File size: 25,789 Bytes
21c4e64
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import os
import cv2
import time
import tqdm
import numpy as np

import torch
import torch.nn.functional as F

import rembg

from cam_utils import orbit_camera, OrbitCamera
from gs_renderer_4d import Renderer, MiniCam

from grid_put import mipmap_linear_grid_put_2d
import imageio

import copy


class GUI:
    def __init__(self, opt):
        self.opt = opt  # shared with the trainer's opt to support in-place modification of rendering parameters.
        self.gui = opt.gui # enable gui
        self.W = opt.W
        self.H = opt.H
        self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy)

        self.mode = "image"
        # self.seed = "random"
        self.seed = 888

        self.buffer_image = np.ones((self.W, self.H, 3), dtype=np.float32)
        self.need_update = True  # update buffer_image

        # models
        self.device = torch.device("cuda")
        self.bg_remover = None

        self.guidance_sd = None
        self.guidance_zero123 = None
        self.guidance_svd = None


        self.enable_sd = False
        self.enable_zero123 = False
        self.enable_svd = False


        # renderer
        self.renderer = Renderer(self.opt, sh_degree=self.opt.sh_degree)
        self.gaussain_scale_factor = 1

        # input image
        self.input_img = None
        self.input_mask = None
        self.input_img_torch = None
        self.input_mask_torch = None
        self.overlay_input_img = False
        self.overlay_input_img_ratio = 0.5

        self.input_img_list = None
        self.input_mask_list = None
        self.input_img_torch_list = None
        self.input_mask_torch_list = None

        # input text
        self.prompt = ""
        self.negative_prompt = ""

        # training stuff
        self.training = False
        self.optimizer = None
        self.step = 0
        self.train_steps = 1  # steps per rendering loop
        
        # load input data from cmdline
        if self.opt.input is not None: # True
            self.load_input(self.opt.input) # load imgs, if has bg, then rm bg; or just load imgs
        
        # override prompt from cmdline
        if self.opt.prompt is not None: # None
            self.prompt = self.opt.prompt

        # override if provide a checkpoint
        if self.opt.load is not None: # not None
            self.renderer.initialize(self.opt.load)  
            # self.renderer.gaussians.load_model(opt.outdir, opt.save_path)             
        else:
            # initialize gaussians to a blob
            self.renderer.initialize(num_pts=self.opt.num_pts)

        self.seed_everything()

    def seed_everything(self):
        try:
            seed = int(self.seed)
        except:
            seed = np.random.randint(0, 1000000)

        print(f'Seed: {seed:d}')
        os.environ["PYTHONHASHSEED"] = str(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = True

        self.last_seed = seed

    def prepare_train(self):

        self.step = 0

        # setup training
        self.renderer.gaussians.training_setup(self.opt)

        # # do not do progressive sh-level
        self.renderer.gaussians.active_sh_degree = self.renderer.gaussians.max_sh_degree
        self.optimizer = self.renderer.gaussians.optimizer

        # default camera
        if self.opt.mvdream or self.opt.imagedream:
            # the second view is the front view for mvdream/imagedream.
            pose = orbit_camera(self.opt.elevation, 90, self.opt.radius)
        else:
            pose = orbit_camera(self.opt.elevation, 0, self.opt.radius)
        self.fixed_cam = MiniCam(
            pose,
            self.opt.ref_size,
            self.opt.ref_size,
            self.cam.fovy,
            self.cam.fovx,
            self.cam.near,
            self.cam.far,
        )

        self.enable_sd = self.opt.lambda_sd > 0
        self.enable_zero123 = self.opt.lambda_zero123 > 0
        self.enable_svd = self.opt.lambda_svd > 0 and self.input_img is not None

        # lazy load guidance model
        if self.guidance_sd is None and self.enable_sd:
            if self.opt.mvdream:
                print(f"[INFO] loading MVDream...")
                from guidance.mvdream_utils import MVDream
                self.guidance_sd = MVDream(self.device)
                print(f"[INFO] loaded MVDream!")
            elif self.opt.imagedream:
                print(f"[INFO] loading ImageDream...")
                from guidance.imagedream_utils import ImageDream
                self.guidance_sd = ImageDream(self.device)
                print(f"[INFO] loaded ImageDream!")
            else:
                print(f"[INFO] loading SD...")
                from guidance.sd_utils import StableDiffusion
                self.guidance_sd = StableDiffusion(self.device)
                print(f"[INFO] loaded SD!")

        if self.guidance_zero123 is None and self.enable_zero123:
            print(f"[INFO] loading zero123...")
            from guidance.zero123_utils import Zero123
            if self.opt.stable_zero123:
                self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/stable-zero123-diffusers')
            else:
                self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/zero123-xl-diffusers')
            print(f"[INFO] loaded zero123!")

        if self.guidance_svd is None and self.enable_svd: # False
            print(f"[INFO] loading SVD...")
            from guidance.svd_utils import StableVideoDiffusion
            self.guidance_svd = StableVideoDiffusion(self.device)
            print(f"[INFO] loaded SVD!")

