Update app.py
Browse files
app.py
CHANGED
@@ -1,5 +1,6 @@
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import streamlit as st
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from diffusers import DiffusionPipeline
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from PIL import Image
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import torch
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@@ -7,7 +8,16 @@ import torch
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@st.cache_resource
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def load_pipeline():
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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return pipe
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pipe = load_pipeline()
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@@ -16,17 +26,23 @@ pipe = load_pipeline()
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st.title("Text-to-Image Generation App")
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# User input for prompt
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user_prompt = st.text_input(
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# Button to generate the image
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if st.button("Generate Image"):
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if user_prompt:
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with st.spinner("Generating image..."):
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else:
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st.error("Please enter a valid prompt.")
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-
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import streamlit as st
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from diffusers import DiffusionPipeline
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from peft import PeftModel # Import PEFT model loader
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from PIL import Image
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import torch
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@st.cache_resource
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def load_pipeline():
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
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# Ensure PEFT is available before loading LoRA weights
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try:
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pipe.load_lora_weights("Melonie/text_to_image_finetuned")
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except ValueError as e:
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st.error("PEFT backend is required but not properly set up.")
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raise e
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if torch.cuda.is_available():
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pipe.to("cuda") # Move pipeline to GPU if available
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return pipe
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pipe = load_pipeline()
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st.title("Text-to-Image Generation App")
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# User input for prompt
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user_prompt = st.text_input(
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"Enter your image prompt",
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value="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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help="Provide a detailed description of the image you'd like to generate."
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)
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# Button to generate the image
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if st.button("Generate Image"):
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if user_prompt:
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with st.spinner("Generating image..."):
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try:
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# Generate the image
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image = pipe(user_prompt).images[0]
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# Display the generated image
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st.image(image, caption="Generated Image", use_column_width=True)
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except Exception as e:
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st.error(f"Error generating image: {str(e)}")
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else:
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st.error("Please enter a valid prompt.")
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