AutoDL部署Wonder3D
输入“/”快速插入内容
AutoDL部署Wonder3D
AutoDL部署Wonder3D
用户3500用户35002024年3月13日修改
Wonder3D
效果更好
这是一个粗糙的笔记，需要Ubuntu基础+1，@龚涛 获得具体指导，
AutoDL环境
下载与环境部署（需挂代理）
代码块
conda create -n wonder3d
conda activate wonder3d
这样就算激活虚拟环境了（所有操作都需要在激活虚拟环境的情况下进行！）
代码块
git clone https://github.com/xxlong0/Wonder3D.git
pip install -r requirements.txt
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
先跑一遍基础Usage，此时生成result.png图片说明生成六视图部分已经成功
代码块
# Usage
# First clone the repo, and use the commands in the repo
import torch
import requests
from PIL import Image
import numpy as np
from torchvision.utils import make_grid, save_image
from diffusers import DiffusionPipeline # only tested on diffusers[torch]==0.19.3, may have conflicts with newer versions of diffusers
def load_wonder3d_pipeline():
pipeline = DiffusionPipeline.from_pretrained(
'flamehaze1115/wonder3d-v1.0', # or use local checkpoint './ckpts'
custom_pipeline='flamehaze1115/wonder3d-pipeline',
torch_dtype=torch.float16
)
# enable xformers
pipeline.unet.enable_xformers_memory_efficient_attention()
if torch.cuda.is_available():
pipeline.to('cuda:0')
return pipeline
pipeline = load_wonder3d_pipeline()
# Download an example image.
cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw)
# The object should be located in the center and resized to 80% of image height.
cond = Image.fromarray(np.array(cond)[:, :, :3])
# Run the pipeline!
images = pipeline(cond, num_inference_steps=20, output_type='pt', guidance_scale=1.0).images
result = make_grid(images, nrow=6, ncol=2, padding=0, value_range=(0, 1))
save_image(result, 'result.png')