File size: 12,882 Bytes
9a947d8
 
 
 
 
 
 
 
 
 
 
 
 
4ccfc8b
a0fdd41
9a947d8
 
 
b00b3bb
9a947d8
 
a0fdd41
9a947d8
 
 
 
 
 
 
 
a0fdd41
9a947d8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a0fdd41
9a947d8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fd49e19
9a947d8
 
 
 
fd49e19
 
a0fdd41
9a947d8
 
fd49e19
4ccfc8b
9a947d8
 
fd49e19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9a947d8
 
75eb903
9a947d8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a0fdd41
9a947d8
a0fdd41
fd49e19
23a8e9a
 
 
 
 
 
 
 
 
fd49e19
 
9a947d8
b00b3bb
 
 
 
 
 
 
fd49e19
 
b00b3bb
 
 
fd49e19
b00b3bb
 
9f38f01
fd49e19
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9f38f01
9e0db6d
9a947d8
a0fdd41
fd49e19
 
a0fdd41
9a947d8
b00b3bb
 
 
 
fd49e19
 
 
 
 
34c96bd
fd49e19
 
34c96bd
 
fd49e19
 
9f38f01
9a947d8
 
4050bb1
 
9a947d8
 
 
4050bb1
9a947d8
 
 
 
 
 
 
 
2293af8
9a947d8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b00b3bb
9a947d8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import spaces

import os
import imageio
import numpy as np
import torch
import rembg
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from einops import rearrange, repeat
from tqdm import tqdm
import threading
from queue import SimpleQueue
from typing import Any
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
import rerun as rr
import rerun.blueprint as rrb
from gradio_rerun import Rerun

import src
from src.utils.train_util import instantiate_from_config
from src.utils.camera_util import (
    FOV_to_intrinsics, 
    get_zero123plus_input_cameras,
    get_circular_camera_poses,
)
from src.utils.mesh_util import save_obj, save_glb
from src.utils.infer_util import remove_background, resize_foreground, images_to_video
from src.models.lrm_mesh import InstantMesh

import tempfile
from functools import partial

from huggingface_hub import hf_hub_download

import gradio as gr


def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
    """
    Get the rendering camera parameters.
    """
    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
    if is_flexicubes:
        cameras = torch.linalg.inv(c2ws)
        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
    else:
        extrinsics = c2ws.flatten(-2)
        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
    return cameras


def images_to_video(images, output_path, fps=30):
    # images: (N, C, H, W)
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    frames = []
    for i in range(images.shape[0]):
        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
        assert frame.min() >= 0 and frame.max() <= 255, \
            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
        frames.append(frame)
    imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')


###############################################################################
# Configuration.
###############################################################################

import shutil

def find_cuda():
    # Check if CUDA_HOME or CUDA_PATH environment variables are set
    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')

    if cuda_home and os.path.exists(cuda_home):
        return cuda_home

    # Search for the nvcc executable in the system's PATH
    nvcc_path = shutil.which('nvcc')

    if nvcc_path:
        # Remove the 'bin/nvcc' part to get the CUDA installation path
        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
        return cuda_path

    return None

cuda_path = find_cuda()

if cuda_path:
    print(f"CUDA installation found at: {cuda_path}")
else:
    print("CUDA installation not found")

config_path = 'configs/instant-mesh-large.yaml'
config = OmegaConf.load(config_path)
config_name = os.path.basename(config_path).replace('.yaml', '')
model_config = config.model_config
infer_config = config.infer_config

IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False

device = torch.device('cuda')

# load diffusion model
print('Loading diffusion model ...')
pipeline = DiffusionPipeline.from_pretrained(
    "sudo-ai/zero123plus-v1.2", 
    custom_pipeline="zero123plus",
    torch_dtype=torch.float16,
)
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
    pipeline.scheduler.config, timestep_spacing='trailing'
)

# load custom white-background UNet
unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
state_dict = torch.load(unet_ckpt_path, map_location='cpu')
pipeline.unet.load_state_dict(state_dict, strict=True)

pipeline = pipeline.to(device)
print(f'type(pipeline)={type(pipeline)}')

# load reconstruction model
print('Loading reconstruction model ...')
model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
model: InstantMesh = instantiate_from_config(model_config)
state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
model.load_state_dict(state_dict, strict=True)

model = model.to(device)

print('Loading Finished!')

def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image uploaded!")

def preprocess(input_image, do_remove_background):

    rembg_session = rembg.new_session() if do_remove_background else None

    if do_remove_background:
        input_image = remove_background(input_image, rembg_session)
        input_image = resize_foreground(input_image, 0.85)

    return input_image


def pipeline_callback(log_queue: SimpleQueue, pipe: Any, step_index: int, timestep: float, callback_kwargs: dict[str, Any]) -> dict[str, Any]:
    latents = callback_kwargs["latents"]
    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]  # type: ignore[attr-defined]
    image = pipe.image_processor.postprocess(image, output_type="np").squeeze()  # type: ignore[attr-defined]

    log_queue.put(("mvs", rr.Image(image)))
    log_queue.put(("latents", rr.Tensor(latents.squeeze())))

    return callback_kwargs

def generate_mvs(log_queue, input_image, sample_steps, sample_seed):

    seed_everything(sample_seed)

    return pipeline(
        input_image,
        num_inference_steps=sample_steps,
        callback_on_step_end=lambda *args, **kwargs: pipeline_callback(log_queue, *args, **kwargs),
    ).images[0]

