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#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import pathlib
import subprocess
import sys
import tarfile
import gradio as gr
import huggingface_hub
import numpy as np
import PIL.Image
import torch
if os.getenv('SYSTEM') == 'spaces':
with open('patch') as f:
subprocess.run('patch -p1'.split(), cwd='gan-control', stdin=f)
sys.path.insert(0, 'gan-control/src')
from gan_control.inference.controller import Controller
TITLE = 'amazon-research/gan-control'
DESCRIPTION = '''This is an unofficial demo for https://github.com/amazon-research/gan-control.
Expected execution time on Hugging Face Spaces: 7s (for one image)
'''
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.gan-control" alt="visitor badge"/></center>'
TOKEN = os.environ['TOKEN']
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--live', action='store_true')
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
parser.add_argument('--allow-flagging', type=str, default='never')
return parser.parse_args()
def download_models() -> None:
model_dir = pathlib.Path('controller_age015id025exp02hai04ori02gam15')
if not model_dir.exists():
path = huggingface_hub.hf_hub_download(
'hysts/gan-control',
'controller_age015id025exp02hai04ori02gam15.tar.gz',
use_auth_token=TOKEN)
with tarfile.open(path) as f:
f.extractall()
@torch.inference_mode()
def run(
seed: int,
truncation: float,
yaw: int,
pitch: int,
age: int,
hair_color_r: float,
hair_color_g: float,
hair_color_b: float,
nrows: int,
ncols: int,
controller: Controller,
device: torch.device,
) -> PIL.Image.Image:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
batch_size = nrows * ncols
latent_size = controller.config.model_config['latent_size']
latent = torch.from_numpy(
np.random.RandomState(seed).randn(batch_size,
latent_size)).float().to(device)
initial_image_tensors, initial_latent_z, initial_latent_w = controller.gen_batch(
latent=latent, truncation=truncation)
res0 = controller.make_resized_grid_image(initial_image_tensors,
nrow=ncols)
pose_control = torch.tensor([[yaw, pitch, 0]], dtype=torch.float32)
image_tensors, _, modified_latent_w = controller.gen_batch_by_controls(
latent=initial_latent_w,
input_is_latent=True,
orientation=pose_control)
res1 = controller.make_resized_grid_image(image_tensors, nrow=ncols)
age_control = torch.tensor([[age]], dtype=torch.float32)
image_tensors, _, modified_latent_w = controller.gen_batch_by_controls(
latent=initial_latent_w, input_is_latent=True, age=age_control)
res2 = controller.make_resized_grid_image(image_tensors, nrow=ncols)
hair_color = torch.tensor([[hair_color_r, hair_color_g, hair_color_b]],
dtype=torch.float32) / 255
hair_color = torch.clamp(hair_color, 0, 1)
image_tensors, _, modified_latent_w = controller.gen_batch_by_controls(
latent=initial_latent_w, input_is_latent=True, hair=hair_color)
res3 = controller.make_resized_grid_image(image_tensors, nrow=ncols)
return res0, res1, res2, res3
def main():
args = parse_args()
device = torch.device(args.device)
download_models()
path = 'controller_age015id025exp02hai04ori02gam15/'
controller = Controller(path, device)
func = functools.partial(run, controller=controller, device=device)
func = functools.update_wrapper(func, run)
gr.Interface(
func,
[
gr.inputs.Number(default=0, label='Seed'),
gr.inputs.Slider(0, 1, step=0.1, default=0.7, label='Truncation'),
gr.inputs.Slider(-90, 90, step=1, default=30, label='Yaw'),
gr.inputs.Slider(-90, 90, step=1, default=0, label='Pitch'),
gr.inputs.Slider(15, 75, step=1, default=75, label='Age'),
gr.inputs.Slider(
0, 255, step=1, default=186, label='Hair Color (R)'),
gr.inputs.Slider(
0, 255, step=1, default=158, label='Hair Color (G)'),
gr.inputs.Slider(
0, 255, step=1, default=92, label='Hair Color (B)'),
gr.inputs.Slider(1, 3, step=1, default=1, label='Number of Rows'),
gr.inputs.Slider(
1, 5, step=1, default=5, label='Number of Columns'),
],
[
gr.outputs.Image(type='pil', label='Generated Image'),
gr.outputs.Image(type='pil', label='Head Pose Controlled'),
gr.outputs.Image(type='pil', label='Age Controlled'),
gr.outputs.Image(type='pil', label='Hair Color Controlled'),
],
title=TITLE,
description=DESCRIPTION,
article=ARTICLE,
theme=args.theme,
allow_flagging=args.allow_flagging,
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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