Upload ghibli2.py

ghibli2.py

@@ -0,0 +1,61 @@
+import streamlit as st
+from PIL import Image as PILImage
+import torch
+from io import BytesIO
+from diffusers import StableDiffusionImg2ImgPipeline
+
+# Function to load the Stable Diffusion model
+def load_model():
+    model_id = "stabilityai/stable-diffusion-2-1"
+    device = "mps" if torch.backends.mps.is_available() else "cpu"
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+        model_id, torch_dtype=torch.float32, safety_checker=None
+    ).to(device)
+    return pipe, device
+
+# Function to generate the transformed image
+def generate_image(pipe, device, init_image, prompt, strength, guidance_scale):
+    init_image = init_image.convert("RGB").resize((512, 512))
+    generator = torch.manual_seed(42)
+    output_image = pipe(
+        prompt=prompt,
+        image=init_image,
+        strength=strength,
+        guidance_scale=guidance_scale,
+        generator=generator
+    ).images[0]
+    return output_image
+
+# Streamlit App
+def main():
+    st.title("Stable Diffusion Image Transformer")
+
+    # Load the model
+    pipe, device = load_model()
+
+    # Image Upload
+    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+    if uploaded_file is not None:
+        init_image = PILImage.open(uploaded_file)
+        st.image(init_image, caption="Uploaded Image", use_column_width=True)
+
+        # Prompt Input
+        prompt = st.text_input("Enter transformation prompt:")
+
+        # Sliders for Strength and Guidance Scale
+        strength = st.slider("Select strength:", 0.1, 1.0, 0.5, 0.1)
+        guidance_scale = st.slider("Select guidance scale:", 1.0, 10.0, 7.5, 0.5)
+
+        # Generate Image Button
+        if st.button("Generate Image"):
+            if prompt:
+                with st.spinner("Generating image..."):
+                    transformed_image = generate_image(
+                        pipe, device, init_image, prompt, strength, guidance_scale
+                    )
+                st.image(transformed_image, caption="Transformed Image", use_column_width=True)
+            else:
+                st.error("Please enter a transformation prompt.")
+
+if __name__ == "__main__":
+    main()