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import streamlit as st
from diffusers import StableDiffusionPipeline
import torch
from PIL import Image
# Title and description of the app
st.title("🖼️ Stable Diffusion Image Generator")
st.write("Generate images from text using the Stable Diffusion v2-1 model!")
# Sidebar for user inputs
st.sidebar.title("Input Options")
prompt = st.sidebar.text_input("Enter your prompt", "A futuristic cityscape at sunset, vibrant colors, 8k")
generate_button = st.sidebar.button("Generate Image")
# Load the pipeline when the app starts
@st.cache_resource
def load_pipeline():
# Use a smaller and more efficient model
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
revision="fp16" if torch.cuda.is_available() else None
)
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)
return pipe
pipe = load_pipeline()
# Generate image when button is clicked
if generate_button:
st.write(f"### Prompt: {prompt}")
with st.spinner("Generating image... Please wait."):
try:
# Generate the image
with torch.autocast("cuda" if torch.cuda.is_available() else "cpu"):
image = pipe(prompt).images[0]
# Display the generated image
st.image(image, caption="Generated Image", use_column_width=True)
# Option to download the image
img_path = "generated_image.png"
image.save(img_path)
with open(img_path, "rb") as img_file:
st.download_button(
label="Download Image",
data=img_file,
file_name="generated_image.png",
mime="image/png"
)
except Exception as e:
st.error(f"An error occurred: {e}") |