File size: 6,942 Bytes
0d89806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
00a9186
0d89806
 
 
 
 
 
 
 
 
 
 
 
 
 
9582ec0
4b257ce
 
 
 
 
 
2670857
9582ec0
 
 
 
 
 
463b37d
0d89806
 
 
 
 
 
 
 
 
 
 
 
 
6524a56
0d89806
 
6524a56
f788b7c
 
6524a56
dbbe505
 
 
 
 
 
 
 
 
0d89806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
825b5cb
0d89806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1adb3ff
0d89806
 
 
43247b9
0d89806
 
9085e1a
0d89806
 
 
9085e1a
0d89806
8693079
6524a56
0d89806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6524a56
0d89806
 
 
 
 
 
 
 
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
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
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 = "stabilityai/stable-diffusion-2-1-unclip"
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 = 'lambdalabs/sd-image-variations-diffusers'
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('.', subfolder="unet")

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)

@spaces.GPU
def depth_normal(img,
                denoising_steps,
                ensemble_size,
                processing_res,
                seed,
                domain):

    seed = int(seed)
    if seed >= 0:
        torch.manual_seed(seed)

    pipe_out = pipe(
        img,
        denoising_steps=denoising_steps,
        ensemble_size=ensemble_size,
        processing_res=processing_res,
        batch_size=0,
        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. (PS: We find the demo running on ZeroGPU output slightly inferior results compared to A100 or 3060 with everything exactly the same.)
    <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],
                    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 (More steps, higher accuracy)",
                         minimum=1,
                         maximum=15,
                         step=1,
                         value=3,
                     )
                        seed = gr.Number(0, label='Random Seed. Negative values for not specifying')
                        
                        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,
                                seed,
                                domain],
                        outputs=[depth, normal]
                        )
        demo.queue().launch(share=True, max_threads=80)


if __name__ == '__main__':
    fire.Fire(run_demo)