firts try gan

app.py

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+import streamlit as st
+
+from utils import load_model, generate
+
+## Title and write
+st.title("Butterfly GAN")
+st.write(
+    "Light-GAN model trained with 1000 butterfly images taken from the Smithsonian Museum collection."
+)
+
+## Sidebar
+st.sidebar.subheader("This butterfly does not exist!.")
+st.sidebar.image("assets/logo.png", width=200)
+st.sidebar.caption(
+    f"[Model](https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512) and [Dataset](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) used."
+)
+st.sidebar.caption(f"*Disclaimers:*")
+st.sidebar.caption(
+    "* This demo is a simplified version of the one created by [Ceyda Cinarel](https://github.com/cceyda) and [Jonathan Whitaker](https://datasciencecastnet.home.blog/) ([link](https://huggingface.co/spaces/huggan/butterfly-gan)) during the hackathon [HugGan](https://github.com/huggingface/community-events)."
+)
+st.sidebar.caption(
+    "* Model based on [paper](https://openreview.net/forum?id=1Fqg133qRaI) *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*."
+)
+
+## Load model
+repo_id = "ceyda/butterfly_cropped_uniq1K_512"
+version_model = "57d36a15546909557d9f967f47713236c8288838"
+model_gan = load_model(repo_id, version_model)
+
+
+n_mariposas = 4
+
+## Function that generates butterflies and saves it as a state of the session
+def run():
+    with st.spinner("Generating, wait a little..."):
+        ims = generate(model_gan, n_mariposas)
+        st.session_state["ims"] = ims
+
+
+if "ims" not in st.session_state:
+    st.session_state["ims"] = None
+    run()
+
+ims = st.session_state["ims"]
+
+
+run_boton = st.button(
+    "Spawn butterflies.",
+    on_click=run,
+    help="Generate images.",
+)
+
+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
+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
+
+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