Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,260 Bytes
184193d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
import random
import gradio as gr
from functools import partial
from .gradio_custommodel3d import CustomModel3D
from .gradio_customgs import CustomGS
def create_interface_img_to_3d(segmentation_api, freesplatter_api, model='Zero123++ v1.2'):
default_views = {
'Zero123++ v1.1': ['Input', 'V2', 'V3', 'V5'],
'Zero123++ v1.2': ['V1', 'V2', 'V3', 'V5', 'V6'],
'Hunyuan3D Std': ['V1', 'V2', 'V4', 'V6']
}
views_info = {
'Zero123++ v1.1': 'View poses (azimuth, elevation): V1(30, 30), V2(90, -20), V3(150, 30), V4(-150, -20), V5(-90, 30), V6(-30, -20)',
'Zero123++ v1.2': 'View poses (azimuth, elevation): V1(30, 20), V2(90, -10), V3(150, 20), V4(-150, -10), V5(-90, 20), V6(-30, -10)',
'Hunyuan3D Std': 'View poses (azimuth, elevation): V1(0, 0), V2(60, 0), V3(120, 0), V4(180, 0), V5(-120, 0), V6(-60, 0)',
}
var_dict = dict()
with gr.Blocks(analytics_enabled=False) as interface:
with gr.Row():
with gr.Column(scale=1):
with gr.Row():
var_dict['in_image'] = gr.Image(
label='Input image',
type='pil',
image_mode='RGBA',
)
var_dict['fg_image'] = gr.Image(
label='Segmented foreground',
type='pil',
interactive=False,
image_mode='RGBA',
)
with gr.Accordion("Diffusion settings", open=True):
with gr.Row():
var_dict['do_rembg'] = gr.Checkbox(
label='Remove background',
value=True,
container=False,
)
with gr.Row():
with gr.Column():
var_dict['seed'] = gr.Number(
label='Random seed',
value=42,
min_width=100,
precision=0,
minimum=0,
maximum=2 ** 31,
elem_classes=['force-hide-container'],
)
var_dict['random_seed'] = gr.Button(
'\U0001f3b2\ufe0f Try your luck!',
elem_classes=['tool'],
)
with gr.Column():
var_dict['diffusion_steps'] = gr.Slider(
label="Sampling steps",
minimum=15,
maximum=75,
value=30,
step=5,
)
var_dict['guidance_scale'] = gr.Slider(
label="Guidance scale",
minimum=1,
maximum=10,
value=4,
step=1,
)
with gr.Accordion("Reconstruction settings", open=True):
with gr.Row():
var_dict['view_indices'] = gr.CheckboxGroup(
choices=['Input', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6'],
value=default_views[model],
type='index',
label='Views used for reconstruction',
info='Using input image is only recommended for Zero123++ v1.1',
)
with gr.Row():
var_dict['gs_type'] = gr.Radio(
choices=['2DGS', '3DGS'],
value='2DGS',
type='value',
label='Gaussian splatting type',
info='2DGS often leads to better mesh geometry'
)
var_dict['mesh_reduction'] = gr.Slider(
label="Mesh simplification ratio",
info='Larger ratio leads to less faces',
minimum=0.,
maximum=0.9,
value=0.7,
step=0.1,
)
with gr.Row(equal_height=False):
var_dict['run_btn'] = gr.Button('Generate', variant='primary', scale=2)
with gr.Row(visible=False):
var_dict['model'] = gr.Textbox(value=model, label='Model')
gr.Examples(
examples='examples/img_to_3d',
inputs=var_dict['in_image'],
cache_examples=False,
label='Examples (click one of the images below to start)',
examples_per_page=21,
)
with gr.Column(scale=1):
var_dict['out_multiview'] = gr.Image(
label='Generated views',
interactive=False,
image_mode='RGBA',
)
var_dict['out_pose'] = gr.Plot(
label='Estimated poses',
)
var_dict['out_gs_vis'] = CustomGS(
label='Output GS',
interactive=False,
height=320,
)
var_dict['out_video'] = gr.Video(
label='Output video',
interactive=False,
autoplay=True,
height=320,
)
var_dict['out_mesh'] = CustomModel3D(
label='Output mesh',
interactive=False,
height=400,
)
var_dict['run_btn'].click(
fn=segmentation_api,
inputs=var_dict['in_image'],
outputs=var_dict['fg_image'],
concurrency_id='default_group',
api_name='run_segmentation',
).success(
fn=partial(freesplatter_api, cache_dir=interface.GRADIO_CACHE),
inputs=[var_dict['fg_image'],
var_dict['model'],
var_dict['diffusion_steps'],
var_dict['guidance_scale'],
var_dict['seed'],
var_dict['view_indices'],
var_dict['gs_type'],
var_dict['mesh_reduction']],
outputs=[var_dict['out_multiview'], var_dict['out_gs_vis'], var_dict['out_video'], var_dict['out_mesh'], var_dict['out_pose']],
concurrency_id='default_group',
api_name='run_image_to_3d',
)
var_dict['random_seed'].click(
fn=lambda: random.randint(0, 2 ** 31),
outputs=var_dict['seed'],
show_progress=False,
api_name=False,
)
return interface, var_dict
|