Create app.py

app.py

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+!pip install git+https://github.com/huggingface/diffusers.git
+from PIL import Image
+import gradio as gr
+from diffusers import LDMSuperResolutionPipeline
+import torch
+import keras
+
+model_id = "CompVis/ldm-super-resolution-4x-openimages"
+
+# load model and scheduler
+pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
+#pipeline = pipeline.to(device)
+
+# let's download an  image
+#url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
+#response = requests.get(url)
+def infer(original_image):
+   #low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
+    image = keras.utils.img_to_array(original_image)
+    image = image.astype("float32") / 255.0
+    image = np.expand_dims(image, axis=0)
+# run pipeline in inference (sample random noise and denoise)
+    upscaled_image = pipeline(image, num_inference_steps=100, eta=1).images[0]
+    return upscaled_image
+# save image
+#upscaled_image.save("ldm_generated_image.png")
+iface = gr.Interface(
+    fn=infer,
+    title="Enhancement Resolution",
+    description = "OpenCV implementation of Enhancement Resolution  🌆🎆",
+    inputs=[gr.inputs.Image(label="image", type="pil")],
+    outputs="image",
+    examples=examples,
+    cache_examples=True,
+     article = "Authors: <a href=\"https://github.com/Uviveknarayan\">Vivek Narayan</a>, <a href=\"https://github.com/chiranjan-7\">Chiranjan</a>,<a href=\"https://github.com/GangaSrujan\">Srujan</a>,<a href=\"https://github.com/RohanPawar3399\">Rohan Pawar</a>,<a href=\"https://github.com/pavankarthik77\">Pavan Karthik</a>").launch(enable_queue=True)