File size: 24,133 Bytes
37ee4a4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import numpy as np
import torch
from diffusers import  DDIMScheduler
import cv2
from utils.sdxl import sdxl
from utils.inversion import Inversion
import math
import torch.nn.functional as F
import utils.utils as utils
import os 
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import spaces

MAX_NUM_WORDS = 77


class LayerFusion:   
    def get_mask(self, maps, alpha, use_pool,x_t):
        k = 1
        maps = (maps * alpha).sum(-1).mean(1)
        if use_pool:
            maps = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
        mask = F.interpolate(maps, size=(x_t.shape[2:])) #[2, 1, 128, 128]
        mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
        mask=(mask - mask.min ()) / (mask.max () - mask.min ())
        mask = mask.gt(self.mask_threshold)
        self.mask=mask
        mask = mask[:1] + mask
        return mask 

    def get_one_mask(self, maps, use_pool, x_t, idx_lst, i=None, sav_img=False):
        k=1
        if sav_img is False:
            mask_tot = 0
            for obj in idx_lst:
                mask = maps[0, :, :, :, obj].mean(0).reshape(1, 1, 32, 32)
                if use_pool:
                    mask = F.max_pool2d(mask, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
                mask = F.interpolate(mask, size=(x_t.shape[2:]))
                mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
                mask=(mask - mask.min ()) / (mask.max () - mask.min ())
                mask = mask.gt(self.mask_threshold[int(self.counter/10)])
                mask_tot |= mask
            mask = mask_tot  
            return mask
        else: 
            for obj in idx_lst:
                mask = maps[0, :, :, :, obj].mean(0).reshape(1, 1, 32, 32)
                if use_pool:
                    mask = F.max_pool2d(mask, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
                mask = F.interpolate(mask, size=(1024, 1024))#[1, 1, 1024, 1024]
                mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
                mask=(mask - mask.min ()) / (mask.max () - mask.min ())
                mask = mask.gt(0.6)  
                mask = np.array(mask[0][0].clone().cpu()).astype(np.uint8)*255
                cv2.imwrite(f'./img/sam_mask/{self.blend_list[i][0]}_{self.counter}.jpg', mask)
        return mask

    def mv_op(self, mp, op, scale=0.2, ones=False, flip=None):
        _, b, H, W = mp.shape
        if ones == False:
            new_mp = torch.zeros_like(mp)
        else:
            new_mp = torch.ones_like(mp)
        K = int(scale*W)
        if op == 'right':
            new_mp[:, :, :, K:] = mp[:, :, :, 0:W-K]
        elif op == 'left':
            new_mp[:, :, :, 0:W-K] = mp[:, :, :, K:]
        elif op == 'down':
            new_mp[:, :, K:, :] = mp[:, :, 0:W-K, :]
        elif op == 'up':
            new_mp[:, :, 0:W-K, :] = mp[:, :, K:, :]
        if flip is not None:
            new_mp = torch.flip(new_mp, dims=flip)
               
        return new_mp

    def mv_layer(self, x_t, bg_id, fg_id, op_id):
        bg_img = x_t[bg_id:(bg_id+1)].clone()
        fg_img = x_t[fg_id:(fg_id+1)].clone()
        fg_mask = self.fg_mask_list[fg_id-3]
        op_list = self.op_list[fg_id-3]

        for item in op_list:
            op, scale = item[0], item[1]
            if scale != 0:
                fg_img = self.mv_op(fg_img, op=op, scale=scale)
                fg_mask = self.mv_op(fg_mask, op=op, scale=scale)
        x_t[op_id:(op_id+1)] = bg_img*(1-fg_mask) + fg_img*fg_mask

    def __call__(self, x_t):
        self.counter += 1
        # inpainting
        if self.blend_time[0] <= self.counter <= self.blend_time[1]:
            x_t[1:2] = x_t[1:2]*self.remove_mask + x_t[0:1]*(1-self.remove_mask) 

        if self.counter == self.blend_time[1] + 1 and self.mode != "removal":
            b = x_t.shape[0]
            bg_id = 1 #bg_layer
            op_id = 2 #canvas
            for fg_id in range(3, b): #fg_layer
                self.mv_layer(x_t, bg_id=bg_id, fg_id=fg_id, op_id=op_id)
                bg_id = op_id
    
