File size: 28,878 Bytes
dc1ad90
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
'''
conda activate zero123
cd stable-diffusion
python gradio_new.py 0
'''

import diffusers  # 0.12.1
import math
import fire
import gradio as gr
import lovely_numpy
import lovely_tensors
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import rich
import sys
import time
import torch
from contextlib import nullcontext
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from einops import rearrange
from functools import partial
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import create_carvekit_interface, load_and_preprocess, instantiate_from_config
from lovely_numpy import lo
from omegaconf import OmegaConf
from PIL import Image
from rich import print
from transformers import AutoFeatureExtractor #, CLIPImageProcessor
from torch import autocast
from torchvision import transforms


_SHOW_DESC = True
_SHOW_INTERMEDIATE = False
# _SHOW_INTERMEDIATE = True
_GPU_INDEX = 0
# _GPU_INDEX = 2

# _TITLE = 'Zero-Shot Control of Camera Viewpoints within a Single Image'
_TITLE = 'Zero-1-to-3: Zero-shot One Image to 3D Object'

# This demo allows you to generate novel viewpoints of an object depicted in an input image using a fine-tuned version of Stable Diffusion.
_DESCRIPTION = '''
This demo allows you to control camera rotation and thereby generate novel viewpoints of an object within a single image.
It is based on Stable Diffusion. Check out our [project webpage](https://zero123.cs.columbia.edu/) and [paper](https://arxiv.org/) if you want to learn more about the method!
Note that this model is not intended for images of humans or faces, and is unlikely to work well for them.
'''

_ARTICLE = 'See uses.md'


def load_model_from_config(config, ckpt, device, verbose=False):
    print(f'Loading model from {ckpt}')
    pl_sd = torch.load(ckpt, map_location=device)
    if 'global_step' in pl_sd:
        print(f'Global Step: {pl_sd["global_step"]}')
    sd = pl_sd['state_dict']
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print('missing keys:')
        print(m)
    if len(u) > 0 and verbose:
        print('unexpected keys:')
        print(u)

    model.to(device)
    model.eval()
    return model


@torch.no_grad()
def sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale,
                 ddim_eta, x, y, z):
    precision_scope = autocast if precision == 'autocast' else nullcontext
    with precision_scope('cuda'):
        with model.ema_scope():
            c = model.get_learned_conditioning(input_im).tile(n_samples, 1, 1)
            T = torch.tensor([math.radians(x), math.sin(
                math.radians(y)), math.cos(math.radians(y)), z])
            T = T[None, None, :].repeat(n_samples, 1, 1).to(c.device)
            c = torch.cat([c, T], dim=-1)
            c = model.cc_projection(c)
            cond = {}
            cond['c_crossattn'] = [c]
            c_concat = model.encode_first_stage((input_im.to(c.device))).mode().detach()
            cond['c_concat'] = [model.encode_first_stage((input_im.to(c.device))).mode().detach()
                                .repeat(n_samples, 1, 1, 1)]
            if scale != 1.0:
                uc = {}
                uc['c_concat'] = [torch.zeros(n_samples, 4, h // 8, w // 8).to(c.device)]
                uc['c_crossattn'] = [torch.zeros_like(c).to(c.device)]
            else:
                uc = None

            shape = [4, h // 8, w // 8]
            samples_ddim, _ = sampler.sample(S=ddim_steps,
                                             conditioning=cond,
                                             batch_size=n_samples,
                                             shape=shape,
                                             verbose=False,
                                             unconditional_guidance_scale=scale,
                                             unconditional_conditioning=uc,
                                             eta=ddim_eta,
                                             x_T=None)
            print(samples_ddim.shape)
            # samples_ddim = torch.nn.functional.interpolate(samples_ddim, 64, mode='nearest', antialias=False)
            x_samples_ddim = model.decode_first_stage(samples_ddim)
            return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()


