Create app.py

app.py

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+import streamlit as st
+import torch
+import numpy as np
+from transformers import pipeline
+from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, UniPCMultistepScheduler
+from diffusers.utils import load_image, make_image_grid
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Initialize the depth estimator
+depth_estimator = pipeline("depth-estimation")
+
+# Function to load an image from a URL
+def load_image_from_url(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    return img
+
+# Function to get depth map
+def get_depth_map(image, depth_estimator):
+    image = depth_estimator(image)["depth"]
+    image = np.array(image)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    detected_map = torch.from_numpy(image).float() / 255.0
+    depth_map = detected_map.permute(2, 0, 1)
+    return depth_map
+
+# Streamlit UI
+st.title("Image Modification with ControlNet and Stable Diffusion")
+
+# User inputs
+image_url = st.text_input("Enter the URL of a farm image:", "")
+prompt = st.text_input("Enter your prompt:", "vineyard agrotourism service on the farm")
+
+if st.button("Generate"):
+    if image_url:
+        # Load the image
+        farm_image = load_image_from_url(image_url)
+        
+        # Process image for depth map
+        depth_map = get_depth_map(farm_image, depth_estimator).unsqueeze(0).half().to("cuda")
+
+        # Load the ControlNet model and the StableDiffusionControlNetImg2ImgPipeline
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal", torch_dtype=torch.float16, use_safetensors=True)
+        pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5",
+            controlnet=controlnet,
+            torch_dtype=torch.float16,
+            use_safetensors=True
+        ).to("cuda")
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_model_cpu_offload()
+
+        # Generate the image
+        output = pipe(
+            prompt,
+            image=farm_image,
+            control_image=depth_map,
+        ).images[0]
+
+        # Convert PIL images to display in Streamlit
+        st.image(farm_image, caption="Original Image")
+        st.image(output, caption="Generated Image")
+    else:
+        st.write("Please enter an image URL.")