        return x_t

    def __init__(self, remove_mask, fg_mask_list, refine_mask=None, 
                blend_time=[0, 40],
                 mode="removal", op_list=None):
        self.counter = 0
        self.mode = mode
        self.op_list = op_list
        self.blend_time = blend_time

        self.remove_mask = remove_mask
        self.refine_mask = refine_mask
        if self.refine_mask is not None:
            self.new_mask = self.remove_mask + self.refine_mask
            self.new_mask[self.new_mask>0] = 1
        else:
            self.new_mask = None
        self.fg_mask_list = fg_mask_list


class Control():
    def step_callback(self, x_t):
        if self.layer_fusion is not None:
             x_t = self.layer_fusion(x_t)
        return x_t
    def __init__(self, layer_fusion):
        self.layer_fusion = layer_fusion

def register_attention_control(model, controller, mask_time=[0, 40], refine_time=[0, 25]):
    def ca_forward(self, place_in_unet):
        to_out = self.to_out
        if type(to_out) is torch.nn.modules.container.ModuleList:
            to_out = self.to_out[0]
        else:
            to_out = self.to_out
        self.counter = 0 #time
        def forward(hidden_states, encoder_hidden_states=None, attention_mask=None): #self_attention
            x = hidden_states.clone() 
            context = encoder_hidden_states
            is_cross = context is not None
            if is_cross is False:
                if controller.layer_fusion is not None and (mask_time[0] < self.counter < mask_time[1]):
                    b, i, j = x.shape
                    H = W = int(math.sqrt(i))
                    x_old = x.clone()
                    x = x.reshape(b, H, W, j)
                    new_mask = controller.layer_fusion.remove_mask
                    if new_mask is not None:
                        new_mask[new_mask>0] = 1
                        new_mask = F.interpolate(new_mask.to(dtype=torch.float32).clone(), size=(H, W), mode='bilinear').cuda()
                        new_mask =  (1 - new_mask).reshape(1, H, W).unsqueeze(-1)
                        if (refine_time[0] < self.counter <= refine_time[1]) and controller.layer_fusion.refine_mask is not None:
                            new_mask = controller.layer_fusion.new_mask
                            new_mask = F.interpolate(new_mask.to(dtype=torch.float32).clone(), size=(H, W), mode='bilinear').cuda()
                            new_mask =  (1 - new_mask).reshape(1, H, W).unsqueeze(-1)                
                        idx = 1 #inpaiint_idx:bg
                        x[int(b/2)+idx, :, :] = (x[int(b/2)+idx, :, :]*new_mask[0])
                    x = x.reshape(b, i, j)
            if is_cross: 
                q = self.to_q(x) 
                k = self.to_k(context)
                v = self.to_v(context)
            else:
                context = x
                q = self.to_q(hidden_states) 
                k = self.to_k(x) 
                v = self.to_v(hidden_states)
            q = self.head_to_batch_dim(q)
            k = self.head_to_batch_dim(k)
            v = self.head_to_batch_dim(v)

            if hasattr(controller, 'count_layers'):
                controller.count_layers(place_in_unet,is_cross)
            sim = torch.einsum("b i d, b j d -> b i j", q.clone(), k.clone()) * self.scale 

            attn = sim.softmax(dim=-1)
            out = torch.einsum("b i j, b j d -> b i d", attn, v)
            out = self.batch_to_head_dim(out)
            global global_cnt
            self.counter += 1
            return to_out(out)
        
        return forward

    def register_recr(net_, count, place_in_unet):
        if net_.__class__.__name__ == 'Attention':
            net_.forward = ca_forward(net_, place_in_unet)
            return count + 1
        elif hasattr(net_, 'children'):
            for net__ in net_.children():
                count = register_recr(net__, count, place_in_unet)
        return count

    cross_att_count = 0
    sub_nets = model.unet.named_children()
    for net in sub_nets:
        if "down" in net[0]:
            cross_att_count += register_recr(net[1], 0, "down")
        elif "up" in net[0]:
            cross_att_count += register_recr(net[1], 0, "up")
        elif "mid" in net[0]:
            cross_att_count += register_recr(net[1], 0, "mid")