class CameraVisualizer:
    def __init__(self, gradio_plot):
        self._gradio_plot = gradio_plot
        self._fig = None
        self._polar = 0.0
        self._azimuth = 0.0
        self._radius = 0.0
        self._raw_image = None
        self._8bit_image = None
        self._image_colorscale = None

    def polar_change(self, value):
        self._polar = value
        # return self.update_figure()

    def azimuth_change(self, value):
        self._azimuth = value
        # return self.update_figure()

    def radius_change(self, value):
        self._radius = value
        # return self.update_figure()

    def encode_image(self, raw_image):
        '''
        :param raw_image (H, W, 3) array of uint8 in [0, 255].
        '''
        # https://stackoverflow.com/questions/60685749/python-plotly-how-to-add-an-image-to-a-3d-scatter-plot

        dum_img = Image.fromarray(np.ones((3, 3, 3), dtype='uint8')).convert('P', palette='WEB')
        idx_to_color = np.array(dum_img.getpalette()).reshape((-1, 3))

        self._raw_image = raw_image
        self._8bit_image = Image.fromarray(raw_image).convert('P', palette='WEB', dither=None)
        # self._8bit_image = Image.fromarray(raw_image.clip(0, 254)).convert(
        #     'P', palette='WEB', dither=None)
        self._image_colorscale = [
            [i / 255.0, 'rgb({}, {}, {})'.format(*rgb)] for i, rgb in enumerate(idx_to_color)]

        # return self.update_figure()

    def update_figure(self):
        fig = go.Figure()

        if self._raw_image is not None:
            (H, W, C) = self._raw_image.shape

            x = np.zeros((H, W))
            (y, z) = np.meshgrid(np.linspace(-1.0, 1.0, W), np.linspace(1.0, -1.0, H) * H / W)
            print('x:', lo(x))
            print('y:', lo(y))
            print('z:', lo(z))

            fig.add_trace(go.Surface(
                x=x, y=y, z=z,
                surfacecolor=self._8bit_image,
                cmin=0,
                cmax=255,
                colorscale=self._image_colorscale,
                showscale=False,
                lighting_diffuse=1.0,
                lighting_ambient=1.0,
                lighting_fresnel=1.0,
                lighting_roughness=1.0,
                lighting_specular=0.3))

            scene_bounds = 3.5
            base_radius = 2.5
            zoom_scale = 1.5  # Note that input radius offset is in [-0.5, 0.5].
            fov_deg = 50.0
            edges = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (3, 4), (4, 1)]

            input_cone = calc_cam_cone_pts_3d(
                0.0, 0.0, base_radius, fov_deg)  # (5, 3).
            output_cone = calc_cam_cone_pts_3d(
                self._polar, self._azimuth, base_radius + self._radius * zoom_scale, fov_deg)  # (5, 3).
            # print('input_cone:', lo(input_cone).v)
            # print('output_cone:', lo(output_cone).v)

            for (cone, clr, legend) in [(input_cone, 'green', 'Input view'),
                                        (output_cone, 'blue', 'Target view')]:

                for (i, edge) in enumerate(edges):
                    (x1, x2) = (cone[edge[0], 0], cone[edge[1], 0])
                    (y1, y2) = (cone[edge[0], 1], cone[edge[1], 1])
                    (z1, z2) = (cone[edge[0], 2], cone[edge[1], 2])
                    fig.add_trace(go.Scatter3d(
                        x=[x1, x2], y=[y1, y2], z=[z1, z2], mode='lines',
                        line=dict(color=clr, width=3),
                        name=legend, showlegend=(i == 0)))
                    # text=(legend if i == 0 else None),
                    # textposition='bottom center'))
                    # hoverinfo='text',
                    # hovertext='hovertext'))

                # Add label.
                if cone[0, 2] <= base_radius / 2.0:
                    fig.add_trace(go.Scatter3d(
                        x=[cone[0, 0]], y=[cone[0, 1]], z=[cone[0, 2] - 0.05], showlegend=False,
                        mode='text', text=legend, textposition='bottom center'))
                else:
                    fig.add_trace(go.Scatter3d(
                        x=[cone[0, 0]], y=[cone[0, 1]], z=[cone[0, 2] + 0.05], showlegend=False,
                        mode='text', text=legend, textposition='top center'))

