Create app.py

app.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+import random
+import torch
+from PIL import Image, ImageOps
+from diffusers import StableDiffusionInstructPix2PixPipeline
+import streamlit as st
+
+# Load model
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+    "timbrooks/instruct-pix2pix", 
+    torch_dtype=torch.float16, safety_checker=None
+).to("cpu")
+
+# Main app
+def main():
+    st.title("InstructPix2Pix Image Editing")
+
+    uploaded_image = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
+    
+    if not uploaded_image:
+        st.warning("Please upload an image to proceed.")
+        return
+
+    input_image = Image.open(uploaded_image).convert("RGB")
+    st.image(input_image, caption="Uploaded Image", width=512)
+
+    instruction = st.text_input("Enter instruction", "Make it a painting")
+    
+    steps = st.slider("Steps", 20, 100, 50)
+    randomize_seed = st.checkbox("Randomize Seed", True)
+    seed = st.number_input("Seed", 0, value=random.randint(0, 10000), disabled=randomize_seed)
+    
+    text_cfg_scale = st.slider("Text CFG", 1.0, 10.0, 7.5)
+    image_cfg_scale = st.slider("Image CFG", 0.5, 2.0, 1.5)
+
+    if st.button("Generate"):
+        result_image = generate(input_image, instruction, steps, randomize_seed, seed, text_cfg_scale, image_cfg_scale)
+        st.image(result_image, caption="Edited Image", width=512)
+        st.download_button("Download", data=result_image.tobytes(), file_name="edited_image.png", mime="image/png")
+
+# Generate image
+def generate(input_image, instruction, steps, randomize_seed, seed, text_cfg_scale, image_cfg_scale):
+    if randomize_seed:
+        seed = random.randint(0, 100000)
+    
+    input_image = ImageOps.fit(input_image, (512, 512), method=Image.Resampling.LANCZOS)
+    
+    generator = torch.manual_seed(seed)
+    edited_image = pipe(
+        instruction, image=input_image, guidance_scale=text_cfg_scale,
+        image_guidance_scale=image_cfg_scale, num_inference_steps=steps,
+        generator=generator
+    ).images[0]
+
+    return edited_image
+
+if __name__ == "__main__":
+    main()