    controller.num_att_layers = cross_att_count

class DesignEdit():
    def __init__(self, pretrained_model_path="/home/jyr/model/stable-diffusion-xl-base-1.0"):
        self.model_dtype = "fp16"
        self.pretrained_model_path=pretrained_model_path
        self.num_ddim_steps = 50
        self.mask_time = [0, 40]
        self.op_list = {}
        self.attend_scale = {}
        scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
        if self.model_dtype == "fp16":
            torch_dtype = torch.float16
        elif self.model_dtype == "fp32":
            torch_dtype = torch.float32
        self.pipe = sdxl.from_pretrained(self.pretrained_model_path, torch_dtype=torch_dtype, use_safetensors=True, variant=self.model_dtype,scheduler=scheduler)
   
    @spaces.GPU
    def init_model(self, num_ddim_steps=50):
        device = torch.device('cuda:0')
        self.pipe.to(device)
        inversion = Inversion(self.pipe,num_ddim_steps)
        return self.pipe, inversion
    
    @spaces.GPU(duration=120, enable_queue=True)
    def run_remove(self, original_image=None, mask_1=None, mask_2=None, mask_3=None, refine_mask=None, 
        ori_1=None, ori_2=None, ori_3=None,
        prompt="", save_dir="./tmp", mode='removal',):
        # 01-1: 
        self.ldm_model, self.inversion= self.init_model(num_ddim_steps=self.num_ddim_steps)
        if original_image is None:
            original_image = ori_1 if ori_1 is not None else ori_2 if ori_2 is not None else ori_3
        op_list = None
        attend_scale = 20
        sample_ref_match={0 : 0, 1 : 0}
        ori_shape = original_image.shape

        # 01-2: prepare: image_gt, remove_mask, fg_mask_list, refine_mask
        image_gt = Image.fromarray(original_image).resize((1024, 1024))
        image_gt = np.stack([np.array(image_gt)])
        mask_list = [mask_1, mask_2, mask_3]
        remove_mask = utils.attend_mask(utils.add_masks_resized(mask_list), attend_scale=attend_scale) # numpy to tensor
        fg_mask_list = None
        refine_mask = utils.attend_mask(utils.convert_and_resize_mask(refine_mask)) if refine_mask is not None else None

        # 01-3: prepare: prompts, blend_time, refine_time
        prompts = len(sample_ref_match)*[prompt] # 2
        blend_time = [0, 41]
        refine_time = [0, 25]
        
        # 02: invert
        _, x_t, x_stars, prompt_embeds, pooled_prompt_embeds = self.inversion.invert(image_gt, prompts, inv_batch_size=1)
        
        # 03: init layer_fusion and controller
        lb = LayerFusion(remove_mask=remove_mask, fg_mask_list=fg_mask_list, refine_mask=refine_mask,
                    blend_time=blend_time, mode=mode, op_list=op_list)
        controller = Control(layer_fusion=lb)
        register_attention_control(model=self.ldm_model, controller=controller, mask_time=self.mask_time, refine_time=refine_time)
        
        # 04: generate images
        images = self.ldm_model(controller=controller, prompt=prompts,
                        latents=x_t, x_stars=x_stars,  
                        negative_prompt_embeds=prompt_embeds, 
                        negative_pooled_prompt_embeds=pooled_prompt_embeds,
                        sample_ref_match=sample_ref_match)
        folder = None
        utils.view_images(images, folder=folder)
        return [cv2.resize(images[1], (ori_shape[1], ori_shape[0]))]
    
    @spaces.GPU(duration=120, enable_queue=True)
    def run_zooming(self, original_image, width_scale=1, height_scale=1, prompt="", save_dir="./tmp", mode='removal'):
        self.ldm_model, self.inversion= self.init_model(num_ddim_steps=self.num_ddim_steps)
        # 01-1: 
        op_list = {0: ['zooming', [height_scale, width_scale]]}
        ori_shape = original_image.shape
        attend_scale = 30
        sample_ref_match = {0 : 0, 1 : 0}

