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import functools
import os
import shutil
import sys
import git
import gradio as gr
import numpy as np
import torch as torch
from PIL import Image
from gradio_imageslider import ImageSlider
import spaces
import fire
REPO_URL = "https://github.com/lemonaddie/geowizard.git"
CHECKPOINT = "lemonaddie/Geowizard"
REPO_DIR = "geowizard"
if os.path.isdir(REPO_DIR):
shutil.rmtree(REPO_DIR)
repo = git.Repo.clone_from(REPO_URL, REPO_DIR)
sys.path.append(os.path.join(os.getcwd(), REPO_DIR))
from pipeline.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT)
try:
import xformers
pipe.enable_xformers_memory_efficient_attention()
except:
pass # run without xformers
pipe = pipe.to(device)
#run_demo_server(pipe)
@spaces.GPU
def depth_normal(img,
denoising_steps,
ensemble_size,
processing_res,
guidance_scale,
domain):
img = img.resize((processing_res, processing_res), Image.Resampling.LANCZOS)
pipe_out = pipe(
img,
denoising_steps=denoising_steps,
ensemble_size=ensemble_size,
processing_res=processing_res,
batch_size=0,
guidance_scale=guidance_scale,
domain=domain,
show_progress_bar=True,
)
depth_colored = pipe_out.depth_colored
normal_colored = pipe_out.normal_colored
return depth_colored, normal_colored
def run_demo():
custom_theme = gr.themes.Soft(primary_hue="blue").set(
button_secondary_background_fill="*neutral_100",
button_secondary_background_fill_hover="*neutral_200")
custom_css = '''#disp_image {
text-align: center; /* Horizontally center the content */
}'''
_TITLE = '''GeoWizard'''
_DESCRIPTION = '''
<div>
Generate consistent depth and normal from single image.
<a style="display:inline-block; margin-left: .5em" href='https://github.com/xxlong0/Wonder3D/'><img src='https://img.shields.io/github/stars/xxlong0/Wonder3D?style=social' /></a>
</div>
'''
_GPU_ID = 0
with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo:
with gr.Row():
with gr.Column(scale=1):
gr.Markdown('# ' + _TITLE)
gr.Markdown(_DESCRIPTION)
with gr.Row(variant='panel'):
with gr.Column(scale=1):
input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image')
example_folder = os.path.join(os.path.dirname(__file__), "./files")
example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
gr.Examples(
examples=example_fns,
inputs=[input_image],
# outputs=[input_image],
cache_examples=False,
label='Examples (click one of the images below to start)',
examples_per_page=30
)
with gr.Column(scale=1):
with gr.Accordion('Advanced options', open=True):
with gr.Row():
domain = gr.Radio(
[
("Outdoor", "outdoor"),
("Indoor", "indoor"),
("Object", "object"),
],
label="Data Domain",
value="indoor",
)
guidance_scale = gr.Slider(
label="Classifier Free Guidance Scale",
minimum=1,
maximum=5,
step=1,
value=3,
)
denoising_steps = gr.Slider(
label="Number of denoising steps",
minimum=1,
maximum=20,
step=1,
value=10,
)
ensemble_size = gr.Slider(
label="Ensemble size",
minimum=1,
maximum=15,
step=1,
value=1,
)
processing_res = gr.Radio(
[
("Native", 0),
("Recommended", 768),
],
label="Processing resolution",
value=768,
)
run_btn = gr.Button('Generate', variant='primary', interactive=True)
with gr.Row():
depth = gr.Image(interactive=False, height=384, show_label=False)
with gr.Row():
normal = gr.Image(interactive=False, height=384, show_label=False)
run_btn.click(fn=depth_normal,
inputs=[input_image, denoising_steps,
ensemble_size,
processing_res,
guidance_scale,
domain],
outputs=[depth, normal]
)
demo.queue().launch(share=True, max_threads=80)
if __name__ == '__main__':
fire.Fire(run_demo)
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