    # def thread_target(output_queue, input_image, sample_steps):
    #     z123_image = pipeline(
    #         input_image,
    #         num_inference_steps=sample_steps,
    #         callback_on_step_end=lambda *args, **kwargs: pipeline_callback(output_queue, *args, **kwargs),
    #     ).images[0]
    #     log_queue.put(("z123_image", z123_image))


    # output_queue = SimpleQueue()
    # z123_thread = threading.Thread(
    #     target=thread_target,
    #     args=
    #     [
    #         output_queue,
    #         input_image,
    #         sample_steps,
    #     ]
    # )
    # z123_thread.start()

    # while True:
    #     msg = output_queue.get()
    #     yield msg
    #     if msg[0] == "z123_image":            
    #         break
    # z123_thread.join()

# def make3d(images: Image.Image):
#     output_queue = SimpleQueue()
#     handle = threading.Thread(target=_make3d, args=[output_queue, images])
#     handle.start()
#     while True:
#         msg = output_queue.get()
#         yield msg
#         if msg[0] == "mesh":
#             break
#     handle.join()

def make3d(log_queue, images: Image.Image):
    global model
    if IS_FLEXICUBES:
        model.init_flexicubes_geometry(device, use_renderer=False)
    model = model.eval()

    images = np.asarray(images, dtype=np.float32) / 255.0
    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)

    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)

    images = images.unsqueeze(0).to(device)
    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)

    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name

    with torch.no_grad():
        # get triplane
        planes = model.forward_planes(images, input_cameras)

        # get mesh
        mesh_out = model.extract_mesh(
            planes,
            use_texture_map=False,
            **infer_config,
        )

        vertices, faces, vertex_colors = mesh_out

        log_queue.put(
            (
                "mesh", 
                rr.Mesh3D(
                    vertex_positions=vertices, 
                    vertex_colors=vertex_colors, 
                    triangle_indices=faces
                ),
            )
        )
    
    return mesh_out

def generate_blueprint() -> rrb.Blueprint:
    return rrb.Blueprint(
        rrb.Horizontal(
            rrb.Spatial3DView(origin="mesh"),
            rrb.Grid(
                rrb.Spatial2DView(origin="z123image"),
                rrb.Spatial2DView(origin="preprocessed_image"),
                rrb.Spatial2DView(origin="mvs"),
                rrb.TensorView(origin="latents", ),
            ),
            column_shares=[1, 1],
        ),
        
        collapse_panels=True,
    )

def compute(log_queue, input_image, do_remove_background, sample_steps, sample_seed):

    preprocessed_image = preprocess(input_image, do_remove_background)

    log_queue.put(("preprocessed_image", rr.Image(preprocessed_image)))
    # rr.log("preprocessed_image", rr.Image(preprocessed_image))

    z123_image = generate_mvs(log_queue, preprocessed_image, sample_steps, sample_seed)
    
    log_queue.put(("z123image", rr.Image(z123_image)))
    # rr.log("z123image", rr.Image(z123_image))

    mesh_out = make3d(log_queue, z123_image)

    log_queue.put("done")

    
@spaces.GPU
@rr.thread_local_stream("InstantMesh")
def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):

    log_queue = SimpleQueue()
    
    stream = rr.binary_stream()

    blueprint = generate_blueprint()
    rr.send_blueprint(blueprint)
    yield stream.read()

    handle = threading.Thread(target=compute, args=[log_queue, input_image, do_remove_background, sample_steps, sample_seed])
    handle.start()
    while True:
        msg = log_queue.get()
        if msg == "done":
            break
        else:
            entity_path, entity = msg
            rr.log(entity_path, entity)
            yield stream.read()
    handle.join()
    
    # return mesh

_HEADER_ = '''
<h2><b>Duplicate of the <a href=https://huggingface.co/spaces/TencentARC/InstantMesh>InstantMesh space</a> that uses <a href=https://rerun.io/>Rerun</a> for visualization.</b></h2>
<h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>

**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.

Technical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.

'''



with gr.Blocks() as demo:
    gr.Markdown(_HEADER_)
    with gr.Row(variant="panel"):
        with gr.Column(scale=1):
            with gr.Row():
                input_image = gr.Image(
                    label="Input Image",
                    image_mode="RGBA",
                    sources="upload",
                    #width=256,
                    #height=256,
                    type="pil",
                    elem_id="content_image",
                )
            with gr.Row():
                with gr.Group():
                    do_remove_background = gr.Checkbox(
                        label="Remove Background", value=True
                    )
                    sample_seed = gr.Number(value=42, label="Seed Value", precision=0)

                    sample_steps = gr.Slider(
                        label="Sample Steps",
                        minimum=30,
                        maximum=75,
                        value=75,
                        step=5
                    )

            with gr.Row():
                submit = gr.Button("Generate", elem_id="generate", variant="primary")

            with gr.Row(variant="panel"):
                gr.Examples(
                    examples=[
                        os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
                    ],
                    inputs=[input_image],
                    label="Examples",
                    cache_examples=False,
                    examples_per_page=16
                )

        with gr.Column(scale=2):
            
            viewer = Rerun(streaming=True, height=800)

            with gr.Row():
                gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')

    mv_images = gr.State()

    submit.click(fn=check_input_image, inputs=[input_image]).success(
        fn=log_to_rr,
        inputs=[input_image, do_remove_background, sample_steps, sample_seed],
        outputs=[viewer]
    )

demo.launch()