        # 01-2: prepare: image_gt, remove_mask, fg_mask_list, refine_mask
        img_new, mask = utils.zooming(original_image, [height_scale, width_scale])
        img_new_copy = img_new.copy()
        mask_copy = mask.copy()
        
        image_gt = Image.fromarray(img_new).resize((1024, 1024))
        image_gt = np.stack([np.array(image_gt)])

        remove_mask = utils.attend_mask(utils.convert_and_resize_mask(mask), attend_scale=attend_scale) # numpy to tensor
        fg_mask_list = None
        refine_mask = None

        # 01-3: prepare: prompts, blend_time, refine_time
        prompts = len(sample_ref_match)*[prompt] # 2
        blend_time = [0, 41]
        refine_time = [0, 25]

        # 02: invert
        _, x_t, x_stars, prompt_embeds, pooled_prompt_embeds = self.inversion.invert(image_gt, prompts, inv_batch_size=1)
        
        # 03: init layer_fusion and controller
        lb = LayerFusion(remove_mask=remove_mask, fg_mask_list=fg_mask_list, blend_time=blend_time,
                    mode=mode, op_list=op_list)
        controller = Control(layer_fusion=lb)
        register_attention_control(model=self.ldm_model, controller=controller, mask_time=self.mask_time, refine_time=refine_time)
        
        # 04: generate images
        images = self.ldm_model(controller=controller, prompt=prompts,
                        latents=x_t, x_stars=x_stars,  
                        negative_prompt_embeds=prompt_embeds, 
                        negative_pooled_prompt_embeds=pooled_prompt_embeds,
                        sample_ref_match=sample_ref_match)
        folder = None
        utils.view_images(images, folder=folder)
        resized_img = cv2.resize(images[1], (ori_shape[1], ori_shape[0]))
        return [resized_img], [img_new_copy], [mask_copy]
    
    @spaces.GPU(duration=120, enable_queue=True)
    def run_panning(self, original_image, w_direction, w_scale, h_direction, h_scale, prompt="", save_dir="./tmp", mode='removal'):
        # 01-1: prepare: op_list, attend_scale, sample_ref_match
        self.ldm_model, self.inversion= self.init_model(num_ddim_steps=self.num_ddim_steps)
        ori_shape = original_image.shape
        attend_scale = 30
        sample_ref_match = {0 : 0, 1 : 0}

        # 01-2: prepare: image_gt, remove_mask, fg_mask_list, refine_mask
        op_list = [[w_direction, w_scale], [h_direction, h_scale]]
        img_new, mask = utils.panning(original_image, op_list=op_list)
        img_new_copy = img_new.copy()
        mask_copy = mask.copy()
        
        image_gt = Image.fromarray(img_new).resize((1024, 1024))
        image_gt = np.stack([np.array(image_gt)])
        remove_mask = utils.attend_mask(utils.convert_and_resize_mask(mask), attend_scale=attend_scale) # numpy to tensor

        fg_mask_list = None
        refine_mask = None

        # 01-3: prepare: prompts, blend_time, refine_time
        prompts = len(sample_ref_match)*[prompt] # 2
        blend_time = [0, 41]
        refine_time = [0, 25]

        # 02: invert
        _, x_t, x_stars, prompt_embeds, pooled_prompt_embeds = self.inversion.invert(image_gt, prompts, inv_batch_size=1)
        # 03: init layer_fusion and controller
        lb = LayerFusion(remove_mask=remove_mask, fg_mask_list=fg_mask_list, blend_time=blend_time,
                    mode=mode, op_list=op_list)
        controller = Control(layer_fusion=lb)
        register_attention_control(model=self.ldm_model, controller=controller, mask_time=self.mask_time, refine_time=refine_time)
        
        # 04: generate images

        images = self.ldm_model(controller=controller, prompt=prompts,
                        latents=x_t, x_stars=x_stars,  
                        negative_prompt_embeds=prompt_embeds, 
                        negative_pooled_prompt_embeds=pooled_prompt_embeds,
                        sample_ref_match=sample_ref_match)
        folder = None
        utils.view_images(images, folder=folder)
        resized_img = cv2.resize(images[1], (ori_shape[1], ori_shape[0]))
        return [resized_img], [img_new_copy], [mask_copy]

