Update app.py

app.py

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+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
+import streamlit as st
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+st.write('creating base model...')
+base_name = 'base40M' # use base300M or base1B for better results
+base_model = model_from_config(MODEL_CONFIGS[base_name], device)
+base_model.eval()
+base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
+st.write('creating upsample model...')
+upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
+upsampler_model.eval()
+upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
+st.write('downloading base checkpoint...')
+base_model.load_state_dict(load_checkpoint(base_name, device))
+st.write('downloading upsampler checkpoint...')
+upsampler_model.load_state_dict(load_checkpoint('upsample', device))
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