refer to the following Colab python code,
write a new Colab python code for request :
user import Three View or Multiview orthographic projection image png jpg etc file, loading image to produce 3D cloud point,
write the results on .obj file
! pip install plotly -q
!git clone https://t.cn/A6KTcqVE
%cd shap-e
!pip install -e .
!git clone https://t.cn/A6NRWmuS
#Enter the directory and install the requirements
%cd shap-e
!pip install -e .
from PIL import Image
import torch
from tqdm.auto import tqdm
from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
from point_e.diffusion.sampler import PointCloudSampler
from point_e.models.download import load_checkpoint
from point_e.models.configs import MODEL_CONFIGS, model_from_config
from point_e.util.plotting import plot_point_cloud
#Implementation and Cooking the 3D models, import all the necessary libraries.
#%cd /content/shap-e
import torch
from shap_e.diffusion.sample import sample_latents
from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
from shap_e.models.download import load_model, load_config
from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget
#set the device to cuda if available, otherwise to cpu.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#load the models and weights.
xm = load_model('transmitter', device=device)
model = load_model('text300M', device=device)
diffusion = diffusion_from_config(load_config('diffusion'))
#generate the 3D models.
batch_size = 1 # this is the size of the models, higher values take longer to generate.
guidance_scale = 65.0 # this is the scale of the guidance, higher values make the model look more like the prompt.
latents = sample_latents(
batch_size=batch_size,
model=model,
diffusion=diffusion,
guidance_scale=guidance_scale,
model_kwargs=dict(texts=[prompt] * batch_size),
progress=True,
clip_denoised=True,
use_fp16=True,
use_karras=True,
karras_steps=64,
sigma_min=1E-3,
sigma_max=160,
s_churn=0,
)
render_mode = 'stf' #
size = 128 # this is the size of the renders, higher values take longer to render.
cameras = create_pan_cameras(size, device)
for i, latent in enumerate(latents):
images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)
display(gif_widget(images))
#save the 3D models as .ply and .obj files.
# Example of saving the latents as meshes.
from shap_e.util.notebooks import decode_latent_mesh
for i, latent in enumerate(latents):
t = decode_latent_mesh(xm, latent).tri_mesh()
with open(f'example_mesh_{i}.ply', 'wb') as f: # this is three-dimensional geometric data of model.
t.write_ply(f)
with open(f'example_mesh_{i}.obj', 'w') as f: # we will use this file to customize in Blender Studio later.
t.write_obj(f)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating base model...')
base_name = 'base40M'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
print('creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
print('downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))
print('downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
sampler = PointCloudSampler(
device=device,
models=[base_model, upsampler_model],
diffusions=[base_diffusion, upsampler_diffusion],
num_points=[1024, 4096 - 1024],
aux_channels=['R', 'G', 'B'],
guidance_scale=[3.0, 3.0],
)
from google.colab import files
uploaded = files.upload()
# Load an image to condition on.
img = Image.open('figure_all.jpg')
# Produce a sample from the model.
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
samples = x
img
pc = sampler.output_to_point_clouds(samples)[0]
fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
import plotly.graph_objects as go
fig_plotly = go.Figure(
data=[
go.Scatter3d(
x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2],
mode='markers',
marker=dict(
size=2,
color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels["R"], pc.channels["G"], pc.channels["B"])],
)
)
],
layout=dict(
scene=dict(
xaxis=dict(visible=False),
yaxis=dict(visible=False),
zaxis=dict(visible=False)
)
),
)
fig_plotly.show(renderer="colab")
from point_e.util.pc_to_mesh import marching_cubes_mesh
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating SDF model...')
name = 'sdf'
model = model_from_config(MODEL_CONFIGS[name], device)
model.eval()
print('loading SDF model...')
model.load_state_dict(load_checkpoint(name, device))
import skimage.measure as measure
# Produce a mesh (with vertex colors)
mesh = marching_cubes_mesh(
pc=pc,
model=model,
batch_size=4096,
grid_size=32, # increase to 128 for resolution used in evals
progress=True,
)
# Write the mesh to a PLY file to import into some other program.
with open('figure_all.obj', 'wb') as f:
mesh.write_ply(f)
write a new Colab python code for request :
user import Three View or Multiview orthographic projection image png jpg etc file, loading image to produce 3D cloud point,
write the results on .obj file
! pip install plotly -q
!git clone https://t.cn/A6KTcqVE
%cd shap-e
!pip install -e .
