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import os
import argparse
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, 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('--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')
# 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 ...')
state_dict = torch.load(infer_config.unet_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)
state_dict = torch.load(infer_config.model_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)
# 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)
outputs.append({'name': name, 'images': images})
# delete pipeline to save memory
del pipeline
###############################################################################
# Stage 2: Reconstruction.
###############################################################################
input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0*args.scale).to(device)
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}")