Create app.py

app.py

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+import torch
+import gradio as gr
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from transformers import CLIPImageProcessor
+from PIL import Image
+import numpy as np
+
+def load_controlnet_model():
+    """Load Stable Diffusion ControlNet pipeline with IP-Adapter."""
+    controlnet = ControlNetModel.from_pretrained(
+        "lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16
+    )
+    pipe = StableDiffusionControlNetPipeline.from_pretrained(
+        "runwayml/stable-diffusion-v1-5",
+        controlnet=controlnet,
+        torch_dtype=torch.float16
+    ).to("cuda")
+    return pipe
+
+def preprocess_image(image):
+    """Convert image to depth map for ControlNet input."""
+    image = image.convert("L")  # Convert to grayscale
+    image = np.array(image)
+    depth_map = np.clip(image, 0, 255)
+    return Image.fromarray(depth_map)
+
+def generate_image(prompt, input_image):
+    """Generate an image using Stable Diffusion ControlNet with IP-Adapter."""
+    pipe = load_controlnet_model()
+    processed_image = preprocess_image(input_image)
+    result = pipe(prompt, image=processed_image, num_inference_steps=50).images[0]
+    return result
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Enter your prompt"),
+        gr.Image(type="pil", label="Upload reference image")
+    ],
+    outputs=gr.Image(type="pil", label="Generated Image"),
+    title="Stable Diffusion with ControlNet and IP-Adapter",
+    description="Generate images with precise object placement and consistent style using ControlNet and IP-Adapter.",
+)
+
+demo.launch(share=True)