StyleGAN2 / app.py
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#!/usr/bin/env python
from __future__ import annotations
import json
import gradio as gr
import numpy as np
from model import Model
DESCRIPTION = "# [StyleGAN2](https://github.com/NVlabs/stylegan3)"
def update_class_index(name: str) -> dict:
if name == "CIFAR-10":
return gr.Slider(maximum=9, visible=True)
else:
return gr.Slider(visible=False)
def get_sample_image_url(name: str) -> str:
sample_image_dir = "https://huggingface.co./spaces/hysts/StyleGAN2/resolve/main/samples"
return f"{sample_image_dir}/{name}.jpg"
def get_sample_image_markdown(name: str) -> str:
url = get_sample_image_url(name)
if name == "cifar10":
size = 32
class_index = "0-9"
seed = "0-9"
else:
class_index = "N/A"
seed = "0-99"
if name == "afhq-cat":
size = 512
elif name == "afhq-dog":
size = 512
elif name == "afhq-wild":
size = 512
elif name == "afhqv2":
size = 512
elif name == "brecahad":
size = 256
elif name == "celebahq":
size = 1024
elif name == "ffhq":
size = 1024
elif name == "ffhq-u":
size = 1024
elif name == "lsun-dog":
size = 256
elif name == "metfaces":
size = 1024
elif name == "metfaces-u":
size = 1024
else:
raise ValueError
return f"""
- size: {size}x{size}
- class_index: {class_index}
- seed: {seed}
- truncation: 0.7
![sample images]({url})"""
def load_class_names(name: str) -> list[str]:
with open(f"labels/{name}_classes.json") as f:
names = json.load(f)
return names
def get_class_name_df(name: str) -> list:
names = load_class_names(name)
return list(map(list, enumerate(names))) # type: ignore
CIFAR10_NAMES = load_class_names("cifar10")
def update_class_name(model_name: str, index: int) -> dict:
if model_name == "CIFAR-10":
value = CIFAR10_NAMES[index]
return gr.Textbox(value=value, visible=True)
else:
return gr.Textbox(visible=False)
model = Model()
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Tabs():
with gr.TabItem("App"):
with gr.Row():
with gr.Column():
model_name = gr.Dropdown(
label="Model", choices=list(model.MODEL_NAME_DICT.keys()), value="FFHQ-1024"
)
seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.uint32).max, step=1, value=0)
psi = gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7)
class_index = gr.Slider(label="Class Index", minimum=0, maximum=9, step=1, value=0, visible=False)
class_name = gr.Textbox(
label="Class Label", value=CIFAR10_NAMES[class_index.value], interactive=False, visible=False
)
run_button = gr.Button()
with gr.Column():
result = gr.Image(label="Result")
with gr.TabItem("Sample Images"):
with gr.Row():
model_name2 = gr.Dropdown(
label="Model",
choices=[
"afhq-cat",
"afhq-dog",
"afhq-wild",
"afhqv2",
"brecahad",
"celebahq",
"cifar10",
"ffhq",
"ffhq-u",
"lsun-dog",
"metfaces",
"metfaces-u",
],
value="afhq-cat",
)
with gr.Row():
text = get_sample_image_markdown(model_name2.value)
sample_images = gr.Markdown(text)
model_name.change(
fn=update_class_index,
inputs=model_name,
outputs=class_index,
queue=False,
api_name=False,
)
model_name.change(
fn=update_class_name,
inputs=[
model_name,
class_index,
],
outputs=class_name,
queue=False,
api_name=False,
)
class_index.change(
fn=update_class_name,
inputs=[
model_name,
class_index,
],
outputs=class_name,
queue=False,
api_name=False,
)
run_button.click(
fn=model.set_model_and_generate_image,
inputs=[
model_name,
seed,
psi,
class_index,
],
outputs=result,
api_name="run",
)
model_name2.change(
fn=get_sample_image_markdown,
inputs=model_name2,
outputs=sample_images,
queue=False,
api_name=False,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()