Create app.py

app.py

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+import torch
+from PIL import Image
+from RealESRGAN import RealESRGAN
+import gradio as gr
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_scales = {'2x': 2, '4x': 4, '8x': 8}
+
+# Load RealESRGAN models for different scales
+models = {scale: RealESRGAN(device, scale=scale) for scale in model_scales.values()}
+
+def inference(images, scale):
+    results = []
+    
+    if images is None or len(images) == 0:
+        raise gr.Error("No image uploaded. Please upload at least one image.")
+    
+    for image in images:
+        width, height = image.size
+        if width >= 5000 or height >= 5000:
+            raise gr.Error("The image is too large.")
+    
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        
+        # Select the appropriate model based on the chosen scale
+        model = models[model_scales[scale]]
+        result = model.predict(image.convert('RGB'))
+        print(f"Image size ({device}): {scale} ... OK")
+        results.append(result)
+    
+    return results
+
+title = "Advanced Real ESRGAN UpScale: 2x 4x 8x"
+description = (
+    "This advanced demo for Real-ESRGAN allows you to upscale multiple images "
+    "with different models and resolutions. Choose the scale and upload images for high-resolution enhancement."
+)
+article = (
+    "<div style='text-align: center;'>Twitter "
+    "<a href='https://twitter.com/DoEvent' target='_blank'>Max Skobeev</a> | "
+    "<a href='https://huggingface.co/sberbank-ai/Real-ESRGAN' target='_blank'>Model card</a></div>"
+)
+
+gr.Interface(
+    inference,
+    [
+        gr.Image(type="pil", label="Upload Image", multiple=True),
+        gr.Radio(
+            list(model_scales.keys()),
+            type="value",
+            value='2x',
+            label='Resolution model',
+        ),
+    ],
+    gr.Image(type="pil", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[['groot.jpeg', '2x']],
+    allow_flagging='never',
+    cache_examples=False,
+).launch()