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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, save_glb
from src.utils.infer_util import remove_background, resize_foreground, images_to_video
import tempfile
from huggingface_hub import hf_hub_download
if torch.cuda.is_available() and torch.cuda.device_count() >= 2:
device0 = torch.device('cuda:0')
device1 = torch.device('cuda:1')
else:
device0 = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device1 = device0
# Define the cache directory for model files
model_cache_dir = './models/'
os.makedirs(model_cache_dir, exist_ok=True)
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(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 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.
###############################################################################
seed_everything(0)
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,
cache_dir=model_cache_dir
)
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", cache_dir=model_cache_dir)
state_dict = torch.load(unet_ckpt_path, map_location='cpu')
pipeline.unet.load_state_dict(state_dict, strict=True)
pipeline = pipeline.to(device0)
# 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", cache_dir=model_cache_dir)
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(device1)
if IS_FLEXICUBES:
model.init_flexicubes_geometry(device1, fovy=30.0)
model = model.eval()
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
generator = torch.Generator(device=device0)
z123_image = pipeline(
input_image,
num_inference_steps=sample_steps,
generator=generator,
).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 -> (n m) h w c', n=3, m=2)
show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
show_image = Image.fromarray(show_image.numpy())
return z123_image, show_image
def make_mesh(mesh_fpath, planes):
mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
mesh_dirname = os.path.dirname(mesh_fpath)
mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
with torch.no_grad():
# get mesh
mesh_out = model.extract_mesh(
planes,
use_texture_map=False,
**infer_config,
)
vertices, faces, vertex_colors = mesh_out
vertices = vertices[:, [1, 2, 0]]
save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
save_obj(vertices, faces, vertex_colors, mesh_fpath)
print(f"Mesh saved to {mesh_fpath}")
return mesh_fpath, mesh_glb_fpath
def make3d(images):
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(device1)
render_cameras = get_render_cameras(
batch_size=1, radius=4.5, elevation=20.0, is_flexicubes=IS_FLEXICUBES).to(device1)
images = images.unsqueeze(0).to(device1)
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}")
mesh_fpath, mesh_glb_fpath = make_mesh(mesh_fpath, planes)
return video_fpath, mesh_fpath, mesh_glb_fpath
import gradio as gr
_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>
**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.
Code: <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a>. Techenical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.
❗️❗️❗️**Important Notes:**
- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>.
- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
'''
_CITE_ = r"""
If InstantMesh is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>Github Repo</a>. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/InstantMesh?style=social)](https://github.com/TencentARC/InstantMesh)
---
📝 **Citation**
If you find our work useful for your research or applications, please cite using this bibtex:
```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}
}
```
📋 **License**
Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/LICENSE) for details.
📧 **Contact**
If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
"""
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 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",
examples_per_page=20
)
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():
with gr.Tab("OBJ"):
output_model_obj = gr.Model3D(
label="Output Model (OBJ Format)",
#width=768,
interactive=False,
)
gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
with gr.Tab("GLB"):
output_model_glb = gr.Model3D(
label="Output Model (GLB Format)",
#width=768,
interactive=False,
)
gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
with gr.Row():
gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
gr.Markdown(_CITE_)
mv_images = gr.State()
submit.click(fn=check_input_image, inputs=[input_image]).success(
fn=preprocess,
inputs=[input_image, do_remove_background],
outputs=[processed_image],
).success(
fn=generate_mvs,
inputs=[processed_image, sample_steps, sample_seed],
outputs=[mv_images, mv_show_images],
).success(
fn=make3d,
inputs=[mv_images],
outputs=[output_video, output_model_obj, output_model_glb]
)
demo.queue(max_size=10)
demo.launch(server_name="0.0.0.0", server_port=43839)