first commit

app.py

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+import streamlit as st
+
+from utils import load_model, genera
+
+## Mean page
+st.title("Butterflies Generator")
+st.write("This is a Light GAN model trained and used by me")
+
+## Sidebar
+st.sidebar.subheader("This butterfly not is real, can you believe it?")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption("Demo created live")
+
+## Load model
+repo_id = "ceyda/butterfly_cropped_uniq1K_512"
+model_gan = load_model(repo_id)
+
+## We generated four butterflies
+n_butterflies = 4
+
+def run():
+    with st.spinner("Generating, wait a bit..."):
+        ims = genera(model_gan, n_butterflies)
+        st.session_state["ims"] = ims
+
+if "ims" not in st.session_state:
+    st.session_state["ims"] = None
+    run()
+
+ims = st.session_state["ims"]
+
+run_button = st.button(
+    "Generate",
+    on_click=run,
+    help="We are in fly"
+)
+
+if ims is not None:
+    cols = st.columns(n_butterflies)
+    for j, im in enumerate(ims):
+        i = j % n_butterflies
+        cols[i].image(im, use_column_width=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+

utils.py

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+import numpy as np
+import torch
+from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
+ 
+def load_model(model_name="ceyda/butterfly_cropper_uniq1K_512", model_version=None):
+    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
+    gan.eval()
+    return gan
+
+def genera(gan, batch_size=1):
+    with torch.no_grad():
+        ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0.0, 1.0) * 255
+        ims = ims.permute(0,2,3,1).detach().cpu().numpy().astype(np.uint8)
+    return ims