feat: generate butterflies

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.vscode/settings.json

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+{
+  "[python]": {
+    "editor.defaultFormatter": "ms-python.black-formatter"
+  },
+  "python.formatting.provider": "none"
+}

app.py

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+import streamlit as st
+
+from utils import load_model, generate_images
+
+st.title("butterflies generator")
+st.write(
+    "this is a simple app to generate butterflies using a pretrained lightweight GAN model"
+)
+
+
+def run():
+    with st.spinner("loading model..."):
+        ims = generate_images(load_model(), n_images=1, batch_size=1)
+        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 new butterfly", on_click=run)
+
+if ims is not None:
+    st.image(ims[0], caption="butterfly", use_column_width=True)

requirements.txt

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+git+https://github.com/huggingface/community-events.git@3fea10c5d5a50c69f509e34cd580fe9139905d04#egg=huggan

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 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
+
+
+def generate_images(gan, n_images=1, batch_size=1):
+    images = []
+    for i in range(n_images // batch_size):
+        images.append(genera(gan, batch_size=batch_size))
+    images = np.concatenate(images, axis=0)
+    return images