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| 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) | |