File size: 11,837 Bytes
204a554
 
 
19a149b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import sys
sys.path.append('.')

import cv2
import einops
import numpy as np
import torch
import random
import gradio as gr
import os
import albumentations as A
from PIL import Image
import torchvision.transforms as T
from datasets.data_utils import * 
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from omegaconf import OmegaConf
from cldm.hack import disable_verbosity, enable_sliced_attention
from huggingface_hub import snapshot_download

snapshot_download(repo_id="xichenhku/AnyDoor_models", local_dir="./AnyDoor_models")


cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)

save_memory = False
disable_verbosity()
if save_memory:
    enable_sliced_attention()


config = OmegaConf.load('./configs/demo.yaml')
model_ckpt =  config.pretrained_model
model_config = config.config_file




model = create_model(model_config ).cpu()
model.load_state_dict(load_state_dict(model_ckpt, location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)


def crop_back( pred, tar_image,  extra_sizes, tar_box_yyxx_crop):
    H1, W1, H2, W2 = extra_sizes
    y1,y2,x1,x2 = tar_box_yyxx_crop    
    pred = cv2.resize(pred, (W2, H2))
    m = 3 # maigin_pixel

    if W1 == H1:
        tar_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
        return tar_image

    if W1 < W2:
        pad1 = int((W2 - W1) / 2)
        pad2 = W2 - W1 - pad1
        pred = pred[:,pad1: -pad2, :]
    else:
        pad1 = int((H2 - H1) / 2)
        pad2 = H2 - H1 - pad1
        pred = pred[pad1: -pad2, :, :]
    tar_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
    return tar_image


def inference_single_image(ref_image, 
                           ref_mask, 
                           tar_image, 
                           tar_mask, 
                           num_samples, 
                           strength, 
                           ddim_steps, 
                           scale, 
                           seed, 
                           ):
    raw_background = tar_image.copy()
    item = process_pairs(ref_image, ref_mask, tar_image, tar_mask)

    ref = item['ref']
    hint = item['hint']
    num_samples = 1

    control = torch.from_numpy(hint.copy()).float().cuda() 
    control = torch.stack([control for _ in range(num_samples)], dim=0)
    control = einops.rearrange(control, 'b h w c -> b c h w').clone()


    clip_input = torch.from_numpy(ref.copy()).float().cuda() 
    clip_input = torch.stack([clip_input for _ in range(num_samples)], dim=0)
    clip_input = einops.rearrange(clip_input, 'b h w c -> b c h w').clone()

    H,W = 512,512

    cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning( clip_input )]}
    un_cond = {"c_concat": [control], 
               "c_crossattn": [model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]}
    shape = (4, H // 8, W // 8)

    if save_memory:
        model.low_vram_shift(is_diffusing=True)

    model.control_scales = ([strength] * 13)
    samples, _ = ddim_sampler.sample(ddim_steps, num_samples,
                                     shape, cond, verbose=False, eta=0,
                                     unconditional_guidance_scale=scale,
                                     unconditional_conditioning=un_cond)

    if save_memory:
        model.low_vram_shift(is_diffusing=False)

    x_samples = model.decode_first_stage(samples)
    x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy()

    result = x_samples[0][:,:,::-1]
    result = np.clip(result,0,255)

    pred = x_samples[0]
    pred = np.clip(pred,0,255)[1:,:,:]
    sizes = item['extra_sizes']
    tar_box_yyxx_crop = item['tar_box_yyxx_crop'] 
    tar_image = crop_back(pred, tar_image, sizes, tar_box_yyxx_crop) 

    # keep background unchanged
    y1,y2,x1,x2 = item['tar_box_yyxx']
    raw_background[y1:y2, x1:x2, :] = tar_image[y1:y2, x1:x2, :]
    return raw_background


def process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 0.8):
    # ========= Reference ===========
    # ref expand 
    ref_box_yyxx = get_bbox_from_mask(ref_mask)

    # ref filter mask 
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
    masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3)

    y1,y2,x1,x2 = ref_box_yyxx
    masked_ref_image = masked_ref_image[y1:y2,x1:x2,:]
    ref_mask = ref_mask[y1:y2,x1:x2]

    ratio = np.random.randint(11, 15) / 10 #11,13
    masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio)
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)

    # to square and resize
    masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False)
    masked_ref_image = cv2.resize(masked_ref_image.astype(np.uint8), (224,224) ).astype(np.uint8)

    ref_mask_3 = pad_to_square(ref_mask_3 * 255, pad_value = 0, random = False)
    ref_mask_3 = cv2.resize(ref_mask_3.astype(np.uint8), (224,224) ).astype(np.uint8)
    ref_mask = ref_mask_3[:,:,0]

    # collage aug 
    masked_ref_image_compose, ref_mask_compose =  masked_ref_image, ref_mask
    ref_mask_3 = np.stack([ref_mask_compose,ref_mask_compose,ref_mask_compose],-1)
    ref_image_collage = sobel(masked_ref_image_compose, ref_mask_compose/255)

    # ========= Target ===========
    tar_box_yyxx = get_bbox_from_mask(tar_mask)
    tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=[1.1,1.2]) #1.1  1.3
    tar_box_yyxx_full = tar_box_yyxx
    
