app.py

demotwotext.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from diffusers import DiffusionPipeline
+import base64
+import io
+
+# Load the pre-trained model
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-512x512", use_safetensors=True)
+
+# Create a Streamlit interface
+st.title("Image Generation Model")
+st.write("Enter a prompt for the image generation model:")
+
+# Generate the image
+prompt = st.text_input("Prompt", label_visibility="collapsed")
+if st.button("Generate Image"):
+    with st.spinner("Generating image..."):
+        # Generate the image
+        image = pipeline(prompt, num_inference_steps=20).images[0]
+
+        # Convert the image to bytes
+        buf = io.BytesIO()
+        image.save(buf, format='JPEG')
+        img_bytes = buf.getvalue()
+
+        # Display the generated image
+        st.write("Generated image:")
+        st.image(img_bytes, caption="Generated image", use_column_width=True)