Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,532 Bytes
7f51798 |
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 |
import os
import imageio
import argparse
from pdb import set_trace as st
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 huggingface_hub import hf_hub_download
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, save_obj_with_mtl
from src.utils.infer_util import remove_background, resize_foreground, save_video
def get_render_cameras(batch_size=1, M=120, radius=4.0, elevation=20.0, 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(30.0).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 render_frames(model, planes, render_cameras, render_size=512, chunk_size=1, is_flexicubes=False):
"""
Render frames from triplanes.
"""
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.forward_synthesizer(
planes,
render_cameras[:, i:i+chunk_size],
render_size=render_size,
)['images_rgb']
frames.append(frame)
frames = torch.cat(frames, dim=1)[0] # we suppose batch size is always 1
return frames
###############################################################################
# Arguments.
###############################################################################
parser = argparse.ArgumentParser()
parser.add_argument('config', type=str, help='Path to config file.')
parser.add_argument('input_path', type=str, help='Path to input image or directory.')
# parser.add_argument('--output_path', type=str, default='outputs/', help='Output directory.')
parser.add_argument('--output_path', type=str, default='outputs_debug/', help='Output directory.')
parser.add_argument('--diffusion_steps', type=int, default=75, help='Denoising Sampling steps.')
parser.add_argument('--seed', type=int, default=42, help='Random seed for sampling.')
parser.add_argument('--scale', type=float, default=1.0, help='Scale of generated object.')
parser.add_argument('--distance', type=float, default=4.5, help='Render distance.')
parser.add_argument('--view', type=int, default=6, choices=[4, 6], help='Number of input views.')
parser.add_argument('--no_rembg', action='store_true', help='Do not remove input background.')
parser.add_argument('--export_texmap', action='store_true', help='Export a mesh with texture map.')
parser.add_argument('--save_video', action='store_true', help='Save a circular-view video.')
args = parser.parse_args()
seed_everything(args.seed)
###############################################################################
# Stage 0: Configuration.
###############################################################################
config = OmegaConf.load(args.config)
config_name = os.path.basename(args.config).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')
input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0*args.scale).to(device)
render_cameras = get_render_cameras(
batch_size=1,
M=120,
radius=args.distance,
# elevation=20.0,
elevation=0,
is_flexicubes=IS_FLEXICUBES,
).to(device)
# torch.save(input_cameras.cpu(), 'input_cameras_1.5.pt')
# torch.save(render_cameras.cpu(), 'render_cameras_1.5.pt')
# st()
# 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
print('Loading custom white-background unet ...')
if os.path.exists(infer_config.unet_path):
unet_ckpt_path = infer_config.unet_path
else:
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 = instantiate_from_config(model_config)
if os.path.exists(infer_config.model_path):
model_ckpt_path = infer_config.model_path
else:
model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename=f"{config_name.replace('-', '_')}.ckpt", repo_type="model")
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.')}
model.load_state_dict(state_dict, strict=True)
model = model.to(device)
if IS_FLEXICUBES:
model.init_flexicubes_geometry(device, fovy=30.0)
model = model.eval()
# make output directories
image_path = os.path.join(args.output_path, config_name, 'images')
mesh_path = os.path.join(args.output_path, config_name, 'meshes')
video_path = os.path.join(args.output_path, config_name, 'videos')
os.makedirs(image_path, exist_ok=True)
os.makedirs(mesh_path, exist_ok=True)
os.makedirs(video_path, exist_ok=True)
# process input files
if os.path.isdir(args.input_path):
input_files = [
os.path.join(args.input_path, file)
for file in os.listdir(args.input_path)
if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.webp')
]
else:
input_files = [args.input_path]
print(f'Total number of input images: {len(input_files)}')
###############################################################################
# Stage 1: Multiview generation.
###############################################################################
rembg_session = None if args.no_rembg else rembg.new_session()
outputs = []
for idx, image_file in enumerate(input_files):
name = os.path.basename(image_file).split('.')[0]
print(f'[{idx+1}/{len(input_files)}] Imagining {name} ...')
# remove background optionally
input_image = Image.open(image_file)
if not args.no_rembg:
input_image = remove_background(input_image, rembg_session)
input_image = resize_foreground(input_image, 0.85)
imageio.imwrite(os.path.join(image_path, f'{name}-input.png'), np.array(input_image))
# continue
# sampling
output_image = pipeline(
input_image,
num_inference_steps=args.diffusion_steps,
).images[0]
output_image.save(os.path.join(image_path, f'{name}.png'))
print(f"Image saved to {os.path.join(image_path, f'{name}.png')}")
images = np.asarray(output_image, 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)
st()
outputs.append({'name': name, 'images': images})
# delete pipeline to save memory
del pipeline
###############################################################################
# Stage 2: Reconstruction.
###############################################################################
exit()
chunk_size = 20 if IS_FLEXICUBES else 1
for idx, sample in enumerate(outputs):
name = sample['name']
print(f'[{idx+1}/{len(outputs)}] Creating {name} ...')
images = sample['images'].unsqueeze(0).to(device)
images = v2.functional.resize(images, 320, interpolation=3, antialias=True).clamp(0, 1)
if args.view == 4:
indices = torch.tensor([0, 2, 4, 5]).long().to(device)
images = images[:, indices]
input_cameras = input_cameras[:, indices]
with torch.no_grad():
# get triplane
planes = model.forward_planes(images, input_cameras)
# get mesh
mesh_path_idx = os.path.join(mesh_path, f'{name}.obj')
mesh_out = model.extract_mesh(
planes,
use_texture_map=args.export_texmap,
**infer_config,
)
if args.export_texmap:
vertices, faces, uvs, mesh_tex_idx, tex_map = mesh_out
save_obj_with_mtl(
vertices.data.cpu().numpy(),
uvs.data.cpu().numpy(),
faces.data.cpu().numpy(),
mesh_tex_idx.data.cpu().numpy(),
tex_map.permute(1, 2, 0).data.cpu().numpy(),
mesh_path_idx,
)
else:
vertices, faces, vertex_colors = mesh_out
save_obj(vertices, faces, vertex_colors, mesh_path_idx)
print(f"Mesh saved to {mesh_path_idx}")
# get video
if args.save_video:
video_path_idx = os.path.join(video_path, f'{name}.mp4')
render_size = infer_config.render_resolution
# render_cameras = get_render_cameras(
# batch_size=1,
# M=120,
# radius=args.distance,
# elevation=20.0,
# is_flexicubes=IS_FLEXICUBES,
# ).to(device)
frames = render_frames(
model,
planes,
render_cameras=render_cameras,
render_size=render_size,
chunk_size=chunk_size,
is_flexicubes=IS_FLEXICUBES,
)
save_video(
frames,
video_path_idx,
fps=30,
)
print(f"Video saved to {video_path_idx}")
|