https://colab.research.google.com/drive/1XvXBALiOwAT5-OaAD7AygqBXFqTijrVf?usp=sharing#scrollTo=7-fLWame0qJw

import torch
from shap_e.diffusion.sample import sample_latentsfrom shap_e.diffusion.gaussian_diffusion import diffusion_from_configfrom shap_e.models.download import load_model, load_configfrom shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widgetfrom shap_e.util.image_util import load_image

xm = load_model('transmitter', device=device)model = load_model('image300M', device=device)diffusion = diffusion_from_config(load_config('diffusion'))

batch_size = 4guidance_scale = 3.0
# To get the best result, you should remove the background and show only the object of interest to the model.image = load_image("example_data/corgi.png")
latents = sample_latents(    batch_size=batch_size,    model=model,    diffusion=diffusion,    guidance_scale=guidance_scale,    model_kwargs=dict(images=[image] * 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,)

batch_size  = 生成的数量guidance_scale = 分辨率img = 图片保存路径

复制如下代码到最后一步render_mode = 'nerf' # you can change this to 'stf' for mesh renderingsize = 64 # 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))