Update app.py

app.py

@@ -1,4 +1,36 @@
 import streamlit as st
+import torch
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import make_image_grid, load_image
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Streamlit UI
+st.title("Stable Diffusion Image-to-Image & Slider Demo")
+
+# Slider input
+x = st.slider('Select a value', 1, 100, 10)
+st.write(x, 'squared is', x * x)
+
+# Image generation pipeline
+@st.cache_resource()
+def load_pipeline():
+    pipeline = AutoPipelineForImage2Image.from_pretrained(
+        "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    )
+    pipeline.enable_model_cpu_offload()
+    pipeline.enable_xformers_memory_efficient_attention()
+    return pipeline
+
+pipeline = load_pipeline()
+
+# Image processing
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"
+init_image = load_image(url)
+
+prompt = st.text_input("Enter a prompt", "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k")
+
+generate_button = st.button("Generate Image")
+
+if generate_button:
+    with st.spinner("Generating..."):
+        image = pipeline(prompt, image=init_image).images[0]
+        st.image(make_image_grid([init_image, image], rows=1, cols=2), caption=["Original", "Generated"], use_column_width=True)