app.py

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)