File size: 14,876 Bytes
2f72267
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d9fca68
 
2f72267
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
effdf42
2f72267
 
 
d9fca68
2f72267
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d9fca68
2f72267
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d9fca68
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
from functools import partial
import os
from PIL import Image, ImageOps
import random

import cv2
from diffusers.models import AutoencoderKL
import gradio as gr
import numpy as np
from segment_anything import build_sam, SamPredictor
from tqdm import tqdm
from transformers import CLIPModel, AutoProcessor, CLIPVisionModel
import torch
from torchvision import transforms

from diffusion import create_diffusion
from model import UNet2DDragConditionModel

import spaces


TITLE = '''DragAPart: Learning a Part-Level Motion Prior for Articulated Objects'''
DESCRIPTION = """
<div>
Try <a href='https://arxiv.org/abs/24xx.xxxxx'><b>DragAPart</b></a> yourself to manipulate your favorite articulated objects in 2 seconds!
</div>
"""
INSTRUCTION = '''
2 steps to get started:
- Upload an image of an articulated object.
- Add one or more drags on the object to specify the part-level interactions.

How to add drags:
- To add a drag, first click on the starting point of the drag, then click on the ending point of the drag, on the Input Image (leftmost).
- You can add up to 10 drags, but we suggest one drag per part.
- After every click, the drags will be visualized on the Image with Drags (second from left).
- If the last drag is not completed (you specified the starting point but not the ending point), it will simply be ignored.
- Have fun dragging!

Then, you will be prompted to verify the object segmentation. Once you confirm that the segmentation is decent, the output image will be generated in seconds!
'''
PREPROCESS_INSTRUCTION = '''
Segmentation is needed if it is not already provided through an alpha channel in the input image.
You don't need to tick this box if you have chosen one of the example images.
If you have uploaded one of your own images, it is very likely that you will need to tick this box.
You should verify that the preprocessed image is object-centric (i.e., clearly contains a single object) and has white background.
'''

def center_and_square_image(pil_image_rgba, drags):
    image = pil_image_rgba
    alpha = np.array(image)[:, :, 3]  # Extract the alpha channel

    cy, cx = np.round(np.mean(np.nonzero(alpha), axis=1)).astype(int)
    side_length = max(image.width, image.height)
    padded_image = ImageOps.expand(
        image, 
        (side_length // 2, side_length // 2, side_length // 2, side_length // 2), 
        fill=(255, 255, 255, 255)
    )
    left, top = cx, cy
    new_drags = []
    for d in drags:
        x, y = d
        new_x, new_y = (x + side_length // 2 - cx) / side_length, (y + side_length // 2 - cy) / side_length
        new_drags.append((new_x, new_y))

    # Crop or pad the image as needed to make it centered around (cx, cy)
    image = padded_image.crop((left, top, left + side_length, top + side_length))
    # Resize the image to 256x256
    image = image.resize((256, 256), Image.Resampling.LANCZOS)
    return image, new_drags

def sam_init():
    sam_checkpoint = os.path.join(os.path.dirname(__file__), "ckpts", "sam_vit_h_4b8939.pth")
    predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to("cuda"))
    return predictor

def model_init():
    model_checkpoint = os.path.join(os.path.dirname(__file__), "ckpts", "drag-a-part-final.pt")
    model = UNet2DDragConditionModel.from_pretrained_sd(
        os.path.join(os.path.dirname(__file__), "ckpts", "stable-diffusion-v1-5"),
        unet_additional_kwargs=dict(
            sample_size=32,
            flow_original_res=False,
            input_concat_dragging=False,
            attn_concat_dragging=True,
            use_drag_tokens=False,
            single_drag_token=False,
            one_sided_attn=True,
            flow_in_old_version=False,
        ),
        load=False,
    )
    model.load_state_dict(torch.load(model_checkpoint, map_location="cpu")["model"])
    model = model.to("cuda")
    return model

@spaces.GPU
def sam_segment(predictor, input_image, drags, foreground_points=None):
    image = np.asarray(input_image)
    predictor.set_image(image)

    with torch.no_grad():
        masks_bbox, _, _ = predictor.predict(
            point_coords=foreground_points if foreground_points is not None else None,
            point_labels=np.ones(len(foreground_points)) if foreground_points is not None else None,
            multimask_output=True
        )

    out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
    out_image[:, :, :3] = image
    out_image[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
    torch.cuda.empty_cache()
    out_image, new_drags = center_and_square_image(Image.fromarray(out_image, mode="RGBA"), drags)

    return out_image, new_drags

def get_point(img, sel_pix, evt: gr.SelectData):
    sel_pix.append(evt.index)
    points = []
    img = np.array(img)
    height = img.shape[0]
    arrow_width_large = 7 * height // 256
    arrow_width_small = 3 * height // 256
    circle_size = 5 * height // 256

