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import streamlit as st
from diffusers import DiffusionPipeline
import torch
# Set page config
st.set_page_config(
page_title="Portrait Generator",
page_icon="🖼️",
layout="centered"
)
# App title and description
st.title("AI Portrait Generator")
st.markdown("Generate beautiful portraits using the AWPortraitCN2 model")
# Model parameters
with st.sidebar:
st.header("Generation Settings")
steps = st.slider("Inference Steps", min_value=20, max_value=100, value=40)
guidance_scale = st.slider("Guidance Scale", min_value=1.0, max_value=15.0, value=7.5, step=0.5)
negative_prompt = st.text_area(
"Negative Prompt",
value="lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, watermark, signature, out of frame"
)
seed = st.number_input("Random Seed (leave at -1 for random)", min_value=-1, value=-1)
# Main prompt input
prompt = st.text_area(
"Describe the portrait you want to generate",
value="Masterpiece portrait of a beautiful young woman with flowing hair, detailed face, photorealistic, 8k, professional photography"
)
# Generate button
if st.button("Generate Portrait", type="primary"):
with st.spinner("Loading model and generating portrait..."):
try:
# Set up the model pipeline
pipeline = DiffusionPipeline.from_pretrained(
"Shakker-Labs/AWPortraitCN2",
torch_dtype=torch.float16,
use_safetensors=True
)
# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = pipeline.to(device)
# Set seed if specified
generator = None
if seed != -1:
generator = torch.Generator(device).manual_seed(seed)
# Generate the image
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=steps,
guidance_scale=guidance_scale,
generator=generator
).images[0]
# Display the generated image
st.image(image, caption="Generated Portrait", use_column_width=True)
# Option to download
# Convert the PIL image to bytes
import io
from PIL import Image
buf = io.BytesIO()
image.save(buf, format="PNG")
byte_im = buf.getvalue()
st.download_button(
label="Download Portrait",
data=byte_im,
file_name="generated_portrait.png",
mime="image/png"
)
except Exception as e:
st.error(f"An error occurred: {e}")
st.info("Make sure you have enough GPU memory and the required dependencies installed.")
# Add requirements info at the bottom
st.markdown("---")
st.markdown("""
### Requirements
To run this app, you need:
- diffusers
- transformers
- accelerate
- torch
- streamlit
Install with: `pip install diffusers transformers accelerate torch streamlit`
""") |