        # input image
        if self.input_img is not None:
            self.input_img_torch = torch.from_numpy(self.input_img).permute(2, 0, 1).unsqueeze(0).to(self.device)
            self.input_img_torch = F.interpolate(self.input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)

            self.input_mask_torch = torch.from_numpy(self.input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device)
            self.input_mask_torch = F.interpolate(self.input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)

        if self.input_img_list is not None:
            self.input_img_torch_list = [torch.from_numpy(input_img).permute(2, 0, 1).unsqueeze(0).to(self.device) for input_img in self.input_img_list]
            self.input_img_torch_list = [F.interpolate(input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False) for input_img_torch in self.input_img_torch_list]
            
            self.input_mask_torch_list = [torch.from_numpy(input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device) for input_mask in self.input_mask_list]
            self.input_mask_torch_list = [F.interpolate(input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False) for input_mask_torch in self.input_mask_torch_list]
        # prepare embeddings
        with torch.no_grad():

            if self.enable_sd:
                if self.opt.imagedream:
                    img_pos_list, img_neg_list, ip_pos_list, ip_neg_list, emb_pos_list, emb_neg_list = [], [], [], [], [], []
                    for _ in range(self.opt.n_views):
                        for input_img_torch in self.input_img_torch_list:
                            img_pos, img_neg, ip_pos, ip_neg, emb_pos, emb_neg = self.guidance_sd.get_image_text_embeds(input_img_torch, [self.prompt], [self.negative_prompt])
                            img_pos_list.append(img_pos)
                            img_neg_list.append(img_neg)
                            ip_pos_list.append(ip_pos)
                            ip_neg_list.append(ip_neg)
                            emb_pos_list.append(emb_pos)
                            emb_neg_list.append(emb_neg)
                    self.guidance_sd.image_embeddings['pos'] = torch.cat(img_pos_list, 0)
                    self.guidance_sd.image_embeddings['neg'] = torch.cat(img_pos_list, 0)
                    self.guidance_sd.image_embeddings['ip_img'] = torch.cat(ip_pos_list, 0)
                    self.guidance_sd.image_embeddings['neg_ip_img'] = torch.cat(ip_neg_list, 0)
                    self.guidance_sd.embeddings['pos'] = torch.cat(emb_pos_list, 0)
                    self.guidance_sd.embeddings['neg'] = torch.cat(emb_neg_list, 0)
                else:
                    self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt])

            if self.enable_zero123:
                c_list, v_list = [], []
                for _ in range(self.opt.n_views):
                    for input_img_torch in self.input_img_torch_list:
                        c, v = self.guidance_zero123.get_img_embeds(input_img_torch)
                        c_list.append(c)
                        v_list.append(v)
                self.guidance_zero123.embeddings = [torch.cat(c_list, 0), torch.cat(v_list, 0)]
            
            if self.enable_svd:
                self.guidance_svd.get_img_embeds(self.input_img)

    def train_step(self):
        starter = torch.cuda.Event(enable_timing=True)
        ender = torch.cuda.Event(enable_timing=True)
        starter.record()

        for _ in range(self.train_steps): # 1

            self.step += 1 # self.step starts from 0
            step_ratio = min(1, self.step / self.opt.iters) # 1, step / 500

            # update lr
            self.renderer.gaussians.update_learning_rate(self.step)

            loss = 0

            self.renderer.prepare_render()
        
            ### known view
            if not self.opt.imagedream:
                for b_idx in range(self.opt.batch_size):
                    cur_cam = copy.deepcopy(self.fixed_cam)
                    cur_cam.time = b_idx
                    out = self.renderer.render(cur_cam)

                    # rgb loss
                    image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
                    loss = loss + 10000 * step_ratio * F.mse_loss(image, self.input_img_torch_list[b_idx]) / self.opt.batch_size

                    # mask loss
                    mask = out["alpha"].unsqueeze(0) # [1, 1, H, W] in [0, 1]
                    loss = loss + 1000 * step_ratio * F.mse_loss(mask, self.input_mask_torch_list[b_idx]) / self.opt.batch_size

