Create app.py

app.py

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+import gradio as gr
+import torch
+from torchvision.transforms import ToPILImage, ToTensor
+from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionUpscalePipeline
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Define the models
+model_2x = "stabilityai/sd-x2-latent-upscaler"
+model_4x = "stabilityai/stable-diffusion-x4-upscaler"
+# Load the models
+sd_2_0_2x = .from_pretrained(model_2x, torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionLatentUpscalePipeline.from_pretrained(model_2x)
+sd_2_1_4x = StableDiffusionUpscalePipeline.from_pretrained(model_4x, torch_dtype=torch.float16, revision="fp16") if torch.cuda.is_available() else StableDiffusionUpscalePipeline.from_pretrained(model_4x)
+
+# Define the input and output components for the Gradio interface
+input_image = gr.inputs.Image()
+output_image = gr.outputs.Image()
+
+# Define the function that will be called when the interface is used
+def upscale_image(model, image):
+    # Convert the image to a PyTorch tensor
+    image_tensor = ToTensor()(image)
+    
+    # Upscale the image using the selected model
+    if model == "SD 2.0 2x Latent Upscaler":
+        upscaled_tensor = sd_2_0_2x(image_tensor)
+    else:
+        upscaled_tensor = sd_2_1_4x(image_tensor)
+    
+    # Convert the upscaled tensor back to a PIL image
+    upscaled_image = ToPILImage()(upscaled_tensor)
+    
+    # Return the upscaled image
+    return upscaled_image
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=upscale_image,
+    inputs=["radio", input_image],
+    outputs=output_image,
+    radio_options=["SD 2.0 2x Latent Upscaler", "SD 2.1 4x Upscaler"],
+    title="Image Upscaler",
+    description="Upscale an image using either the SD 2.0 2x Latent Upscaler or the SD 2.1 4x Upscaler."
+)
+
+# Launch the interface
+iface.launch(debug=True)