Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from authtoken import auth_token
+import torch
+import torch.cuda.amp as amp
+from diffusers import StableDiffusionPipeline
+
+modelid = "CompVis/stable-diffusion-v1-4"
+device = torch.device("cpu")  # Default to CPU device
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+pipe = StableDiffusionPipeline.from_pretrained(modelid, use_auth_token=auth_token)
+pipe.to(device)
+
+def generate(prompt):
+    with torch.no_grad(), amp.autocast(enabled=device != torch.device("cpu")):
+        image = pipe(prompt, guidance_scale=8.5)["sample"][0]
+
+    image.save('generatedimage.png')
+    return image
+
+def predict_text(prompt):
+    image = generate(prompt)
+    return image
+
+def predict_image(input_image):
+    input_image.save('input_image.png')
+    prompt = input("Enter your prompt: ")
+    image = generate(prompt)
+    return image
+
+iface = gr.Interface(
+    fn=predict_text,
+    inputs="text",
+    outputs="image",
+    capture_session=True,
+)
+
+iface.launch(share=True)