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| from diffusers import StableDiffusionPipeline | |
| import torch | |
| from PIL import Image | |
| import streamlit as st | |
| # Load the Stable Diffusion pipeline | |
| def load_pipeline(): | |
| pipeline = StableDiffusionPipeline.from_pretrained( | |
| "CompVis/stable-diffusion-v1-4", | |
| torch_dtype=torch.float32 # Use float32 for CPU support | |
| ) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipeline.to(device) | |
| return pipeline | |
| def main(): | |
| st.title("Stable Diffusion Image Generator") | |
| st.write("Generate images from text prompts using Stable Diffusion") | |
| # Initialize the pipeline | |
| pipeline = load_pipeline() | |
| # Text input for the prompt | |
| prompt = st.text_input("Enter your text prompt", "") | |
| # Generate button | |
| if st.button("Generate"): | |
| if not prompt: | |
| st.warning("Please enter a prompt first.") | |
| return # ✅ Fixed indentation | |
| st.write("Generating your image...") | |
| with torch.no_grad(): | |
| result = pipeline(prompt) | |
| image = result.images[0] # Extract the generated image | |
| st.write("Generated Image:") | |
| st.image(image, use_column_width=True) | |
| if __name__ == "__main__": | |
| main() | |