!git clone https://t.cn/A6NRWmuS
#Enter the directory and install the requirements
%cd shap-e
!pip install -e .
from PIL import Image
import torch
from tqdm.auto import tqdm
from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
from point_e.diffusion.sampler import PointCloudSampler
from point_e.models.download import load_checkpoint
from point_e.models.configs import MODEL_CONFIGS, model_from_config
from point_e.util.plotting import plot_point_cloud
#Implementation and Cooking the 3D models, import all the necessary libraries.
#%cd /content/shap-e
import torch
from shap_e.diffusion.sample import sample_latents
from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
from shap_e.models.download import load_model, load_config
from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget
#set the device to cuda if available, otherwise to cpu.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#load the models and weights.
xm = load_model('transmitter', device=device)
model = load_model('text300M', device=device)
diffusion = diffusion_from_config(load_config('diffusion'))
#generate the 3D models.
batch_size = 1 # this is the size of the models, higher values take longer to generate.
guidance_scale = 65.0 # this is the scale of the guidance, higher values make the model look more like the prompt.
latents = sample_latents(
batch_size=batch_size,
model=model,
diffusion=diffusion,
guidance_scale=guidance_scale,
model_kwargs=dict(texts=[prompt] * batch_size),
progress=True,
clip_denoised=True,
use_fp16=True,
use_karras=True,
karras_steps=64,
sigma_min=1E-3,
sigma_max=160,
s_churn=0,
)
render_mode = 'stf' #
size = 128 # this is the size of the renders, higher values take longer to render.
cameras = create_pan_cameras(size, device)
for i, latent in enumerate(latents):
images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)
display(gif_widget(images))
#save the 3D models as .ply and .obj files.
# Example of saving the latents as meshes.
from shap_e.util.notebooks import decode_latent_mesh
for i, latent in enumerate(latents):
t = decode_latent_mesh(xm, latent).tri_mesh()
with open(f'example_mesh_{i}.ply', 'wb') as f: # this is three-dimensional geometric data of model.
t.write_ply(f)
with open(f'example_mesh_{i}.obj', 'w') as f: # we will use this file to customize in Blender Studio later.
t.write_obj(f)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating base model...')
base_name = 'base40M'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
print('creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
print('downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))
print('downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
sampler = PointCloudSampler(
device=device,
models=[base_model, upsampler_model],
diffusions=[base_diffusion, upsampler_diffusion],
num_points=[1024, 4096 - 1024],
aux_channels=['R', 'G', 'B'],
guidance_scale=[3.0, 3.0],
)
from google.colab import files
uploaded = files.upload()
# Load an image to condition on.
img = Image.open('figure_all.jpg')
# Produce a sample from the model.
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
samples = x
img
pc = sampler.output_to_point_clouds(samples)[0]
fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
import plotly.graph_objects as go
fig_plotly = go.Figure(
data=[
go.Scatter3d(
x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2],
mode='markers',
marker=dict(
size=2,
color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels["R"], pc.channels["G"], pc.channels["B"])],
)
)
],
layout=dict(
scene=dict(
xaxis=dict(visible=False),
yaxis=dict(visible=False),
zaxis=dict(visible=False)
)
),
)
fig_plotly.show(renderer="colab")
from point_e.util.pc_to_mesh import marching_cubes_mesh
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating SDF model...')
name = 'sdf'
model = model_from_config(MODEL_CONFIGS[name], device)
model.eval()
print('loading SDF model...')
model.load_state_dict(load_checkpoint(name, device))
import skimage.measure as measure
# Produce a mesh (with vertex colors)
mesh = marching_cubes_mesh(
pc=pc,
model=model,
batch_size=4096,
grid_size=32, # increase to 128 for resolution used in evals
progress=True,
)
# Write the mesh to a PLY file to import into some other program.
with open('figure_all.obj', 'wb') as f:
mesh.write_ply(f)
import requests
from PIL import Image
import torch
from tqdm.auto import tqdm
from shap_e.diffusion.sample import sample_latents
from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
from shap_e.models.download import load_model, load_config
from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget
from shap_e.util.notebooks import decode_latent_mesh
from shap_e.util.pc_to_mesh import marching_cubes_mesh
# Install necessary packages
!pip install plotly -q
# Clone the required repositories
!git clone https://t.cn/A6KTcqVE
%cd point-e
!pip install -e .
