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!")
+