Agregando los archivos al repositorio

app.py

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+import streamlit as st
+from utils import carga_modelo, genera
+
+## Página principal
+st.title("Generador de mariposas")
+st.write(
+    "Este es un generador de mariposas que utiliza una red neuronal generativa (GAN)"
+)
+
+## Barra lateral
+st.sidebar.subheader("¡Esta mariposa no existe!, ¿Puedes creerlo?")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption("Demo creado en vivo.")
+
+## Cargamos el modelo
+repo_id = "ceyda/butterfly_cropped_uniq1K_512"
+modelo_gan = carga_modelo(repo_id)
+
+# Numero de mariposas a generar
+n_mariposas = 4
+
+
+def corre():
+    with st.spinner("Generando mariposa..."):
+        ims = genera(modelo_gan, n_mariposas)
+        st.session_state["ims"] = ims
+
+
+if "ims" not in st.session_state:
+    st.session_state["ims"] = None
+    corre()
+
+ims = st.session_state["ims"]
+
+corre_boton = st.button(
+    "Generar mariposas", on_click=corre, help="Genera mariposas aleatorias."
+)
+
+if ims is not None:
+    cols = st.columns(n_mariposas)
+    for j, im in enumerate(ims):
+        i = j % n_mariposas
+        cols[i].image(im, 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 carga_modelo(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
+    """
+    Loads a pre-trained LightweightGAN model from Hugging Face Model Hub.
+
+    Args:
+        model_name (str): The name of the pre-trained model to load. Defaults to "ceyda/butterfly_cropped_uniq1K_512".
+        model_version (str): The version of the pre-trained model to load. Defaults to None.
+
+    Returns:
+        LightweightGAN: The loaded pre-trained model.
+    """
+    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
+    gan.eval()
+    return gan
+
+
+def genera(gan, batch_size=1):
+    """
+    Generates images using the given GAN model.
+
+    Args:
+        gan (nn.Module): The GAN model to use for generating images.
+        batch_size (int, optional): The number of images to generate in each batch. Defaults to 1.
+
+    Returns:
+        numpy.ndarray: A numpy array of generated images.
+    """
+    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