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Create app.py
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app.py
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from diffusers import DiffusionPipeline
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import torch
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import streamlit as st
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sdxl_base_model_path = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
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sdxl_refiner_model_path = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
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@st.cache_resource
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def load_pipeline():
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device = "cuda" if torch.cuda.is_available() else "cpu"
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try:
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# Load the base pipeline
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pipe = DiffusionPipeline.from_pretrained(
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sdxl_base_model_path,
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torch_dtype=torch.float16 if device == "cuda" else torch.float32
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)
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pipe.to(device)
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return pipe
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except ValueError as e:
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st.error(f"Error loading the pipeline: {str(e)}")
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return None
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def image_generation(pipe, prompt):
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if not pipe:
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return None
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try:
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# Generate the image
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image = pipe(
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prompt=prompt,
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negative_prompt="blurred, ugly, watermark, low resolution",
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num_inference_steps=40,
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guidance_scale=9.0
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).images[0]
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return image
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except Exception as e:
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st.error(f"Error generating image: {str(e)}")
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return None
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# Streamlit app interface
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st.title("Project 11: Image Generation using SD XL")
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prompt = st.text_input("Enter your prompt", value="A futuristic superhero cat")
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pipeline = load_pipeline()
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if pipeline and st.button("Generate Image"):
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with st.spinner("Generating your Image..."):
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image = image_generation(pipeline, prompt)
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if image:
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st.image(image)
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