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from diffusers import DiffusionPipeline
import torch
import streamlit as st
# Model paths for Hugging Face
sdxl_base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
sdxl_refiner_model_path = "stabilityai/stable-diffusion-xl-refiner-1.0"
@st.cache_resource
def load_pipeline():
device = "cuda" if torch.cuda.is_available() else "cpu"
try:
# Load the base pipeline
pipe = DiffusionPipeline.from_pretrained(
sdxl_base_model_path,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
)
pipe.to(device)
return pipe
except ValueError as e:
st.error(f"Error loading the pipeline: {str(e)}")
return None
def image_generation(pipe, prompt):
if not pipe:
return None
try:
# Generate the image
image = pipe(
prompt=prompt,
negative_prompt="blurred, ugly, watermark, low resolution",
num_inference_steps=20,
guidance_scale=9.0
).images[0]
return image
except Exception as e:
st.error(f"Error generating image: {str(e)}")
return None
# Streamlit app interface
st.title("Project 11: Image Generation using SD XL")
prompt = st.text_input("Enter your prompt", value="A futuristic superhero cat")
pipeline = load_pipeline()
if pipeline and st.button("Generate Image"):
with st.spinner("Generating your Image..."):
image = image_generation(pipeline, prompt)
if image:
st.image(image)
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