    # layer-wise multi-object editing
    def process_layer_states(self, layer_states):
        self.ldm_model, self.inversion= self.init_model(num_ddim_steps=self.num_ddim_steps)
        image_paths = []
        mask_paths = []
        op_list = []
        
        for state in layer_states:
            img, mask, dx, dy, resize, w_flip, h_flip = state
            if img is not None:  
                img = cv2.resize(img, (1024, 1024))
                mask = utils.convert_and_resize_mask(mask)
                dx_command = ['right', dx] if dx > 0 else ['left', -dx]
                dy_command = ['up', dy] if dy > 0 else ['down', -dy]
                flip_code = None
                if w_flip == "left/right" and h_flip == "down/up":
                    flip_code = -1
                elif w_flip == "left/right":
                    flip_code = 1  # 或者其他默认值,根据您的需要设置
                elif h_flip == "down/up":
                    flip_code = 0
                op_list.append([dx_command, dy_command])
                img, mask, _ = utils.resize_image_with_mask(img, mask, resize)
                img, mask, _ = utils.flip_image_with_mask(img, mask, flip_code=flip_code)
                image_paths.append(img)
                mask_paths.append(utils.attend_mask(mask))
        sample_ref_match = {0: 0, 1: 0, 2: 0, 3: 1, 4: 2, 5: 3}
        required_length = len(image_paths) + 3
        truncated_sample_ref_match = {k: sample_ref_match[k] for k in sorted(sample_ref_match.keys())[:required_length]}
        return image_paths, mask_paths, op_list, truncated_sample_ref_match

    @spaces.GPU(duration=200)
    def run_layer(self, bg_img, l1_img, l1_dx, l1_dy, l1_resize, l1_w_flip, l1_h_flip, 
        l2_img, l2_dx, l2_dy, l2_resize, l2_w_flip, l2_h_flip,
        l3_img, l3_dx, l3_dy, l3_resize, l3_w_flip, l3_h_flip,
        bg_mask, l1_mask, l2_mask, l3_mask,
        bg_ori=None, l1_ori=None, l2_ori=None, l3_ori=None,
        prompt="", save_dir="./tmp", mode='layerwise'):
        self.ldm_model, self.inversion= self.init_model(num_ddim_steps=self.num_ddim_steps)
        # 00: prepare: layer-wise states
        bg_img = bg_ori if bg_ori is not None else bg_img
        l1_img = l1_ori if l1_ori is not None else l1_img
        l2_img = l2_ori if l2_ori is not None else l2_img
        l3_img = l3_ori if l3_ori is not None else l3_img
        for mask in [bg_mask, l1_mask, l2_mask, l3_mask]:
            if mask is None:
                mask = np.zeros((1024, 1024), dtype=np.uint8)
            else:
                mask = utils.convert_and_resize_mask(mask)
        l1_state = [l1_img, l1_mask, l1_dx, l1_dy, l1_resize, l1_w_flip, l1_h_flip]
        l2_state = [l2_img, l2_mask, l2_dx, l2_dy, l2_resize, l2_w_flip, l2_h_flip]
        l3_state = [l3_img, l3_mask, l3_dx, l3_dy, l3_resize, l3_w_flip, l3_h_flip]
        ori_shape = bg_img.shape

        image_paths, fg_mask_list, op_list, sample_ref_match = self.process_layer_states([l1_state, l2_state, l3_state])
        if image_paths == []:
            mode = "removal"
        # 01-1: prepare: image_gt, remove_mask, fg_mask_list, refine_mask
        attend_scale = 20
        image_gt = [bg_img] + image_paths
        image_gt = [Image.fromarray(img).resize((1024, 1024)) for img in image_gt]
        image_gt = np.stack(image_gt)      
        remove_mask = utils.attend_mask(bg_mask, attend_scale=attend_scale)
        refine_mask = None

        # 01-2: prepare: promptrun_masks, blend_time, refine_time
        prompts = len(sample_ref_match)*[prompt] # 2
        blend_time = [0, 41]
        refine_time = [0, 25]
        attend_scale = []

