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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 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") | |
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('./wocfg/unet_ema') | |
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) | |
def depth_normal(img, | |
denoising_steps, | |
ensemble_size, | |
processing_res, | |
#guidance_scale, | |
seed, | |
domain): | |
seed = int(seed) | |
torch.manual_seed(seed) | |
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", | |
) | |
denoising_steps = gr.Slider( | |
label="Number of denoising steps (More steps, better quality)", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=10, | |
) | |
ensemble_size = gr.Slider( | |
label="Ensemble size (1 will be enough. More steps, higher accuracy)", | |
minimum=1, | |
maximum=15, | |
step=1, | |
value=1, | |
) | |
seed = gr.Number(42, label='Seed. May try different seed for better results.') | |
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, | |
seed, | |
domain], | |
outputs=[depth, normal] | |
) | |
demo.queue().launch(share=True, max_threads=80) | |
if __name__ == '__main__': | |
fire.Fire(run_demo) | |