            ### novel view (manual batch)
            render_resolution = 128 if step_ratio < 0.3 else (256 if step_ratio < 0.6 else 512)
            # render_resolution = 512
            images = []
            poses = []
            vers, hors, radii = [], [], []
            # avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30]
            min_ver = max(min(self.opt.min_ver, self.opt.min_ver - self.opt.elevation), -80 - self.opt.elevation)
            max_ver = min(max(self.opt.max_ver, self.opt.max_ver - self.opt.elevation), 80 - self.opt.elevation)

            for _ in range(self.opt.n_views):
                for b_idx in range(self.opt.batch_size):

                    # render random view
                    ver = np.random.randint(min_ver, max_ver)
                    hor = np.random.randint(-180, 180)
                    radius = 0

                    vers.append(ver)
                    hors.append(hor)
                    radii.append(radius)

                    pose = orbit_camera(self.opt.elevation + ver, hor, self.opt.radius + radius)
                    poses.append(pose)

                    cur_cam = MiniCam(pose, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, time=b_idx)

                    bg_color = torch.tensor([1, 1, 1] if np.random.rand() > self.opt.invert_bg_prob else [0, 0, 0], dtype=torch.float32, device="cuda")
                    out = self.renderer.render(cur_cam, bg_color=bg_color)

                    image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
                    images.append(image)

                    # enable mvdream training
                    if self.opt.mvdream or self.opt.imagedream: # False
                        for view_i in range(1, 4):
                            pose_i = orbit_camera(self.opt.elevation + ver, hor + 90 * view_i, self.opt.radius + radius)
                            poses.append(pose_i)

                            cur_cam_i = MiniCam(pose_i, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far)

                            # bg_color = torch.tensor([0.5, 0.5, 0.5], dtype=torch.float32, device="cuda")
                            out_i = self.renderer.render(cur_cam_i, bg_color=bg_color)

                            image = out_i["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
                            images.append(image)



            images = torch.cat(images, dim=0)
            poses = torch.from_numpy(np.stack(poses, axis=0)).to(self.device)

            # guidance loss
            if self.enable_sd:
                if self.opt.mvdream or self.opt.imagedream:
                    loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, poses, step_ratio)
                else:
                    loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, step_ratio)

            if self.enable_zero123:
                loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii, step_ratio) / (self.opt.batch_size * self.opt.n_views)

            if self.enable_svd:
                loss = loss + self.opt.lambda_svd * self.guidance_svd.train_step(images, step_ratio)
            
            # optimize step
            loss.backward()
            self.optimizer.step()
            self.optimizer.zero_grad()

            # densify and prune
            if self.step >= self.opt.density_start_iter and self.step <= self.opt.density_end_iter:
                viewspace_point_tensor, visibility_filter, radii = out["viewspace_points"], out["visibility_filter"], out["radii"]
                self.renderer.gaussians.max_radii2D[visibility_filter] = torch.max(self.renderer.gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
                self.renderer.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)

                if self.step % self.opt.densification_interval == 0:
                    # size_threshold = 20 if self.step > self.opt.opacity_reset_interval else None
                    self.renderer.gaussians.densify_and_prune(self.opt.densify_grad_threshold, min_opacity=0.01, extent=0.5, max_screen_size=1)
                
                if self.step % self.opt.opacity_reset_interval == 0:
                    self.renderer.gaussians.reset_opacity()

        ender.record()
        torch.cuda.synchronize()
        t = starter.elapsed_time(ender)

        self.need_update = True

    
    def load_input(self, file):
        if self.opt.data_mode == 'c4d':
            file_list = [os.path.join(file, f'{x * self.opt.downsample_rate}.png') for x in range(self.opt.batch_size)] 
        elif self.opt.data_mode == 'svd':
            # file_list = [file.replace('.png', f'_frames/{x* self.opt.downsample_rate:03d}_rgba.png') for x in range(self.opt.batch_size)]
            # file_list = [x if os.path.exists(x) else (x.replace('_rgba.png', '.png')) for x in file_list]
            file_list = [file.replace('.png', f'_frames/{x* self.opt.downsample_rate:03d}.png') for x in range(self.opt.batch_size)]
        else:
            raise NotImplementedError
        self.input_img_list, self.input_mask_list = [], []
        for file in file_list:
            # load image
            print(f'[INFO] load image from {file}...')
            img = cv2.imread(file, cv2.IMREAD_UNCHANGED)
            if img.shape[-1] == 3:
                if self.bg_remover is None:
                    self.bg_remover = rembg.new_session()
                img = rembg.remove(img, session=self.bg_remover)
                # cv2.imwrite(file.replace('.png', '_rgba.png'), img) 
            img = cv2.resize(img, (self.W, self.H), interpolation=cv2.INTER_AREA)
            img = img.astype(np.float32) / 255.0
            input_mask = img[..., 3:]
            # white bg
            input_img = img[..., :3] * input_mask + (1 - input_mask)
            # bgr to rgb
            input_img = input_img[..., ::-1].copy()
            self.input_img_list.append(input_img)
            self.input_mask_list.append(input_mask)