    with_alpha = img.shape[2] == 4
    for idx, point in enumerate(sel_pix):
        if idx % 2 == 1:
            cv2.circle(img, tuple(point), circle_size, (0, 0, 255, 255) if with_alpha else (0, 0, 255), -1)
        else:
            cv2.circle(img, tuple(point), circle_size, (255, 0, 0, 255) if with_alpha else (255, 0, 0), -1)
        points.append(tuple(point))
        if len(points) == 2:
            cv2.arrowedLine(img, points[0], points[1], (0, 0, 0, 255) if with_alpha else (0, 0, 0), arrow_width_large)
            cv2.arrowedLine(img, points[0], points[1], (255, 255, 0, 255) if with_alpha else (0, 0, 0), arrow_width_small)
            points = []
    return img if isinstance(img, np.ndarray) else np.array(img)

def clear_drag():
    return []

def preprocess_image(SAM_predictor, img, chk_group, drags):
    if img is None:
        gr.Warning("No image is specified. Please specify an image before preprocessing.")
        return None, drags

    if drags is None or len(drags) == 0:
        foreground_points = None
    else:
        foreground_points = np.array([drags[i] for i in range(0, len(drags), 2)])

    if len(drags) == 0:
        gr.Warning("No drags are specified. We recommend first specifying the drags before preprocessing.")

    new_drags = drags
    if "Preprocess with Segmentation" in chk_group:
        img_np = np.array(img)
        rgb_img = img_np[..., :3]
        img, new_drags = sam_segment(
            SAM_predictor,
            rgb_img,
            drags,
            foreground_points=foreground_points,
        )
    else:
        new_drags = [(d[0] / img.width, d[1] / img.height) for d in drags]

    img = np.array(img).astype(np.float32)
    processed_img = img[..., :3] * img[..., 3:] / 255. + 255. * (1 - img[..., 3:] / 255.)
    image_pil = Image.fromarray(processed_img.astype(np.uint8), mode="RGB")
    processed_img = image_pil.resize((256, 256), Image.LANCZOS)
    return processed_img, new_drags

@spaces.GPU
def single_image_sample(
    model,
    diffusion,
    x_cond,
    x_cond_clip,
    rel,
    cfg_scale,
    x_cond_extra,
    drags,
    hidden_cls,
    num_steps=50,
):
    z = torch.randn(2, 4, 32, 32).to("cuda")

    # Prepare input for classifer-free guidance
    rel = torch.cat([rel, rel], dim=0)
    x_cond = torch.cat([x_cond, x_cond], dim=0)
    x_cond_clip = torch.cat([x_cond_clip, x_cond_clip], dim=0)
    x_cond_extra = torch.cat([x_cond_extra, x_cond_extra], dim=0)
    drags = torch.cat([drags, drags], dim=0)
    hidden_cls = torch.cat([hidden_cls, hidden_cls], dim=0)

    model_kwargs = dict(
        x_cond=x_cond,
        x_cond_extra=x_cond_extra,
        cfg_scale=cfg_scale,
        hidden_cls=hidden_cls,
        drags=drags,
    )

    # Denoising
    step_delta = diffusion.num_timesteps // num_steps
    for i in tqdm(range(num_steps)):
        with torch.no_grad():
            samples = diffusion.p_sample(
                model.forward_with_cfg,
                z,
                torch.Tensor([diffusion.num_timesteps - 1 - step_delta * i]).long().to("cuda").repeat(z.shape[0]),
                clip_denoised=False,
                model_kwargs=model_kwargs,
            )["pred_xstart"]
            if i != num_steps - 1:
                z = diffusion.q_sample(
                    samples, 
                    torch.Tensor([diffusion.num_timesteps - 1 - step_delta * i]).long().to("cuda").repeat(z.shape[0])
                )

        samples, _ = samples.chunk(2, dim=0)
    return samples

def generate_image(model, image_processor, vae, clip_model, clip_vit, diffusion, img_cond, seed, cfg_scale, drags_list):
    if img_cond is None:
        gr.Warning("Please preprocess the image first.")
        return None

    with torch.no_grad():
        torch.manual_seed(seed)
        np.random.seed(seed)
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        random.seed(seed)

        pixels_cond = transforms.ToTensor()(img_cond.astype(np.float32) / 127.5 - 1).unsqueeze(0).to("cuda")

        cond_pixel_preprocessed_for_clip = image_processor(
            images=Image.fromarray(img_cond), return_tensors="pt"
        ).pixel_values.to("cuda")
        with torch.no_grad():
            x_cond = vae.encode(pixels_cond).latent_dist.sample().mul_(0.18215)
            cond_clip_features = clip_model.get_image_features(cond_pixel_preprocessed_for_clip)
            cls_embedding = torch.stack(
                clip_vit(pixel_values=cond_pixel_preprocessed_for_clip, output_hidden_states=True).hidden_states,
                dim=1
            )[:, :, 0]

