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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from tqdm.auto import tqdm\n",
"\n",
"from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config\n",
"from point_e.diffusion.sampler import PointCloudSampler\n",
"from point_e.models.download import load_checkpoint\n",
"from point_e.models.configs import MODEL_CONFIGS, model_from_config\n",
"from point_e.util.plotting import plot_point_cloud"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"creating base model...\n",
"creating upsample model...\n",
"downloading base checkpoint...\n",
"downloading upsampler checkpoint...\n"
]
},
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device('cuda')\n",
"\n",
"print('creating base model...')\n",
"base_name = 'base40M-textvec'\n",
"base_model = model_from_config(MODEL_CONFIGS[base_name], device)\n",
"base_model.eval()\n",
"base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])\n",
"\n",
"print('creating upsample model...')\n",
"upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)\n",
"upsampler_model.eval()\n",
"upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])\n",
"\n",
"print('downloading base checkpoint...')\n",
"base_model.load_state_dict(load_checkpoint(base_name, device))\n",
"\n",
"print('downloading upsampler checkpoint...')\n",
"upsampler_model.load_state_dict(load_checkpoint('upsample', device))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"sampler = PointCloudSampler(\n",
" device=device,\n",
" models=[base_model, upsampler_model],\n",
" diffusions=[base_diffusion, upsampler_diffusion],\n",
" num_points=[1024, 4096 - 1024],\n",
" aux_channels=['R', 'G', 'B'],\n",
" guidance_scale=[3.0, 0.0],\n",
" model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2777bd89bbef428aaae750480cbdf123",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set a prompt to condition on.\n",
"prompt = 'a yellow dinosaur'\n",
"\n",
"# Produce a sample from the model.\n",
"samples = None\n",
"for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))):\n",
" samples = x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pc = sampler.output_to_point_clouds(samples)[0]\n",
"fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (GPU)",
"language": "python",
"name": "gpu_env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
},
"vscode": {
"interpreter": {
"hash": "b270b0f43bc427bcab7703c037711644cc480aac7c1cc8d2940cfaf0b447ee2e"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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