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# Imports
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
# Downloads
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
st.write('creating base model...')
base_name = 'base40M-textvec'
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))
# Define Sampler
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, 0.0],
model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
)
# Load an image to condition on.
prompt = st.sidebar.text_input("Prompt")
# Produce a sample from the model.
samples = None
for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))):
samples = x
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