Spaces:
Running
Running
File size: 4,764 Bytes
5eeb557 8612fe9 5eeb557 0a27377 a6f75f6 8612fe9 5eeb557 bf742c5 b56cca7 c708775 b56cca7 5eeb557 8612fe9 bf742c5 8612fe9 5eeb557 8612fe9 bf742c5 8612fe9 5eeb557 8612fe9 bf742c5 8612fe9 bf742c5 8612fe9 bf742c5 8612fe9 bf742c5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import shlex
import subprocess
import gradio as gr
if os.getenv('SYSTEM') == 'spaces':
import mim
mim.uninstall('mmcv-full', confirm_yes=True)
mim.install('mmcv-full==1.5.2', is_yes=True)
with open('patch') as f:
subprocess.run(shlex.split('patch -p1'), cwd='Text2Human', stdin=f)
from model import Model
DESCRIPTION = '''# [Text2Human](https://github.com/yumingj/Text2Human)
You can modify sample steps and seeds. By varying seeds, you can sample different human images under the same pose, shape description, and texture description. The larger the sample steps, the better quality of the generated images. (The default value of sample steps is 256 in the original repo.)
Label image generation step can be skipped. However, in that case, the input label image must be 512x256 in size and must contain only the specified colors.
'''
model = Model()
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label='Input Pose Image',
type='pil',
elem_id='input-image')
pose_data = gr.State()
with gr.Row():
paths = sorted(pathlib.Path('pose_images').glob('*.png'))
gr.Examples(examples=[[path.as_posix()] for path in paths],
inputs=input_image)
with gr.Row():
shape_text = gr.Textbox(
label='Shape Description',
placeholder=
'''<gender>, <sleeve length>, <length of lower clothing>, <outer clothing type>, <other accessories1>, ...
Note: The outer clothing type and accessories can be omitted.''')
with gr.Row():
gr.Examples(
examples=[['man, sleeveless T-shirt, long pants'],
['woman, short-sleeve T-shirt, short jeans']],
inputs=shape_text)
with gr.Row():
generate_label_button = gr.Button('Generate Label Image')
with gr.Column():
with gr.Row():
label_image = gr.Image(label='Label Image',
type='numpy',
elem_id='label-image')
with gr.Row():
texture_text = gr.Textbox(
label='Texture Description',
placeholder=
'''<upper clothing texture>, <lower clothing texture>, <outer clothing texture>
Note: Currently, only 5 types of textures are supported, i.e., pure color, stripe/spline, plaid/lattice, floral, denim.'''
)
with gr.Row():
gr.Examples(examples=[
['pure color, denim'],
['floral, stripe'],
],
inputs=texture_text)
with gr.Row():
sample_steps = gr.Slider(label='Sample Steps',
minimum=10,
maximum=300,
step=10,
value=10)
with gr.Row():
seed = gr.Slider(label='Seed',
minimum=0,
maximum=1000000,
step=1,
value=0)
with gr.Row():
generate_human_button = gr.Button('Generate Human')
with gr.Column():
with gr.Row():
result = gr.Image(label='Result',
type='numpy',
elem_id='result-image')
input_image.change(fn=model.process_pose_image,
inputs=input_image,
outputs=pose_data)
generate_label_button.click(fn=model.generate_label_image,
inputs=[
pose_data,
shape_text,
],
outputs=label_image)
generate_human_button.click(fn=model.generate_human,
inputs=[
label_image,
texture_text,
sample_steps,
seed,
],
outputs=result)
demo.queue(max_size=10).launch()
|