            # look at center of scene
            fig.update_layout(
                # width=640,
                # height=480,
                # height=400,
                height=360,
                autosize=True,
                hovermode=False,
                margin=go.layout.Margin(l=0, r=0, b=0, t=0),
                showlegend=True,
                legend=dict(
                    yanchor='bottom',
                    y=0.01,
                    xanchor='right',
                    x=0.99,
                ),
                scene=dict(
                    aspectmode='manual',
                    aspectratio=dict(x=1, y=1, z=1.0),
                    camera=dict(
                        eye=dict(x=base_radius - 1.6, y=0.0, z=0.6),
                        center=dict(x=0.0, y=0.0, z=0.0),
                        up=dict(x=0.0, y=0.0, z=1.0)),
                    xaxis_title='',
                    yaxis_title='',
                    zaxis_title='',
                    xaxis=dict(
                        range=[-scene_bounds, scene_bounds],
                        showticklabels=False,
                        showgrid=True,
                        zeroline=False,
                        showbackground=True,
                        showspikes=False,
                        showline=False,
                        ticks=''),
                    yaxis=dict(
                        range=[-scene_bounds, scene_bounds],
                        showticklabels=False,
                        showgrid=True,
                        zeroline=False,
                        showbackground=True,
                        showspikes=False,
                        showline=False,
                        ticks=''),
                    zaxis=dict(
                        range=[-scene_bounds, scene_bounds],
                        showticklabels=False,
                        showgrid=True,
                        zeroline=False,
                        showbackground=True,
                        showspikes=False,
                        showline=False,
                        ticks='')))

        self._fig = fig
        return fig


def preprocess_image(models, input_im, preprocess):
    '''
    :param input_im (PIL Image).
    :return input_im (H, W, 3) array in [0, 1].
    '''

    print('old input_im:', input_im.size)
    start_time = time.time()

    if preprocess:
        input_im = load_and_preprocess(models['carvekit'], input_im)
        input_im = (input_im / 255.0).astype(np.float32)
        # (H, W, 3) array in [0, 1].

    else:
        input_im = input_im.resize([256, 256], Image.Resampling.LANCZOS)
        input_im = np.asarray(input_im, dtype=np.float32) / 255.0
        # (H, W, 4) array in [0, 1].

        # old method: thresholding background, very important
        # input_im[input_im[:, :, -1] <= 0.9] = [1., 1., 1., 1.]

        # new method: apply correct method of compositing to avoid sudden transitions / thresholding
        # (smoothly transition foreground to white background based on alpha values)
        alpha = input_im[:, :, 3:4]
        white_im = np.ones_like(input_im)
        input_im = alpha * input_im + (1.0 - alpha) * white_im

        input_im = input_im[:, :, 0:3]
        # (H, W, 3) array in [0, 1].

    print(f'Infer foreground mask (preprocess_image) took {time.time() - start_time:.3f}s.')
    print('new input_im:', lo(input_im))

    return input_im


def main_run(models, device, cam_vis, return_what,
             x=0.0, y=0.0, z=0.0,
             raw_im=None, preprocess=True,
             scale=3.0, n_samples=4, ddim_steps=50, ddim_eta=1.0,
             precision='fp32', h=256, w=256):
    '''
    :param raw_im (PIL Image).
    '''
    
    safety_checker_input = models['clip_fe'](raw_im, return_tensors='pt').to(device)
    (image, has_nsfw_concept) = models['nsfw'](
        images=np.ones((1, 3)), clip_input=safety_checker_input.pixel_values)
    print('has_nsfw_concept:', has_nsfw_concept)
    if np.any(has_nsfw_concept):
        print('NSFW content detected.')
        to_return = [None] * 10
        description = ('###  <span style="color:red"> Unfortunately, '
                       'potential NSFW content was detected, '
                       'which is not supported by our model. '
                       'Please try again with a different image. </span>')
        if 'angles' in return_what:
            to_return[0] = 0.0
            to_return[1] = 0.0
            to_return[2] = 0.0
            to_return[3] = description
        else:
            to_return[0] = description
        return to_return

    else:
        print('Safety check passed.')

