| | |
| |
|
| | from __future__ import annotations |
| |
|
| | import gradio as gr |
| | import numpy as np |
| |
|
| | from model import Model |
| |
|
| | DESCRIPTION = "# [AvantGAN](https://github.com/ellemcfarlane/AvantGAN)" |
| |
|
| |
|
| | def get_sample_image_url(name: str) -> str: |
| | sample_image_dir = "https://huggingface.co/spaces/ellemac/avantGAN/resolve/main/samples" |
| | return f"{sample_image_dir}/{name}.png" |
| |
|
| |
|
| | def get_sample_image_markdown(name: str) -> str: |
| | url = get_sample_image_url(name) |
| | size = 128 if ("stylegan3" in name or "original" in name) else 64 |
| | return f""" |
| | - size: {size}x{size} |
| | """ |
| |
|
| |
|
| | 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_DICT.keys()), value="stylegan3-abstract" |
| | ) |
| | seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.uint32).max, step=1, value=0) |
| | run_button = gr.Button() |
| | with gr.Column(): |
| | result = gr.Image(label="Result", elem_id="result", width=300, height=300) |
| |
|
| | with gr.TabItem("Sample Images"): |
| | with gr.Row(): |
| | model_name2 = gr.Dropdown( |
| | [ |
| | "stylegan3-abstract", |
| | "stylegan3-high-fidelity", |
| | "ada-dcgan", |
| | "original-training-data", |
| | ], |
| | value="stylegan3-abstract", |
| | label="Model", |
| | ) |
| | with gr.Row(): |
| | text = get_sample_image_markdown(model_name2.value) |
| | sample_images = gr.Markdown(text) |
| |
|
| | run_button.click( |
| | fn=model.set_model_and_generate_image, |
| | inputs=[ |
| | model_name, |
| | seed, |
| | ], |
| | 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=20).launch() |
| |
|