        # 02: invert
        _, x_t, x_stars, prompt_embeds, pooled_prompt_embeds = self.inversion.invert(image_gt, prompts, inv_batch_size=len(image_gt))
        # 03: init layer_fusion and controller
        lb = LayerFusion(remove_mask=remove_mask, fg_mask_list=fg_mask_list, blend_time=blend_time, refine_mask=refine_mask,
                    mode=mode, op_list=op_list)
        controller = Control(layer_fusion=lb)
        register_attention_control(model=self.ldm_model, controller=controller, mask_time=self.mask_time, refine_time=refine_time)
        # 04: generate images
        images = self.ldm_model(controller=controller, prompt=prompts,
                        latents=x_t, x_stars=x_stars,  
                        negative_prompt_embeds=prompt_embeds, 
                        negative_pooled_prompt_embeds=pooled_prompt_embeds,
                        sample_ref_match=sample_ref_match)
        folder = None
        utils.view_images(images, folder=folder) 
        if mode == 'removal':
            resized_img = cv2.resize(images[1], (ori_shape[1], ori_shape[0]))       
        else:
            resized_img = cv2.resize(images[2], (ori_shape[1], ori_shape[0]))       
        return [resized_img]

    @spaces.GPU(duration=120, enable_queue=True)
    def run_moving(self, bg_img, bg_ori, bg_mask, l1_dx, l1_dy, l1_resize, 
        l1_w_flip=None, l1_h_flip=None, selected_points=None,
        prompt="", save_dir="./tmp", mode='layerwise'):
        self.ldm_model, self.inversion= self.init_model(num_ddim_steps=self.num_ddim_steps)
        # 00: prepare: layer-wise states
        bg_img = bg_ori if bg_ori is not None else bg_img
        l1_img = bg_img
        if bg_mask is None:
            bg_mask = np.zeros((1024, 1024), dtype=np.uint8)
        else:
            bg_mask = utils.convert_and_resize_mask(bg_mask)
        l1_mask = bg_mask
        l1_state = [l1_img, l1_mask, l1_dx, l1_dy, l1_resize, l1_w_flip, l1_h_flip]
        ori_shape = bg_img.shape

        image_paths, fg_mask_list, op_list, sample_ref_match = self.process_layer_states([l1_state])

        # 01-1: prepare: image_gt, remove_mask, fg_mask_list, refine_mask
        attend_scale = 20
        image_gt = [bg_img] + image_paths
        image_gt = [Image.fromarray(img).resize((1024, 1024)) for img in image_gt]
        image_gt = np.stack(image_gt)      
        remove_mask = utils.attend_mask(bg_mask, attend_scale=attend_scale)
        refine_mask = None

        # 01-2: prepare: promptrun_masks, blend_time, refine_time
        prompts = len(sample_ref_match)*[prompt] # 2
        blend_time = [0, 41]
        refine_time = [0, 25]
        attend_scale = []

        # 02: invert
        _, x_t, x_stars, prompt_embeds, pooled_prompt_embeds = self.inversion.invert(image_gt, prompts, inv_batch_size=len(image_gt))
        # 03: init layer_fusion and controller
        lb = LayerFusion(remove_mask=remove_mask, fg_mask_list=fg_mask_list, blend_time=blend_time, refine_mask=refine_mask,
                    mode=mode, op_list=op_list)
        controller = Control(layer_fusion=lb)
        register_attention_control(model=self.ldm_model, controller=controller, mask_time=self.mask_time, refine_time=refine_time)
        # 04: generate images
        images = self.ldm_model(controller=controller, prompt=prompts,
                        latents=x_t, x_stars=x_stars,  
                        negative_prompt_embeds=prompt_embeds, 
                        negative_pooled_prompt_embeds=pooled_prompt_embeds,
                        sample_ref_match=sample_ref_match)
        folder = None
        utils.view_images(images, folder=folder) 
        resized_img = cv2.resize(images[2], (ori_shape[1], ori_shape[0]))       
        return [resized_img]

    # turn mask to 1024x1024 unit-8
    def run_mask(self, mask_1, mask_2, mask_3, mask_4):
        mask_list = [mask_1, mask_2, mask_3, mask_4]
        final_mask = utils.add_masks_resized(mask_list)
        return final_mask