shape-e.ipynb

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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # text_to_3d.ipynb\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from shap_e.diffusion.sample import sample_latents\n",
    "from shap_e.diffusion.gaussian_diffusion import diffusion_from_config\n",
    "from shap_e.models.download import load_model, load_config\n",
    "from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "xm = load_model('transmitter', device=device)\n",
    "model = load_model('text300M', device=device)\n",
    "diffusion = diffusion_from_config(load_config('diffusion'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4463514fa1642f48f801a7aa9d144c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size = 1\n",
    "guidance_scale = 15.0\n",
    "prompt = \"a plush toy of a corgi nurse\"\n",
    "\n",
    "latents = sample_latents(\n",
    "    batch_size=batch_size,\n",
    "    model=model,\n",
    "    diffusion=diffusion,\n",
    "    guidance_scale=guidance_scale,\n",
    "    model_kwargs=dict(texts=[prompt] * batch_size),\n",
    "    progress=True,\n",
    "    clip_denoised=True,\n",
    "    use_fp16=True,\n",
    "    use_karras=True,\n",
    "    karras_steps=100,\n",
    "    sigma_min=1e-3,\n",
    "    sigma_max=160,\n",
    "    s_churn=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eeb9437bc9ef46598a6833de62b94595",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='<img src=\"data:image/gif;base64,R0lGODlhIAAgAIcAAMbe2sXd2bra1rfZ0r7W0rfQyLjOyK7PuLTKvLLIwq/Iv6zMsr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "render_mode = 'nerf' # you can change this to 'stf'\n",
    "size = 32 # this is the size of the renders; higher values take longer to render.\n",
    "\n",
    "cameras = create_pan_cameras(size, device)\n",
    "for i, latent in enumerate(latents):\n",
    "    images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
    "    display(gif_widget(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Example of saving the latents as meshes.\n",
    "from shap_e.util.notebooks import decode_latent_mesh\n",
    "import os\n",
    "\n",
    "name = prompt.replace(' ', '_')\n",
    "os.makedirs(f'./output/{name}', exist_ok=True)\n",
    "\n",
    "for i, latent in enumerate(latents):\n",
    "    with open(f'./output/{name}/{name}_{i}.ply', 'wb') as f:\n",
    "        decode_latent_mesh(xm, latent).tri_mesh().write_ply(f)\n",
    "        # decode_latent_mesh(xm, latent).write_ply(f) # color"
   ]
  }
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