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LICENSE

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+MIT License
+
+Copyright (c) 2022 Comunidad Platzi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+import streamlit as st
+
+from utils import carga_modelo, genera
+
+## Página principal
+st.title("Butterfly GAN (GAN de mariposas)")
+st.write(
+    "Modelo Light-GAN entrenado con 1000 imágenes de mariposas tomadas de la colección del Museo Smithsonian."
+)
+
+## Barra lateral
+st.sidebar.subheader("¡Esta mariposa no existe! Ni en América Latina 🤯.")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption(
+    f"[Modelo](https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512) y [Dataset](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) usados."
+)
+st.sidebar.caption(f"*Disclaimers:*")
+st.sidebar.caption(
+    "* Este demo es una versión simplificada del creado por [Ceyda Cinarel](https://github.com/cceyda) y [Jonathan Whitaker](https://datasciencecastnet.home.blog/) ([link](https://huggingface.co/spaces/huggan/butterfly-gan)) durante el hackathon [HugGan](https://github.com/huggingface/community-events). Cualquier error se atribuye a [Omar Espejel](https://twitter.com/espejelomar)."
+)
+st.sidebar.caption(
+    "* Modelo basado en el [paper](https://openreview.net/forum?id=1Fqg133qRaI) *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*."
+)
+
+## Cargamos modelo
+repo_id = "ceyda/butterfly_cropped_uniq1K_512"
+version_modelo = "57d36a15546909557d9f967f47713236c8288838"
+modelo_gan = carga_modelo(repo_id, version_modelo)
+
+## Generamos 4 mariposas
+n_mariposas = 4
+
+## Función que genera mariposas y lo guarda como un estado de la sesión
+def corre():
+    with st.spinner("Generando, espera un poco..."):
+        ims = genera(modelo_gan, n_mariposas)
+        st.session_state["ims"] = ims
+
+
+## Si no hay una imagen generada entonces generala
+if "ims" not in st.session_state:
+    st.session_state["ims"] = None
+    corre()
+
+## ims contiene las imágenes generadas
+ims = st.session_state["ims"]
+
+## Si la usuaria da click en el botón entonces corremos la función genera()
+corre_boton = st.button(
+    "Genera mariposas, porfa.",
+    on_click=corre,
+    help="Estamos en pleno vuelo, puede tardar.",
+)
+
+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)

packages.txt

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+libgl1

requirements.txt

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+git+https://github.com/huggingface/community-events.git@3fea10c5d5a50c69f509e34cd580fe9139905d04#egg=huggan
+transformers
+faiss-cpu
+paddlehub
+paddlepaddle

utils.py

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+import numpy as np
+import torch
+from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
+
+
+## Cargamos el modelo desde el Hub de Hugging Face
+def carga_modelo(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
+    gan = LightweightGAN.from_pretrained(model_name, version=model_version)
+    gan.eval()
+    return gan
+
+
+## Usamos el modelo GAN para generar imágenes
+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