    # crop
    tar_box_yyxx_crop =  expand_bbox(tar_image, tar_box_yyxx, ratio=[1.3, 3.0])   
    tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box
    y1,y2,x1,x2 = tar_box_yyxx_crop

    cropped_target_image = tar_image[y1:y2,x1:x2,:]
    cropped_tar_mask = tar_mask[y1:y2,x1:x2]

    tar_box_yyxx = box_in_box(tar_box_yyxx, tar_box_yyxx_crop)
    y1,y2,x1,x2 = tar_box_yyxx

    # collage
    ref_image_collage = cv2.resize(ref_image_collage.astype(np.uint8), (x2-x1, y2-y1))
    ref_mask_compose = cv2.resize(ref_mask_compose.astype(np.uint8), (x2-x1, y2-y1))
    ref_mask_compose = (ref_mask_compose > 128).astype(np.uint8)

    collage = cropped_target_image.copy() 
    collage[y1:y2,x1:x2,:] = ref_image_collage

    collage_mask = cropped_target_image.copy() * 0.0
    collage_mask[y1:y2,x1:x2,:] = 1.0
    collage_mask = np.stack([cropped_tar_mask,cropped_tar_mask,cropped_tar_mask],-1)

    # the size before pad
    H1, W1 = collage.shape[0], collage.shape[1]

    cropped_target_image = pad_to_square(cropped_target_image, pad_value = 0, random = False).astype(np.uint8)
    collage = pad_to_square(collage, pad_value = 0, random = False).astype(np.uint8)
    collage_mask = pad_to_square(collage_mask, pad_value = 0, random = False).astype(np.uint8)

    # the size after pad
    H2, W2 = collage.shape[0], collage.shape[1]

    cropped_target_image = cv2.resize(cropped_target_image.astype(np.uint8), (512,512)).astype(np.float32)
    collage = cv2.resize(collage.astype(np.uint8), (512,512)).astype(np.float32)
    collage_mask  = (cv2.resize(collage_mask.astype(np.uint8), (512,512)).astype(np.float32) > 0.5).astype(np.float32)

    masked_ref_image = masked_ref_image  / 255 
    cropped_target_image = cropped_target_image / 127.5 - 1.0
    collage = collage / 127.5 - 1.0 
    collage = np.concatenate([collage, collage_mask[:,:,:1]  ] , -1)
    
    item = dict(ref=masked_ref_image.copy(), jpg=cropped_target_image.copy(), hint=collage.copy(), 
                extra_sizes=np.array([H1, W1, H2, W2]), 
                tar_box_yyxx_crop=np.array( tar_box_yyxx_crop ),
                tar_box_yyxx=np.array(tar_box_yyxx_full),
                 ) 
    return item


ref_dir='./examples/Gradio/FG'
image_dir='./examples/Gradio/BG'
ref_list=[os.path.join(ref_dir,file) for file in os.listdir(ref_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file ]
ref_list.sort()
image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file]
image_list.sort()

def mask_image(image, mask):
    blanc = np.ones_like(image) * 255
    mask = np.stack([mask,mask,mask],-1) / 255
    masked_image = mask * ( 0.5 * blanc + 0.5 * image) + (1-mask) * image
    return masked_image.astype(np.uint8)

def run_local(base,
              ref,
              *args):
    image = base["image"].convert("RGB")
    mask = base["mask"].convert("L")
    ref_image = ref["image"].convert("RGB")
    ref_mask = ref["mask"].convert("L")
    image = np.asarray(image)
    mask = np.asarray(mask)
    mask = np.where(mask > 128, 255, 0).astype(np.uint8)
    ref_image = np.asarray(ref_image)
    ref_mask = np.asarray(ref_mask)
    ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8)

    processed_item = process_pairs(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), max_ratio = 0.8)
    masked_ref = (processed_item['ref']*255)

    mased_image = mask_image(image, mask)
    #synthesis = image
    synthesis = inference_single_image(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), *args)
    synthesis = torch.from_numpy(synthesis).permute(2, 0, 1)
    synthesis = synthesis.permute(1, 2, 0).numpy()

    masked_ref = cv2.resize(masked_ref.astype(np.uint8), (512,512))
    return [synthesis]



with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("#  Play with AnyDoor to Teleport your Target Objects! ")
        with gr.Row():
            baseline_gallery = gr.Gallery(label='Output', show_label=True, elem_id="gallery", columns=1, height=768)
            with gr.Accordion("Advanced Option", open=True):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=30, step=1)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=5.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=-1)
                gr.Markdown(" Higher guidance-scale makes higher fidelity, while lower guidance-scale leads to more harmonized blending.")
    




        gr.Markdown("# Upload / Select Images for the Background (left) and Reference Object (right)")
        gr.Markdown("### Your could draw coarse masks on the background to indicate the desired location and shape.")
        gr.Markdown("### <u>Do not forget</u> to annotate the target object on the reference image.")
        with gr.Row():
            base = gr.Image(label="Background", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5)
            ref = gr.Image(label="Reference", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5)
        run_local_button = gr.Button(label="Generate", value="Run")

        with gr.Row():
            with gr.Column():
                gr.Examples(image_list, inputs=[base],label="Examples - Background Image",examples_per_page=16)
            with gr.Column():
                gr.Examples(ref_list, inputs=[ref],label="Examples - Reference Object",examples_per_page=16)
        
    run_local_button.click(fn=run_local, 
                           inputs=[base, 
                                   ref, 
                                   num_samples, 
                                   strength, 
                                   ddim_steps, 
                                   scale, 
                                   seed, 
                                   ], 
                           outputs=[baseline_gallery]
                        )

demo.launch(server_name="0.0.0.0")