#!/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()