%cd ..
!git clone https://t.cn/A6NRWmuS
%cd shap-e
!pip install -e .
%cd ..
def generate_3d_model(image_path, prompt, render_mode='stf', size=128):
# Load models and weights
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
xm = load_model('transmitter', device=device)
model = load_model('text300M', device=device)
diffusion = diffusion_from_config(load_config('diffusion'))
# Generate 3D models
batch_size = 1
guidance_scale = 65.0
latents = sample_latents(
batch_size=batch_size,
model=model,
diffusion=diffusion,
guidance_scale=guidance_scale,
model_kwargs=dict(texts=[prompt] * batch_size),
progress=True,
clip_denoised=True,
use_fp16=True,
use_karras=True,
karras_steps=64,
sigma_min=1E-3,
sigma_max=160,
s_churn=0,
)
cameras = create_pan_cameras(size, device)
for i, latent in enumerate(latents):
images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)
display(gif_widget(images))
# Save the 3D models as .ply and .obj files
for i, latent in enumerate(latents):
t = decode_latent_mesh(xm, latent).tri_mesh()
with open(f'example_mesh_{i}.ply', 'wb') as f:
t.write_ply(f)
with open(f'example_mesh_{i}.obj', 'w') as f:
t.write_obj(f)
# Load the image to condition on
img = Image.open(image_path)
# Produce a sample from the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating base model...')
base_name = 'base40M'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
print('creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
print('downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))
print('downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
sampler = PointCloudSampler(
device=device,
models=[base_model, upsampler_model],
diffusions=[base_diffusion, upsampler_diffusion],
num_points=[1024, 4096 - 1024],
aux_channels=['R', 'G', 'B'],
guidance_scale=[3.0, 3.0],
)
# Load the image to condition on
img = Image.open(image_path)
# Produce a sample from the model
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
samples = x
# Convert the output to point clouds
pc = sampler.output_to_point_clouds(samples)[0]
# Plot the point cloud
fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
fig.show(renderer="colab")
# Generate the mesh
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating SDF model...')
name = 'sdf'
model = model_from_config(MODEL_CONFIGS[name], device)
model.eval()
print('loading SDF model...')
model.load_state_dict(load_checkpoint(name, device))
# Produce a mesh (with vertex colors)
mesh = marching_cubes_mesh(
pc=pc,
model=model,
batch_size=4096,
grid_size=32,
progress=True,
)
# Write the mesh to a PLY file
with open('output.obj', 'wb') as f:
mesh.write_obj(f)
# Example usage
image_path = 'path/to/image.jpg'
prompt = 'example prompt'
generate_3d_model(image_path, prompt)
from PIL import Image
import torch
from tqdm.auto import tqdm
from shap_e.diffusion.sample import sample_latents
from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
from shap_e.models.download import load_model, load_config
from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget
from shap_e.util.notebooks import decode_latent_mesh
from shap_e.util.pc_to_mesh import marching_cubes_mesh
# Install necessary packages
!pip install plotly -q
# Clone the required repositories
!git clone https://t.cn/A6KTcqVE
%cd point-e
!pip install -e .
%cd ..
!git clone https://t.cn/A6NRWmuS
%cd shap-e
!pip install -e .
%cd ..
def generate_3d_model(image_path, prompt, render_mode='stf', size=128):
# Load models and weights
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
xm = load_model('transmitter', device=device)
model = load_model('text300M', device=device)
diffusion = diffusion_from_config(load_config('diffusion'))
# Generate 3D models
batch_size = 1
guidance_scale = 65.0
latents = sample_latents(
batch_size=batch_size,
model=model,
diffusion=diffusion,
guidance_scale=guidance_scale,
model_kwargs=dict(texts=[prompt] * batch_size),
progress=True,
clip_denoised=True,
use_fp16=True,
use_karras=True,
karras_steps=64,
sigma_min=1E-3,
sigma_max=160,
s_churn=0,
)
cameras = create_pan_cameras(size, device)
for i, latent in enumerate(latents):
images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)
display(gif_widget(images))
# Save the 3D models as .ply and .obj files
for i, latent in enumerate(latents):
t = decode_latent_mesh(xm, latent).tri_mesh()
with open(f'example_mesh_{i}.ply', 'wb') as f:
t.write_ply(f)
with open(f'example_mesh_{i}.obj', 'w') as f:
t.write_obj(f)
# Load the image to condition on
img = Image.open(image_path)