    input_im = preprocess_image(models, raw_im, preprocess)

    # if np.random.rand() < 0.3:
    #     description = ('Unfortunately, a human, a face, or potential NSFW content was detected, '
    #                    'which is not supported by our model.')
    #     if vis_only:
    #         return (None, None, description)
    #     else:
    #         return (None, None, None, description)

    show_in_im1 = (input_im * 255.0).astype(np.uint8)
    show_in_im2 = Image.fromarray(show_in_im1)

    if 'rand' in return_what:
        x = int(np.round(np.arcsin(np.random.uniform(-1.0, 1.0)) * 160.0 / np.pi))  # [-80, 80].
        y = int(np.round(np.random.uniform(-150.0, 150.0)))
        z = 0.0

    cam_vis.polar_change(x)
    cam_vis.azimuth_change(y)
    cam_vis.radius_change(z)
    cam_vis.encode_image(show_in_im1)
    new_fig = cam_vis.update_figure()

    if 'vis' in return_what:
        description = ('The viewpoints are visualized on the top right. '
                       'Click Run Generation to update the results on the bottom right.')

        if 'angles' in return_what:
            return (x, y, z, description, new_fig, show_in_im2)
        else:
            return (description, new_fig, show_in_im2)

    elif 'gen' in return_what:
        input_im = transforms.ToTensor()(input_im).unsqueeze(0).to(device)
        input_im = input_im * 2 - 1
        input_im = transforms.functional.resize(input_im, [h, w])

        sampler = DDIMSampler(models['turncam'])
        # used_x = -x  # NOTE: Polar makes more sense in Basile's opinion this way!
        used_x = x  # NOTE: Set this way for consistency.
        x_samples_ddim = sample_model(input_im, models['turncam'], sampler, precision, h, w,
                                      ddim_steps, n_samples, scale, ddim_eta, used_x, y, z)

        output_ims = []
        for x_sample in x_samples_ddim:
            x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
            output_ims.append(Image.fromarray(x_sample.astype(np.uint8)))

        description = None

        if 'angles' in return_what:
            return (x, y, z, description, new_fig, show_in_im2, output_ims)
        else:
            return (description, new_fig, show_in_im2, output_ims)


def calc_cam_cone_pts_3d(polar_deg, azimuth_deg, radius_m, fov_deg):
    '''
    :param polar_deg (float).
    :param azimuth_deg (float).
    :param radius_m (float).
    :param fov_deg (float).
    :return (5, 3) array of float with (x, y, z).
    '''
    polar_rad = np.deg2rad(polar_deg)
    azimuth_rad = np.deg2rad(azimuth_deg)
    fov_rad = np.deg2rad(fov_deg)
    polar_rad = -polar_rad  # NOTE: Inverse of how used_x relates to x.

    # Camera pose center:
    cam_x = radius_m * np.cos(azimuth_rad) * np.cos(polar_rad)
    cam_y = radius_m * np.sin(azimuth_rad) * np.cos(polar_rad)
    cam_z = radius_m * np.sin(polar_rad)

    # Obtain four corners of camera frustum, assuming it is looking at origin.
    # First, obtain camera extrinsics (rotation matrix only):
    camera_R = np.array([[np.cos(azimuth_rad) * np.cos(polar_rad),
                          -np.sin(azimuth_rad),
                          -np.cos(azimuth_rad) * np.sin(polar_rad)],
                         [np.sin(azimuth_rad) * np.cos(polar_rad),
                          np.cos(azimuth_rad),
                          -np.sin(azimuth_rad) * np.sin(polar_rad)],
                         [np.sin(polar_rad),
                          0.0,
                          np.cos(polar_rad)]])
    # print('camera_R:', lo(camera_R).v)

