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import streamlit as st |
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from PIL import Image |
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import torch |
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from diffusers import AutoPipelineForInpainting |
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from diffusers.utils import load_image |
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pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16") |
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generator = torch.Generator().manual_seed(0) |
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def main(): |
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st.title("Image and Text Preview App") |
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st.sidebar.subheader("Upload Images") |
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uploaded_image1 = st.sidebarfile_uploader("Upload Image", type=["jpg", "jpeg", "png"]) |
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uploaded_image2 = st.sidebarfile_uploader("Upload Mask", type=["jpg", "jpeg", "png"]) |
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st.subheader("Text Prompts") |
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prompt = st.text_input("Text prompt", "") |
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negative_prompt = st.text_input("Negative prompt", "") |
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guidance_scale = st.text_input("Guidance scale", 7.5) |
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steps = st.text_input("Steps", 20) |
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strength = st.text_input("Strength", 1) |
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btn_submit = st.button("Generate") |
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if btn_submit: |
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init_image = Image.open(uploaded_image1).convert("RGB") |
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st.image(init_image, caption="Uploaded Image", use_column_width=True) |
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mask_image = Image.open(uploaded_image2).convert("RGB") |
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st.image(mask_image, caption="Uploaded Mask", use_column_width=True) |
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output = pipe(prompt = prompt, |
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negative_prompt=negative_prompt, |
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image=init_image, |
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mask_image=mask_image, |
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guidance_scale=int(guidance_scale), |
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num_inference_steps=int(steps), |
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strength=strength, |
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generator=generator) |
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st.text("Generated Image:") |
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st.image(output.images[0]) |
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if __name__ == "__main__": |
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main() |
