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import streamlit as st |
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from diffusers import StableDiffusionXLPipeline |
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import torch |
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from io import BytesIO |
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st.set_page_config(page_title="Dreamscape Visualizer (HD)") |
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st.title("🌌 Dreamscape Visualizer – High Quality") |
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@st.cache_resource |
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def load_pipeline(): |
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pipe = StableDiffusionXLPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
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variant="fp16" if torch.cuda.is_available() else None, |
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use_safetensors=True |
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) |
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pipe.to("cuda" if torch.cuda.is_available() else "cpu") |
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return pipe |
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pipe = load_pipeline() |
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style_modifiers = { |
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"Fantasy": "fantasy dream, ethereal, colorful", |
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"Nightmare": "dark horror dream, creepy surreal", |
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"Lucid": "hyperreal dream, bright, vivid", |
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"Sci-Fi": "futuristic city, neon dream", |
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"Mythical": "mythical world, god-like, celestial" |
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} |
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prompt = st.text_area("Describe your dream:") |
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style = st.selectbox("Choose a dream style:", list(style_modifiers.keys())) |
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if st.button("Generate Dream Image"): |
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if not prompt.strip(): |
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st.warning("Please describe your dream first!") |
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else: |
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with st.spinner("Generating your high-res dream..."): |
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final_prompt = f"{prompt}, {style_modifiers[style]}" |
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result = pipe(prompt=final_prompt, guidance_scale=7.5, num_inference_steps=30) |
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image = result.images[0] |
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st.image(image, caption="✨ Your Dream Visualized in HD", use_column_width=True) |
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buf = BytesIO() |
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image.save(buf, format="PNG") |
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byte_im = buf.getvalue() |
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st.download_button( |
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label="📥 Download Dream Image", |
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data=byte_im, |
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file_name="dreamscape_hd.png", |
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mime="image/png" |
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) |
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