    # Multiply by corners in camera space to obtain go to space:
    corn1 = [-1.0, np.tan(fov_rad / 2.0), np.tan(fov_rad / 2.0)]
    corn2 = [-1.0, -np.tan(fov_rad / 2.0), np.tan(fov_rad / 2.0)]
    corn3 = [-1.0, -np.tan(fov_rad / 2.0), -np.tan(fov_rad / 2.0)]
    corn4 = [-1.0, np.tan(fov_rad / 2.0), -np.tan(fov_rad / 2.0)]
    corn1 = np.dot(camera_R, corn1)
    corn2 = np.dot(camera_R, corn2)
    corn3 = np.dot(camera_R, corn3)
    corn4 = np.dot(camera_R, corn4)

    # Now attach as offset to actual 3D camera position:
    corn1 = np.array(corn1) / np.linalg.norm(corn1, ord=2)
    corn_x1 = cam_x + corn1[0]
    corn_y1 = cam_y + corn1[1]
    corn_z1 = cam_z + corn1[2]
    corn2 = np.array(corn2) / np.linalg.norm(corn2, ord=2)
    corn_x2 = cam_x + corn2[0]
    corn_y2 = cam_y + corn2[1]
    corn_z2 = cam_z + corn2[2]
    corn3 = np.array(corn3) / np.linalg.norm(corn3, ord=2)
    corn_x3 = cam_x + corn3[0]
    corn_y3 = cam_y + corn3[1]
    corn_z3 = cam_z + corn3[2]
    corn4 = np.array(corn4) / np.linalg.norm(corn4, ord=2)
    corn_x4 = cam_x + corn4[0]
    corn_y4 = cam_y + corn4[1]
    corn_z4 = cam_z + corn4[2]

    xs = [cam_x, corn_x1, corn_x2, corn_x3, corn_x4]
    ys = [cam_y, corn_y1, corn_y2, corn_y3, corn_y4]
    zs = [cam_z, corn_z1, corn_z2, corn_z3, corn_z4]

    return np.array([xs, ys, zs]).T


def run_demo(
        device_idx=_GPU_INDEX,
        ckpt='105000.ckpt',
        config='configs/sd-objaverse-finetune-c_concat-256.yaml'):

    print('sys.argv:', sys.argv)
    if len(sys.argv) > 1:
        print('old device_idx:', device_idx)
        device_idx = int(sys.argv[1])
        print('new device_idx:', device_idx)

    device = f'cuda:{device_idx}'
    config = OmegaConf.load(config)

    # Instantiate all models beforehand for efficiency.
    models = dict()
    print('Instantiating LatentDiffusion...')
    models['turncam'] = load_model_from_config(config, ckpt, device=device)
    print('Instantiating Carvekit HiInterface...')
    models['carvekit'] = create_carvekit_interface()
    print('Instantiating StableDiffusionSafetyChecker...')
    models['nsfw'] = StableDiffusionSafetyChecker.from_pretrained(
        'CompVis/stable-diffusion-safety-checker').to(device)
    print('Instantiating AutoFeatureExtractor...')
    models['clip_fe'] = AutoFeatureExtractor.from_pretrained(
        'CompVis/stable-diffusion-safety-checker')

    # Reduce NSFW false positives.
    # NOTE: At the time of writing, and for diffusers 0.12.1, the default parameters are:
    # models['nsfw'].concept_embeds_weights:
    # [0.1800, 0.1900, 0.2060, 0.2100, 0.1950, 0.1900, 0.1940, 0.1900, 0.1900, 0.2200, 0.1900,
    #  0.1900, 0.1950, 0.1984, 0.2100, 0.2140, 0.2000].
    # models['nsfw'].special_care_embeds_weights:
    # [0.1950, 0.2000, 0.2200].
    # We multiply all by some factor > 1 to make them less likely to be triggered.
    models['nsfw'].concept_embeds_weights *= 1.07
    models['nsfw'].special_care_embeds_weights *= 1.07

    with open('instructions.md', 'r') as f:
        article = f.read()