    @torch.no_grad()
    def save_model(self, mode='geo', texture_size=1024, interp=1):
        os.makedirs(self.opt.outdir, exist_ok=True)
        if mode == 'geo':
            path = f'logs/{opt.save_path}_mesh_{t:03d}.ply'
            mesh = self.renderer.gaussians.extract_mesh_t(path, self.opt.density_thresh, t=t)
            mesh.write_ply(path)

        elif mode == 'geo+tex':
            from mesh import Mesh, safe_normalize
            os.makedirs(os.path.join(self.opt.outdir, self.opt.save_path+'_meshes'), exist_ok=True)
            for t in range(self.opt.batch_size):
                path = os.path.join(self.opt.outdir, self.opt.save_path+'_meshes', f'{t:03d}.obj')
                mesh = self.renderer.gaussians.extract_mesh_t(path, self.opt.density_thresh, t=t)

                # perform texture extraction
                print(f"[INFO] unwrap uv...")
                h = w = texture_size
                mesh.auto_uv()
                mesh.auto_normal()

                albedo = torch.zeros((h, w, 3), device=self.device, dtype=torch.float32)
                cnt = torch.zeros((h, w, 1), device=self.device, dtype=torch.float32)

                vers = [0] * 8 + [-45] * 8 + [45] * 8 + [-89.9, 89.9]
                hors = [0, 45, -45, 90, -90, 135, -135, 180] * 3 + [0, 0]

                render_resolution = 512

                import nvdiffrast.torch as dr

                if not self.opt.force_cuda_rast and (not self.opt.gui or os.name == 'nt'):
                    glctx = dr.RasterizeGLContext()
                else:
                    glctx = dr.RasterizeCudaContext()

                for ver, hor in zip(vers, hors):
                    # render image
                    pose = orbit_camera(ver, hor, self.cam.radius)

                    cur_cam = MiniCam(
                        pose,
                        render_resolution,
                        render_resolution,
                        self.cam.fovy,
                        self.cam.fovx,
                        self.cam.near,
                        self.cam.far,
                        time=t
                    )
                    
                    cur_out = self.renderer.render(cur_cam)

                    rgbs = cur_out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
                        
                    # get coordinate in texture image
                    pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
                    proj = torch.from_numpy(self.cam.perspective.astype(np.float32)).to(self.device)

                    v_cam = torch.matmul(F.pad(mesh.v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0)
                    v_clip = v_cam @ proj.T
                    rast, rast_db = dr.rasterize(glctx, v_clip, mesh.f, (render_resolution, render_resolution))

                    depth, _ = dr.interpolate(-v_cam[..., [2]], rast, mesh.f) # [1, H, W, 1]
                    depth = depth.squeeze(0) # [H, W, 1]

                    alpha = (rast[0, ..., 3:] > 0).float()

                    uvs, _ = dr.interpolate(mesh.vt.unsqueeze(0), rast, mesh.ft)  # [1, 512, 512, 2] in [0, 1]

                    # use normal to produce a back-project mask
                    normal, _ = dr.interpolate(mesh.vn.unsqueeze(0).contiguous(), rast, mesh.fn)
                    normal = safe_normalize(normal[0])

                    # rotated normal (where [0, 0, 1] always faces camera)
                    rot_normal = normal @ pose[:3, :3]
                    viewcos = rot_normal[..., [2]]

                    mask = (alpha > 0) & (viewcos > 0.5)  # [H, W, 1]
                    mask = mask.view(-1)

                    uvs = uvs.view(-1, 2).clamp(0, 1)[mask]
                    rgbs = rgbs.view(3, -1).permute(1, 0)[mask].contiguous()
                    
                    # update texture image
                    cur_albedo, cur_cnt = mipmap_linear_grid_put_2d(
                        h, w,
                        uvs[..., [1, 0]] * 2 - 1,
                        rgbs,
                        min_resolution=256,
                        return_count=True,
                    )
                    
                    mask = cnt.squeeze(-1) < 0.1
                    albedo[mask] += cur_albedo[mask]
                    cnt[mask] += cur_cnt[mask]

                mask = cnt.squeeze(-1) > 0
                albedo[mask] = albedo[mask] / cnt[mask].repeat(1, 3)

                mask = mask.view(h, w)

                albedo = albedo.detach().cpu().numpy()
                mask = mask.detach().cpu().numpy()

