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