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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from shap_e.models.download import load_model\n",
"from shap_e.util.data_util import load_or_create_multimodal_batch\n",
"from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xm = load_model('transmitter', device=device)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"example_data/cactus/object.obj\"\n",
"\n",
"# This may take a few minutes, since it requires rendering the model twice\n",
"# in two different modes.\n",
"batch = load_or_create_multimodal_batch(\n",
" device,\n",
" model_path=model_path,\n",
" mv_light_mode=\"basic\",\n",
" mv_image_size=256,\n",
" cache_dir=\"example_data/cactus/cached\",\n",
" verbose=True, # this will show Blender output during renders\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" latent = xm.encoder.encode_to_bottleneck(batch)\n",
"\n",
" render_mode = 'stf' # you can change this to 'nerf'\n",
" size = 128 # recommended that you lower resolution when using nerf\n",
"\n",
" cameras = create_pan_cameras(size, device)\n",
" images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
" display(gif_widget(images))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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