN

```python
import cv2
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
from tqdm import tqdm

# Setup model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth', model=model, tile=400, half=True)

# Open video
cap = cv2.VideoCapture('input.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) * 4
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) * 4
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

# Output
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('output.mp4', fourcc, fps, (width, height))

# Process frames
for _ in tqdm(range(total_frames)):
    ret, frame = cap.read()
    if not ret:
        break

    output, _ = upsampler.enhance(frame, outscale=4)
    out.write(output)

cap.release()
out.release()
```

### FFmpeg Pipeline

```bash

# Extract frames
ffmpeg -i input.mp4 -qscale:v 1 frames/frame_%06d.png

# Upscale frames
python -m realesrgan -i frames -o upscaled -n RealESRGAN_x4plus -s 4

# Reassemble video
ffmpeg -framerate 30 -i upscaled/frame_%06d.png -c:v libx264 -pix_fmt yuv420p output.mp4

# Add audio back
ffmpeg -i output.mp4 -i input.mp4 -c:v copy -c:a aac -map 0:v:0 -map 1:a:0 final.mp4
```

## API Server

### FastAPI Server

```python
from fastapi import FastAPI, UploadFile
from fastapi.responses import Response
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
import cv2
import numpy as np
import io

app = FastAPI()

# Load model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth', model=model, tile=400, half=True)

@app.post("/upscale")
async def upscale(file: UploadFile, scale: int = 4):
    contents = await file.read()
    nparr = np.frombuffer(contents, np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)

    output, _ = upsampler.enhance(img, outscale=scale)

    _, encoded = cv2.imencode('.png', output)
    return Response(content=encoded.tobytes(), media_type="image/png")

# Run: uvicorn server:app --host 0.0.0.0 --port 8000
```

### Usage

```bash
curl -X POST "http://localhost:8000/upscale?scale=4" \
    -F "file=@input.jpg" \
    --output upscaled.png
```

## Model Comparison

| Model             | Quality | Speed   | VRAM | Best For |
| ----------------- | ------- | ------- | ---- | -------- |
| x4plus            | Best    | Slow    | 4GB+ | Photos   |
| x4plus\_anime\_6B | Best    | Medium  | 3GB+ | Anime    |
| x2plus            | Good    | Fast    | 2GB+ | Quick 2x |
| RealESRNet        | OK      | Fastest | 2GB+ | Previews |

## Performance

| Image Size | GPU      | 4x Upscale Time |
| ---------- | -------- | --------------- |
| 512x512    | RTX 3090 | \~0.5s          |
| 1024x1024  | RTX 3090 | \~1.5s          |
| 2048x2048  | RTX 3090 | \~5s            |
| 512x512    | RTX 4090 | \~0.3s          |

## Troubleshooting

### CUDA Out of Memory

```python

# Use tiling
upsampler = RealESRGANer(..., tile=200, ...)

# Or reduce scale
output, _ = upsampler.enhance(img, outscale=2)  # Instead of 4
```

### Artifacts in Output

* Use smaller tile size with more overlap
* Try different model (anime vs photo)
* Check input image quality

### Slow Processing

* Enable FP16: `half=True`
* Increase tile size if VRAM allows
* Use faster model: RealESRNet

## Download Results

```bash
scp -P <port> -r root@<proxy>:/workspace/outputs/ ./upscaled_images/
```

## Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

| GPU       | Hourly Rate | Daily Rate | 4-Hour Session |
| --------- | ----------- | ---------- | -------------- |
| RTX 3060  | \~$0.03     | \~$0.70    | \~$0.12        |
| RTX 3090  | \~$0.06     | \~$1.50    | \~$0.25        |
| RTX 4090  | \~$0.10     | \~$2.30    | \~$0.40        |
| A100 40GB | \~$0.17     | \~$4.00    | \~$0.70        |
| A100 80GB | \~$0.25     | \~$6.00    | \~$1.00        |

*Prices vary by provider and demand. Check* [*CLORE.AI Marketplace*](https://clore.ai/marketplace) *for current rates.*

**Save money:**

* Use **Spot** market for flexible workloads (often 30-50% cheaper)
* Pay with **CLORE** tokens
* Compare prices across different providers

## Next Steps

* [GFPGAN Face Restoration](https://docs.clore.ai/guides/image-processing/gfpgan-face-restore)
* [AI Video Generation](https://docs.clore.ai/guides/video-generation/ai-video-generation)
* Stable Diffusion WebUI