# Produce a sample from the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating base model...')
base_name = 'base40M'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
print('creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
print('downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))
print('downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
sampler = PointCloudSampler(
device=device,
models=[base_model, upsampler_model],
diffusions=[base_diffusion, upsampler_diffusion],
num_points=[1024, 4096 - 1024],
aux_channels=['R', 'G', 'B'],
guidance_scale=[3.0, 3.0],
)
# Load the image to condition on
img = Image.open(image_path)
# Produce a sample from the model
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(images=[img]))):
samples = x
# Convert the output to point clouds
pc = sampler.output_to_point_clouds(samples)[0]
# Plot the point cloud
fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))
fig.show(renderer="colab")
# Generate the mesh
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating SDF model...')
name = 'sdf'
model = model_from_config(MODEL_CONFIGS[name], device)
model.eval()
print('loading SDF model...')
model.load_state_dict(load_checkpoint(name, device))
# Produce a mesh (with vertex colors)
mesh = marching_cubes_mesh(
pc=pc,
model=model,
batch_size=4096,
grid_size=32,
progress=True,
)
# Write the mesh to a PLY file
with open('output.obj', 'wb') as f:
mesh.write_obj(f)
# Example usage
image_path = 'path/to/image.jpg'
prompt = 'example prompt'
generate_3d_model(image_path, prompt)
惊！天降老公竟是首富许南歌霍北宴（最新章节无弹窗）全文许南歌阅读[嘻嘻][来]!"fiG❗书名：《惊！天降老公竟是首富》
❗主角：许南歌霍北宴
全文请到公#众#号〖岁月书摘〗发个 主角名，就行了“别多想。” 许南歌忽然说了这么一句，霍北宴一听，还以为她听进去了，刚要松一口气，就听见她又补了一句：“你们一个也跑不了。” 第525章报复 又过了两三天。 新闻上爆出了一则消息，霍北宴找社会上人员企图对圈内女明星不轨，不法分子良心发现，向警方自首。 一时间，霍北宴名声更是岌岌可危。 全网人都在讨伐她。 胡玉汐得知这些消息的时候，有些坐立难安。 “我一直以为霍北宴退圈，事情就应该过去了，但是没想到，他们完全不给一点机会，居然把人往绝路上逼。” “有我在，你担心什么？” 坐在对面和她共赏夜景的优雅男人不紧不慢的吃了一小口牛排，十分惬意，和她的焦灼完全相反。 胡玉汐看了看他，见他高贵随意的姿态，欲言又止。 