Spaces:
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Sleeping
Eloi Campeny
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d717c03
1
Parent(s):
5c7b333
firts try gan
Browse files- app.py +57 -0
- assets/logo.png +0 -0
- requirements.txt +5 -0
- utils.py +15 -0
app.py
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import streamlit as st
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from utils import load_model, generate
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## Title and write
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st.title("Butterfly GAN")
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st.write(
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"Light-GAN model trained with 1000 butterfly images taken from the Smithsonian Museum collection."
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)
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## Sidebar
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st.sidebar.subheader("This butterfly does not exist!.")
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st.sidebar.image("assets/logo.png", width=200)
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st.sidebar.caption(
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f"[Model](https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512) and [Dataset](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) used."
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)
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st.sidebar.caption(f"*Disclaimers:*")
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st.sidebar.caption(
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"* 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)."
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)
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st.sidebar.caption(
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"* Model based on [paper](https://openreview.net/forum?id=1Fqg133qRaI) *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*."
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)
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## Load model
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repo_id = "ceyda/butterfly_cropped_uniq1K_512"
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version_model = "57d36a15546909557d9f967f47713236c8288838"
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model_gan = load_model(repo_id, version_model)
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n_mariposas = 4
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## Function that generates butterflies and saves it as a state of the session
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def run():
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with st.spinner("Generating, wait a little..."):
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ims = generate(model_gan, n_mariposas)
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st.session_state["ims"] = ims
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if "ims" not in st.session_state:
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st.session_state["ims"] = None
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run()
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ims = st.session_state["ims"]
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run_boton = st.button(
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"Spawn butterflies.",
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on_click=run,
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help="Generate images.",
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)
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if ims is not None:
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cols = st.columns(n_mariposas)
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for j, im in enumerate(ims):
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i = j % n_mariposas
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cols[i].image(im, use_column_width=True)
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assets/logo.png
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![]() |
requirements.txt
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git+https://github.com/huggingface/community-events.git@3fea10c5d5a50c69f509e34cd580fe9139905d04#egg=huggan
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transformers
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faiss-cpu
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paddlehub
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paddlepaddle
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utils.py
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import numpy as np
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import torch
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from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
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def load_model(model_name="ceyda/butterfly_cropped_uniq1K_512", model_version=None):
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gan = LightweightGAN.from_pretrained(model_name, version=model_version)
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gan.eval()
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return gan
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def generate(gan, batch_size=1):
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with torch.no_grad():
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ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0.0, 1.0) * 255
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ims = ims.permute(0, 2, 3, 1).detach().cpu().numpy().astype(np.uint8)
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return ims
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