import gradio as gr
import argparse
import os, sys
import torch
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')
parser.add_argument('--allow-screenshot', action='store_true')
return parser.parse_args()
_TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
_DESCRIPTION = '''
We present DreamGausssion, a 3D content generation framework that significantly improves the efficiency of 3D content creation.
'''
_IMG_USER_GUIDE = "Please upload an image in the block above (or choose an example above) and click **Run Generation**."
_TXT_USER_GUIDE = "Please type what you want to generate in the block above and click **Run Generation**."
# trigger Image-to-3D model
def inference_img(img):
pass
# trigger Text-to-3D model
def inference_txt(txt):
pass
def run_demo():
args = parse_args()
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('*** Now using %s.'%(args.device))
# append README as extra info
with open('README.md', 'r') as f:
article = f.read()
# NOTE: Examples must match inputs
example_folder = os.path.join(os.path.dirname(__file__), 'demo_examples')
example_fns = os.listdir(example_folder)
example_fns.sort()
examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')]
# Compose demo layout & data flow
with gr.Blocks(title=_TITLE, css="style.css") as demo:
with gr.Row():
with gr.Column(scale=1):
gr.Markdown('# ' + _TITLE)
with gr.Column(scale=0):
gr.DuplicateButton(value='Duplicate Space for private use',
elem_id='duplicate-button')
gr.Markdown(_DESCRIPTION)
# Image-to-3D
with gr.Row(variant='panel'):
with gr.Column(scale=6):
image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image', tool=None)
elevation_slider = gr.Slider(-90, 90, value=0, step=1, label='Estimated elevation angle')
gr.Markdown("default to 0 (horizontal), range from [-90, 90]. If you upload a look-down image, try a value like -30")
preprocess_chk = gr.Checkbox(True, label='Preprocess image automatically (remove background and recenter object)')
gr.Examples(
examples=examples_full, # NOTE: elements must match inputs list!
inputs=[image_block],
outputs=[image_block],
cache_examples=False,
label='Examples (click one of the images below to start)',
examples_per_page=40
)
img_run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
img_guide_text = gr.Markdown(_IMG_USER_GUIDE, visible=True)
with gr.Column(scale=4):
processed_image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Processed image', tool=None)
img_mesh_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="Textured Mesh", elem_id="img-model-3d-out")
# Text-to-3D
with gr.Row(variant='panel'):
with gr.Column(scale=6):
text_block = gr.Textbox(label="Input text")
txt_run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
txt_guide_text = gr.Markdown(_TXT_USER_GUIDE, visible=True)
with gr.Column(scale=4):
txt_mesh_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="Textured Mesh", elem_id="txt-model-3d-out")
gr.Markdown(article)
gr.HTML("""
""")
demo.queue().launch(share=True, max_threads=80) # auth=("admin", os.environ['PASSWD'])
if __name__ == '__main__':
run_demo()