    # Compose demo layout & data flow.
    demo = gr.Blocks(title=_TITLE)

    with demo:
        gr.Markdown('# ' + _TITLE)
        gr.Markdown(_DESCRIPTION)

        with gr.Row():
            with gr.Column(scale=0.9, variant='panel'):

                image_block = gr.Image(type='pil', image_mode='RGBA',
                                       label='Input image of single object')
                preprocess_chk = gr.Checkbox(
                    True, label='Preprocess image automatically (remove background and recenter object)')
                # info='If enabled, the uploaded image will be preprocessed to remove the background and recenter the object by cropping and/or padding as necessary. '
                # 'If disabled, the image will be used as-is, *BUT* a fully transparent or white background is required.'),

                gr.Markdown('*Try camera position presets:*')
                with gr.Row():
                    left_btn = gr.Button('View from the Left', variant='primary')
                    above_btn = gr.Button('View from Above', variant='primary')
                    right_btn = gr.Button('View from the Right', variant='primary')
                with gr.Row():
                    random_btn = gr.Button('Random Rotation', variant='primary')
                    below_btn = gr.Button('View from Below', variant='primary')
                    behind_btn = gr.Button('View from Behind', variant='primary')

                gr.Markdown('*Control camera position manually:*')
                polar_slider = gr.Slider(
                    -90, 90, value=0, step=5, label='Polar angle (vertical rotation in degrees)')
                # info='Positive values move the camera down, while negative values move the camera up.')
                azimuth_slider = gr.Slider(
                    -180, 180, value=0, step=5, label='Azimuth angle (horizontal rotation in degrees)')
                # info='Positive values move the camera right, while negative values move the camera left.')
                radius_slider = gr.Slider(
                    -0.5, 0.5, value=0.0, step=0.1, label='Zoom (relative distance from center)')
                # info='Positive values move the camera further away, while negative values move the camera closer.')

                samples_slider = gr.Slider(1, 8, value=4, step=1,
                                           label='Number of samples to generate')

                with gr.Accordion('Advanced options', open=False):
                    scale_slider = gr.Slider(0, 30, value=3, step=1,
                                             label='Diffusion guidance scale')
                    steps_slider = gr.Slider(5, 200, value=75, step=5,
                                             label='Number of diffusion inference steps')

                with gr.Row():
                    vis_btn = gr.Button('Visualize Angles', variant='secondary')
                    run_btn = gr.Button('Run Generation', variant='primary')

                desc_output = gr.Markdown('The results will appear on the right.', visible=_SHOW_DESC)

            with gr.Column(scale=1.1, variant='panel'):

                vis_output = gr.Plot(
                    label='Relationship between input (green) and output (blue) camera poses')

                gen_output = gr.Gallery(label='Generated images from specified new viewpoint')
                gen_output.style(grid=2)

                preproc_output = gr.Image(type='pil', image_mode='RGB',
                                          label='Preprocessed input image', visible=_SHOW_INTERMEDIATE)

        gr.Markdown(article)

        # NOTE: I am forced to update vis_output for these preset buttons,
        # because otherwise the gradio plot always resets the plotly 3D viewpoint for some reason,
        # which might confuse the user into thinking that the plot has been updated too.

        # OLD 1:
        # left_btn.click(fn=lambda: [0.0, -90.0], #, 0.0],
        #                inputs=[], outputs=[polar_slider, azimuth_slider]), #], radius_slider])
        # above_btn.click(fn=lambda: [90.0, 0.0], #, 0.0],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider]), #, radius_slider])
        # right_btn.click(fn=lambda: [0.0, 90.0], #, 0.0],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider]), #, radius_slider])
        # random_btn.click(fn=lambda: [int(np.round(np.random.uniform(-60.0, 60.0))),
        #                              int(np.round(np.random.uniform(-150.0, 150.0)))], #, 0.0],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider]), #, radius_slider])
        # below_btn.click(fn=lambda: [-90.0, 0.0], #, 0.0],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider]), #, radius_slider])
        # behind_btn.click(fn=lambda: [0.0, 180.0], #, 0.0],
        #                  inputs=[], outputs=[polar_slider, azimuth_slider]), #, radius_slider])

        # OLD 2:
        # preset_text = ('You have selected a preset target camera view. '
        #                'Now click Run Generation to update the results!')

