Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,076 Bytes
d0d35cc 74465a1 0cd8dc5 74465a1 ac99742 3b4754e 74465a1 00059bc 74465a1 00059bc 74465a1 35f429b 74465a1 00059bc 74465a1 00059bc 74465a1 b5e00ca 7103ae4 74465a1 00059bc 74465a1 00059bc 74465a1 |
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 |
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
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
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
from src.utils.infer_util import remove_background, resize_foreground, images_to_video
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)
# 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 = 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 generate_mvs(input_image, sample_steps, sample_seed):
seed_everything(sample_seed)
# sampling
z123_image = pipeline(
input_image,
num_inference_steps=sample_steps
).images[0]
show_image = np.asarray(z123_image, dtype=np.uint8)
show_image = torch.from_numpy(show_image) # (960, 640, 3)
show_image = rearrange(show_image, '(n h) (m w) c -> (m h) (n w) c', n=3, m=2)
show_image = Image.fromarray(show_image.numpy())
return z123_image, show_image
@spaces.GPU
def make3d(input_image, sample_steps, sample_seed):
cuda_path = find_cuda()
if cuda_path:
print(f"CUDA installation found at: {cuda_path}")
else:
print("CUDA installation not found")
global model
if IS_FLEXICUBES:
model.init_flexicubes_geometry(device, use_renderer=False)
model = model.eval()
images, show_images = generate_mvs(input_image, sample_steps, sample_seed)
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=2.5).to(device)
render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).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
print(mesh_fpath)
mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
mesh_dirname = os.path.dirname(mesh_fpath)
video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
with torch.no_grad():
# get triplane
planes = model.forward_planes(images, input_cameras)
# # get video
# chunk_size = 20 if IS_FLEXICUBES else 1
# render_size = 384
# frames = []
# for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
# if IS_FLEXICUBES:
# frame = model.forward_geometry(
# planes,
# render_cameras[:, i:i+chunk_size],
# render_size=render_size,
# )['img']
# else:
# frame = model.synthesizer(
# planes,
# cameras=render_cameras[:, i:i+chunk_size],
# render_size=render_size,
# )['images_rgb']
# frames.append(frame)
# frames = torch.cat(frames, dim=1)
# images_to_video(
# frames[0],
# video_fpath,
# fps=30,
# )
# print(f"Video saved to {video_fpath}")
# get mesh
mesh_out = model.extract_mesh(
planes,
use_texture_map=False,
**infer_config,
)
vertices, faces, vertex_colors = mesh_out
vertices = vertices[:, [0, 2, 1]]
vertices[:, -1] *= -1
save_obj(vertices, faces, vertex_colors, mesh_fpath)
print(f"Mesh saved to {mesh_fpath}")
return mesh_fpath, show_images
_HEADER_ = '''
<h2><b>Official 🤗 Gradio Demo</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>
'''
_LINKS_ = '''
<h3>Code is available at <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a></h3>
<h3>Report is available at <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a></h3>
'''
_CITE_ = r"""
```bibtex
@article{xu2024instantmesh,
title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models},
author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying},
journal={arXiv preprint arXiv:2404.07191},
year={2024}
}
```
"""
with gr.Blocks() as demo:
gr.Markdown(_HEADER_)
with gr.Row(variant="panel"):
with gr.Column():
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",
)
processed_image = gr.Image(
label="Processed Image",
image_mode="RGBA",
width=256,
height=256,
type="pil",
interactive=False
)
with gr.Row():
with gr.Group():
do_remove_background = gr.Checkbox(
label="Remove Background", value=True
)
sample_seed = gr.Number(value=42, label="Seed (Try a different value if the result is unsatisfying)", 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=12
)
with gr.Column():
with gr.Row():
with gr.Column():
mv_show_images = gr.Image(
label="Generated Multi-views",
type="pil",
width=379,
interactive=False
)
# with gr.Column():
# output_video = gr.Video(
# label="video", format="mp4",
# width=379,
# autoplay=True,
# interactive=False
# )
with gr.Row():
output_model_obj = gr.Model3D(
label="Output Model (OBJ Format)",
interactive=False,
)
gr.Markdown(_LINKS_)
gr.Markdown(_CITE_)
submit.click(fn=check_input_image, inputs=[input_image]).success(
fn=preprocess,
inputs=[input_image, do_remove_background],
outputs=[processed_image],
).success(
fn=make3d,
inputs=[processed_image, sample_steps, sample_seed],
outputs=[output_model_obj, mv_show_images]
)
demo.launch() |