                # dilate texture
                from sklearn.neighbors import NearestNeighbors
                from scipy.ndimage import binary_dilation, binary_erosion

                inpaint_region = binary_dilation(mask, iterations=32)
                inpaint_region[mask] = 0

                search_region = mask.copy()
                not_search_region = binary_erosion(search_region, iterations=3)
                search_region[not_search_region] = 0

                search_coords = np.stack(np.nonzero(search_region), axis=-1)
                inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)

                knn = NearestNeighbors(n_neighbors=1, algorithm="kd_tree").fit(
                    search_coords
                )
                _, indices = knn.kneighbors(inpaint_coords)

                albedo[tuple(inpaint_coords.T)] = albedo[tuple(search_coords[indices[:, 0]].T)]

                mesh.albedo = torch.from_numpy(albedo).to(self.device)
                mesh.write(path)

            
        elif mode == 'frames':
            os.makedirs(os.path.join(self.opt.outdir, self.opt.save_path+'_frames'), exist_ok=True)
            for t in range(self.opt.batch_size * interp):
                tt = t / interp
                path = os.path.join(self.opt.outdir, self.opt.save_path+'_frames', f'{t:03d}.ply')
                self.renderer.gaussians.save_frame_ply(path, tt)
        else:
            path = os.path.join(self.opt.outdir, self.opt.save_path + '_4d_model.ply')
            self.renderer.gaussians.save_ply(path)
            self.renderer.gaussians.save_deformation(self.opt.outdir, self.opt.save_path)

        print(f"[INFO] save model to {path}.")

    # no gui mode
    def train(self, iters=500, ui=False):
        if self.gui:
            from visualizer.visergui import ViserViewer
            self.viser_gui = ViserViewer(device="cuda", viewer_port=8080)
        if iters > 0:
            self.prepare_train()
            if self.gui:
                self.viser_gui.set_renderer(self.renderer, self.fixed_cam)
            
            for i in tqdm.trange(iters):
                self.train_step()
                if self.gui:
                    self.viser_gui.update()
        if self.opt.mesh_format == 'frames':
            self.save_model(mode='frames', interp=4)
        elif self.opt.mesh_format == 'obj':
            self.save_model(mode='geo+tex')
        
        if self.opt.save_model:
            self.save_model(mode='model')

        # render eval
        image_list =[]
        nframes = self.opt.batch_size * 7 + 15 * 7
        hor = 180
        delta_hor = 45 / 15
        delta_time = 1
        for i in range(8):
            time = 0
            for j in range(self.opt.batch_size + 15):
                pose = orbit_camera(self.opt.elevation, hor-180, self.opt.radius)
                cur_cam = MiniCam(
                    pose,
                    512,
                    512,
                    self.cam.fovy,
                    self.cam.fovx,
                    self.cam.near,
                    self.cam.far,
                    time=time
                )
                with torch.no_grad():
                    outputs = self.renderer.render(cur_cam)

                out = outputs["image"].cpu().detach().numpy().astype(np.float32)
                out = np.transpose(out, (1, 2, 0))
                out = np.uint8(out*255)
                image_list.append(out)

                time = (time + delta_time) % self.opt.batch_size
                if j >= self.opt.batch_size:
                    hor = (hor+delta_hor) % 360


        imageio.mimwrite(f'vis_data/{opt.save_path}.mp4', image_list, fps=7)

        if self.gui:
            while True:
                self.viser_gui.update()

if __name__ == "__main__":
    import argparse
    from omegaconf import OmegaConf

    parser = argparse.ArgumentParser()
    parser.add_argument("--config", required=True, help="path to the yaml config file")
    args, extras = parser.parse_known_args()

    # override default config from cli
    opt = OmegaConf.merge(OmegaConf.load(args.config), OmegaConf.from_cli(extras))
    opt.save_path = os.path.splitext(os.path.basename(opt.input))[0] if opt.save_path == '' else opt.save_path


    # auto find mesh from stage 1
    opt.load = os.path.join(opt.outdir, opt.save_path + '_model.ply')

    gui = GUI(opt)

    gui.train(opt.iters)


# python main_4d.py  --config configs/4d_low.yaml input=data/CONSISTENT4D_DATA/in-the-wild/blooming_rose