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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 | |