        # left_btn.click(fn=lambda: [0.0, -90.0, None, preset_text],
        #                inputs=[], outputs=[polar_slider, azimuth_slider, vis_output, desc_output])
        # above_btn.click(fn=lambda: [90.0, 0.0, None, preset_text],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider, vis_output, desc_output])
        # right_btn.click(fn=lambda: [0.0, 90.0, None, preset_text],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider, vis_output, desc_output])
        # random_btn.click(fn=lambda: [int(np.round(np.random.uniform(-60.0, 60.0))),
        #                              int(np.round(np.random.uniform(-150.0, 150.0))),
        #                              None, preset_text],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider, vis_output, desc_output])
        # below_btn.click(fn=lambda: [-90.0, 0.0, None, preset_text],
        #                 inputs=[], outputs=[polar_slider, azimuth_slider, vis_output, desc_output])
        # behind_btn.click(fn=lambda: [0.0, 180.0, None, preset_text],
        #                  inputs=[], outputs=[polar_slider, azimuth_slider, vis_output, desc_output])

        # OLD 3 (does not work at all):
        # def a():
        #     polar_slider.value = 77.7
        #     polar_slider.postprocess(77.7)
        #     print('testa')
        # left_btn.click(fn=a)

        cam_vis = CameraVisualizer(vis_output)

        vis_btn.click(fn=partial(main_run, models, device, cam_vis, 'vis'),
                      inputs=[polar_slider, azimuth_slider, radius_slider,
                              image_block, preprocess_chk],
                      outputs=[desc_output, vis_output, preproc_output])

        run_btn.click(fn=partial(main_run, models, device, cam_vis, 'gen'),
                      inputs=[polar_slider, azimuth_slider, radius_slider,
                              image_block, preprocess_chk,
                              scale_slider, samples_slider, steps_slider],
                      outputs=[desc_output, vis_output, preproc_output, gen_output])

        # NEW:
        preset_inputs = [image_block, preprocess_chk,
                         scale_slider, samples_slider, steps_slider]
        preset_outputs = [polar_slider, azimuth_slider, radius_slider,
                          desc_output, vis_output, preproc_output, gen_output]
        left_btn.click(fn=partial(main_run, models, device, cam_vis, 'angles_gen',
                                  0.0, -90.0, 0.0),
                       inputs=preset_inputs, outputs=preset_outputs)
        above_btn.click(fn=partial(main_run, models, device, cam_vis, 'angles_gen',
                                   -90.0, 0.0, 0.0),
                       inputs=preset_inputs, outputs=preset_outputs)
        right_btn.click(fn=partial(main_run, models, device, cam_vis, 'angles_gen',
                                   0.0, 90.0, 0.0),
                       inputs=preset_inputs, outputs=preset_outputs)
        random_btn.click(fn=partial(main_run, models, device, cam_vis, 'rand_angles_gen',
                                    -1.0, -1.0, -1.0),
                       inputs=preset_inputs, outputs=preset_outputs)
        below_btn.click(fn=partial(main_run, models, device, cam_vis, 'angles_gen',
                                   90.0, 0.0, 0.0),
                       inputs=preset_inputs, outputs=preset_outputs)
        behind_btn.click(fn=partial(main_run, models, device, cam_vis, 'angles_gen',
                                    0.0, 180.0, 0.0),
                       inputs=preset_inputs, outputs=preset_outputs)

    demo.launch(enable_queue=True, share=True)


if __name__ == '__main__':

    fire.Fire(run_demo)