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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()
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