Load files; app.py and utils.py

app.py

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+import streamlit as st
+
+from utils import load_model, generate
+
+
+# main page
+st.title("Butterflies generator")
+st.write("LightGan Model to generate Butterflies")
+
+# sidebar
+st.sidebar.subheader("Butterfly generated with AI")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption("Demo")
+
+# Load model
+repo_id = "ceyda/butterfly_cropped_uniq1K_512"
+modelo_gan = load_model(repo_id)
+
+# Generate 4 btfs
+n_btfs = 4
+
+def run():
+    with st.spinner("Generating... "):
+        ims = generate(model_gan, n_btfs)
+        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 Butterfly",
+    on_click = run, 
+    help = "... flying ..."
+)
+
+if ims is not None:
+    cols = st.columns(n_btfs)
+    for j, im in enumerate(ims):
+        i = j % n_btfs
+        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_cropped_uniq1K_512", model_version=None):
+    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
+    gan.eval()
+    return gan
+
+
+def generate(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