create app.py

app.py

@@ -0,0 +1,45 @@
+import streamlit as st
+from diffusers import StableDiffusionInpaintPipeline
+from PIL import Image
+import torch
+
+# Set up title and description
+st.title("Insert Yourself Into Historical Photos with Stable Diffusion!")
+st.write("Upload a historical photo and a mask, then describe how you'd like to place yourself into the scene.")
+
+# Load model (for inpainting)
+@st.cache_resource
+def load_pipeline():
+    pipe = StableDiffusionInpaintPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-2-inpainting",
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    )
+    pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+    return pipe
+
+pipe = load_pipeline()
+
+# File uploads for base image and mask
+base_image = st.file_uploader("Upload the Historical Photo", type=["jpg", "jpeg", "png"])
+mask_image = st.file_uploader("Upload the Mask (Black & White Image)", type=["jpg", "jpeg", "png"])
+
+# Prompt input
+prompt = st.text_input("Describe the scene. How do you want to place yourself?")
+
+if base_image and mask_image and prompt:
+    # Display input images
+    st.image(base_image, caption="Historical Photo", use_column_width=True)
+    st.image(mask_image, caption="Mask", use_column_width=True)
+
+    # Load images using PIL
+    base_image = Image.open(base_image).convert("RGB")
+    mask_image = Image.open(mask_image).convert("RGB")
+
+    # Generate inpainting
+    st.write("Generating the image... This may take a moment.")
+    result = pipe(prompt=prompt, image=base_image, mask_image=mask_image).images[0]
+
+    # Display result
+    st.image(result, caption="Inpainted Image", use_column_width=True)
+    st.write("Right-click on the image to download!")
+