utils/pointe_utils.py

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 numpy as np

def init_from_pointe(prompt):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print('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])
    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, 0.0],
        model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
    )
    # 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

    pc = sampler.output_to_point_clouds(samples)[0]
    xyz = pc.coords
    rgb = np.zeros_like(xyz)
    rgb[:,0],rgb[:,1],rgb[:,2] = pc.channels['R'],pc.channels['G'],pc.channels['B']
    return xyz,rgb