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import streamlit as st
from diffusers import AutoPipelineForText2Image
import torch
from io import BytesIO
st.set_page_config(page_title="Dreamscape Visualizer (Turbo)")
st.title("🌌 Dreamscape Visualizer")
@st.cache_resource
def load_pipeline():
pipe = AutoPipelineForText2Image.from_pretrained(
"stabilityai/sdxl-turbo",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
variant="fp16" if torch.cuda.is_available() else None
)
pipe.to("cuda" if torch.cuda.is_available() else "cpu")
return pipe
pipe = load_pipeline()
style_modifiers = {
"Fantasy": "fantasy dream, ethereal, colorful",
"Nightmare": "dark horror dream, creepy surreal",
"Lucid": "hyperreal dream, bright, vivid",
"Sci-Fi": "futuristic city, neon dream",
"Mythical": "mythical world, god-like, celestial"
}
prompt = st.text_area("Describe your dream:")
style = st.selectbox("Choose a dream style:", list(style_modifiers.keys()))
if st.button("Generate Dream Image"):
if not prompt.strip():
st.warning("Please describe your dream first!")
else:
with st.spinner("Dreaming up your world..."):
final_prompt = f"{prompt}, {style_modifiers[style]}"
result = pipe(prompt=final_prompt, guidance_scale=0.0, num_inference_steps=2)
image = result.images[0]
st.image(image, caption="✨ Your Dream Visualized", use_column_width=True)
# Convert image to bytes for download
buf = BytesIO()
image.save(buf, format="PNG")
byte_im = buf.getvalue()
st.download_button(
label="📥 Download Dream Image",
data=byte_im,
file_name="dreamscape.png",
mime="image/png"
)
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