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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
import argparse
import os
import logging
import numpy as np
import torch
from PIL import Image
from tqdm.auto import tqdm
import glob
import json
import cv2
import sys
sys.path.append("../")
from models.depth_normal_pipeline_clip import DepthNormalEstimationPipeline
from utils.seed_all import seed_all
import matplotlib.pyplot as plt
from dataloader.file_io import read_hdf5, align_normal, creat_uv_mesh
from utils.de_normalized import align_scale_shift
from utils.depth2normal import *
from diffusers import DiffusionPipeline, DDIMScheduler, AutoencoderKL
from models.unet_2d_condition import UNet2DConditionModel
from transformers import CLIPTextModel, CLIPTokenizer
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
import torchvision.transforms.functional as TF
from torchvision.transforms import InterpolationMode
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT)
stable_diffusion_repo_path = '.'
vae = AutoencoderKL.from_pretrained(stable_diffusion_repo_path, subfolder='vae')
scheduler = DDIMScheduler.from_pretrained(stable_diffusion_repo_path, subfolder='scheduler')
sd_image_variations_diffusers_path = '.'
image_encoder = CLIPVisionModelWithProjection.from_pretrained(sd_image_variations_diffusers_path, subfolder="image_encoder")
feature_extractor = CLIPImageProcessor.from_pretrained(sd_image_variations_diffusers_path, subfolder="feature_extractor")
unet = UNet2DConditionModel.from_pretrained('tbd')
pipe = DepthNormalEstimationPipeline(vae=vae,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
unet=unet,
scheduler=scheduler)
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: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image'''
_DESCRIPTION = '''
<div>
Generate consistent depth and normal from single image. High quality and rich details.
<a style="display:inline-block; margin-left: .5em" href='https://github.com/fuxiao0719/GeoWizard/'><img src='https://img.shields.io/github/stars/fuxiao0719/GeoWizard?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.Column():
domain = gr.Radio(
[
("Outdoor", "outdoor"),
("Indoor", "indoor"),
("Object", "object"),
],
label="Data Type (Must Select One matches your image)",
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 (More stepes, better quality)",
minimum=1,
maximum=50,
step=1,
value=20,
)
ensemble_size = gr.Slider(
label="Ensemble size (1 will be enough. More steps, higher accuracy)",
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():
with gr.Column():
depth = gr.Image(interactive=False, show_label=False)
with gr.Column():
normal = gr.Image(interactive=False, 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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