        # dummies
        rel = torch.zeros(1, 4).to("cuda")
        x_cond_extra = torch.zeros(1, 3, 32, 32).to("cuda")

        drags = torch.zeros(1, 10, 4).to("cuda")
        for i in range(0, len(drags_list), 2):
            if i + 1 == len(drags_list):
                gr.Warning("The ending point of the last drag is not specified. The last drag is ignored.")
                break

            idx = i // 2
            drags[0, idx, 0], drags[0, idx, 1], drags[0, idx, 2], drags[0, idx, 3] = \
                drags_list[i][0], drags_list[i][1], drags_list[i + 1][0], drags_list[i + 1][1]

            if idx == 9:
                break

        samples = single_image_sample(
            model,
            diffusion,
            x_cond,
            cond_clip_features,
            rel,
            cfg_scale,
            x_cond_extra,
            drags,
            cls_embedding,
            num_steps=50,
        )

        with torch.no_grad():
            images = vae.decode(samples / 0.18215).sample
        images = ((images + 1)[0].permute(1, 2, 0) * 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
        return images


sam_predictor = sam_init()
model = model_init()

vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-ema").to('cuda')
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to('cuda')
clip_vit = CLIPVisionModel.from_pretrained("openai/clip-vit-large-patch14").to('cuda')
image_processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
diffusion = create_diffusion(
    timestep_respacing="",
    learn_sigma=False,
)

with gr.Blocks(title=TITLE) as demo:
    gr.Markdown("# " + DESCRIPTION)

    with gr.Row():
        gr.Markdown(INSTRUCTION)
    
    drags = gr.State(value=[])

    with gr.Row(variant="panel"):
        with gr.Column(scale=1):
            input_image = gr.Image(
                interactive=True,
                type='pil',
                image_mode="RGBA",
                width=256,
                show_label=True,
                label="Input Image",
            )

            example_folder = os.path.join(os.path.dirname(__file__), "./example_images")
            example_fns = [os.path.join(example_folder, example) for example in sorted(os.listdir(example_folder))]
            gr.Examples(
                examples=example_fns,
                inputs=[input_image],
                cache_examples=False,
                label='Feel free to use one of our provided examples!',
                examples_per_page=30
            )

            input_image.change(
                fn=clear_drag,
                outputs=[drags],
            )

        with gr.Column(scale=1):
            drag_image = gr.Image(
                type="numpy",
                label="Image with Drags",
                interactive=False,
                width=256,
                image_mode="RGB",
            )

            input_image.select(
                fn=get_point,
                inputs=[input_image, drags],
                outputs=[drag_image],
            )
        
        with gr.Column(scale=1):
            processed_image = gr.Image(
                type='numpy', 
                label="Processed Image", 
                interactive=False, 
                width=256,
                height=256,
                image_mode='RGB',
            )
            processed_image_highres = gr.Image(type='pil', image_mode='RGB', visible=False)

            with gr.Accordion('Advanced preprocessing options', open=True):
                with gr.Row():
                    with gr.Column():
                        preprocess_chk_group = gr.CheckboxGroup(
                            ['Preprocess with Segmentation'], 
                            label='Segment',
                            info=PREPROCESS_INSTRUCTION
                        )
            
            preprocess_button = gr.Button(
                value="Preprocess Input Image",
            )
            preprocess_button.click(
                fn=partial(preprocess_image, sam_predictor),
                inputs=[input_image, preprocess_chk_group, drags],
                outputs=[processed_image, drags],
                queue=True,
            )

        with gr.Column(scale=1):
            generated_image = gr.Image(
                type="numpy",
                label="Generated Image",
                interactive=False,
                height=256,
                width=256,
                image_mode="RGB",
            )

            with gr.Accordion('Advanced generation options', open=True):
                with gr.Row():
                    with gr.Column():
                        seed = gr.Slider(label="seed", value=0, minimum=0, maximum=10000, step=1, randomize=False)
                        cfg_scale = gr.Slider(
                            label="classifier-free guidance weight",
                            value=5, minimum=1, maximum=10, step=0.1
                        )

            generate_button = gr.Button(
                value="Generate Image",
            )
            generate_button.click(
                fn=partial(generate_image, model, image_processor, vae, clip_model, clip_vit, diffusion),
                inputs=[processed_image, seed, cfg_scale, drags],
                outputs=[generated_image],
            )

    demo.launch()