Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+from PIL import Image
+import torch
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
+import io
+
+@st.cache_resource
+def load_model():
+    controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sdxl_qrcode_monster")
+    pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0",
+        controlnet=controlnet,
+        torch_dtype=torch.float16
+    )
+    return pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
+
+pipeline = load_model()
+
+st.title("QR Code Image Generator")
+
+uploaded_file = st.file_uploader("Choose a QR code image", type=["png", "jpg", "jpeg"])
+prompt = st.text_input("Enter your prompt")
+num_inference_steps = st.slider("Number of inference steps", 1, 100, 30)
+guidance_scale = st.slider("Guidance scale", 1.0, 20.0, 7.5)
+
+if uploaded_file is not None and prompt:
+    qr_image = Image.open(uploaded_file).convert("RGB")
+    
+    if st.button("Generate Image"):
+        with st.spinner("Generating image..."):
+            image = pipeline(
+                prompt,
+                image=qr_image,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale
+            ).images[0]
+            
+            st.image(image, caption="Generated Image")
+            
+            # Option to download the generated image
+            buf = io.BytesIO()
+            image.save(buf, format="PNG")
+            btn = st.download_button(
+                label="Download image",
+                data=buf.getvalue(),
+                file_name="generated_image.png",
+                mime="image/png"
+            )