似乎是看出了她的意思，男人又眼皮子一掀，说：“你还担心会报复到你身上？” “是有点。” 她回答道：“我不知道为什么许南歌对我有那么大的敌意，我一开始明明想和她交好，但是她一点面子不给，反而还三番两次终止了跟我的合作，我也不知道是哪里得罪过她，海平，我现在可以依靠的只有你了，你可不能不管我。” 说到最后，胡玉汐还跟他撒起了娇。 她很美，性格一向强势，但此时服软做小的模样更是别有一番风味，能极大的满足男人的那点大男子主义。 陆海平看着她的眼神果然变得深了点，但细看，眼底依旧一片清明。“许南歌，你现在真有钱的话，不如先去医院挂个脑科或者精神科好吗？”我扭头就走，懒得和她废话。 “纪辛瑜，你直到现在依然不把我放在眼里是吧？”许南歌有些恼怒地拦住了我。 我视若无睹，拨开她便继续往前走。 许南歌拽我的手，我拎着的礼盒被她扯落在地上，发出一声闷响。 许南歌还想上前抓我的手，我抬手挡了一下，她竟然直接跌坐在地上，仿佛我用了极大的力气一样。 陶叶急忙去扶许南歌，然后指责我，“纪辛瑜你有病吧？你推她干什么！” 我蹲下身子把礼盒捡起来，起身时却看到霍北宴正站在不远处看着我。 “抱歉，我不是故意的。” 当我们的动静引来了别人的关注，许南歌一改刚才的气急败坏，反而是开口道歉。 我起身，不免有些好笑，“许南歌，你不知道这种地方四处都有监控吗？装什么无辜呢？ 一听到监控，许南歌不自然地看了一眼四周，最后视线落在了霍北宴身上。 霍北宴却只是看着我，我略有心虚。 昨天的事我都还没太敢仔细去想，本来打算这段时间疏远一点，大家都清醒一下。 没想到今天又在这里碰上了，真是有毒，干脆就假装没看到？ 我心一横，拎着礼盒转身便走，准备去找何远之，把贺礼带到以后就离开这里，毕竟同时遇到了许南歌和霍北宴，证明此地不宜久留。 “纪辛瑜！”霍北宴的声音不算高，却清晰地传入我的耳中，他三两步跟上了我的步伐，与我并肩。
❗主角：许南歌霍北宴
全文请到公#众#号〖岁月书摘〗发个 主角名，就行了“别多想。” 许南歌忽然说了这么一句，霍北宴一听，还以为她听进去了，刚要松一口气，就听见她又补了一句：“你们一个也跑不了。” 第525章报复 又过了两三天。 新闻上爆出了一则消息，霍北宴找社会上人员企图对圈内女明星不轨，不法分子良心发现，向警方自首。 一时间，霍北宴名声更是岌岌可危。 全网人都在讨伐她。 胡玉汐得知这些消息的时候，有些坐立难安。 “我一直以为霍北宴退圈，事情就应该过去了，但是没想到，他们完全不给一点机会，居然把人往绝路上逼。” “有我在，你担心什么？” 坐在对面和她共赏夜景的优雅男人不紧不慢的吃了一小口牛排，十分惬意，和她的焦灼完全相反。 胡玉汐看了看他，见他高贵随意的姿态，欲言又止。 似乎是看出了她的意思，男人又眼皮子一掀，说：“你还担心会报复到你身上？” “是有点。” 她回答道：“我不知道为什么许南歌对我有那么大的敌意，我一开始明明想和她交好，但是她一点面子不给，反而还三番两次终止了跟我的合作，我也不知道是哪里得罪过她，海平，我现在可以依靠的只有你了，你可不能不管我。” 说到最后，胡玉汐还跟他撒起了娇。 她很美，性格一向强势，但此时服软做小的模样更是别有一番风味，能极大的满足男人的那点大男子主义。 陆海平看着她的眼神果然变得深了点，但细看，眼底依旧一片清明。“许南歌，你现在真有钱的话，不如先去医院挂个脑科或者精神科好吗？”我扭头就走，懒得和她废话。 “纪辛瑜，你直到现在依然不把我放在眼里是吧？”许南歌有些恼怒地拦住了我。 我视若无睹，拨开她便继续往前走。 许南歌拽我的手，我拎着的礼盒被她扯落在地上，发出一声闷响。 许南歌还想上前抓我的手，我抬手挡了一下，她竟然直接跌坐在地上，仿佛我用了极大的力气一样。 陶叶急忙去扶许南歌，然后指责我，“纪辛瑜你有病吧？你推她干什么！” 我蹲下身子把礼盒捡起来，起身时却看到霍北宴正站在不远处看着我。 “抱歉，我不是故意的。” 当我们的动静引来了别人的关注，许南歌一改刚才的气急败坏，反而是开口道歉。 我起身，不免有些好笑，“许南歌，你不知道这种地方四处都有监控吗？装什么无辜呢？ 一听到监控，许南歌不自然地看了一眼四周，最后视线落在了霍北宴身上。 霍北宴却只是看着我，我略有心虚。 昨天的事我都还没太敢仔细去想，本来打算这段时间疏远一点，大家都清醒一下。 没想到今天又在这里碰上了，真是有毒，干脆就假装没看到？ 我心一横，拎着礼盒转身便走，准备去找何远之，把贺礼带到以后就离开这里，毕竟同时遇到了许南歌和霍北宴，证明此地不宜久留。 “纪辛瑜！”霍北宴的声音不算高，却清晰地传入我的耳中，他三两步跟上了我的步伐，与我并肩。
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