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Add image downloading in case colab env #39

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Add image downloading in case colab env #39

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23 changes: 20 additions & 3 deletions notebooks/inpaint.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,17 @@
"device = th.device('cpu' if not has_cuda else 'cuda')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"if 'COLAB_GPU' in os.environ: # Downloading image in case running in colab env\n",
" !wget https://raw.githubusercontent.com/openai/glide-text2im/main/notebooks/grass.png"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -57,7 +68,7 @@
"options = model_and_diffusion_defaults()\n",
"options['inpaint'] = True\n",
"options['use_fp16'] = has_cuda\n",
"options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling\n",
"options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling\n",
"model, diffusion = create_model_and_diffusion(**options)\n",
"model.eval()\n",
"if has_cuda:\n",
Expand All @@ -77,7 +88,7 @@
"options_up = model_and_diffusion_defaults_upsampler()\n",
"options_up['inpaint'] = True\n",
"options_up['use_fp16'] = has_cuda\n",
"options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling\n",
"options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling\n",
"model_up, diffusion_up = create_model_and_diffusion(**options_up)\n",
"model_up.eval()\n",
"if has_cuda:\n",
Expand All @@ -95,10 +106,11 @@
"source": [
"def show_images(batch: th.Tensor):\n",
" \"\"\" Display a batch of images inline. \"\"\"\n",
" scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()\n",
" scaled = ((batch + 1)*127.5).round().clamp(0, 255).to(th.uint8).cpu()\n",
" reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])\n",
" display(Image.fromarray(reshaped.numpy()))\n",
"\n",
"\n",
"def read_image(path: str, size: int = 256) -> Tuple[th.Tensor, th.Tensor]:\n",
" pil_img = Image.open(path).convert('RGB')\n",
" pil_img = pil_img.resize((size, size), resample=Image.BICUBIC)\n",
Expand Down Expand Up @@ -173,6 +185,7 @@
" inpaint_mask=source_mask_64.repeat(full_batch_size, 1, 1, 1).to(device),\n",
")\n",
"\n",
"\n",
"# Create an classifier-free guidance sampling function\n",
"def model_fn(x_t, ts, **kwargs):\n",
" half = x_t[: len(x_t) // 2]\n",
Expand All @@ -184,6 +197,7 @@
" eps = th.cat([half_eps, half_eps], dim=0)\n",
" return th.cat([eps, rest], dim=1)\n",
"\n",
"\n",
"def denoised_fn(x_start):\n",
" # Force the model to have the exact right x_start predictions\n",
" # for the part of the image which is known.\n",
Expand All @@ -192,6 +206,7 @@
" + model_kwargs['inpaint_image'] * model_kwargs['inpaint_mask']\n",
" )\n",
"\n",
"\n",
"# Sample from the base model.\n",
"model.del_cache()\n",
"samples = diffusion.p_sample_loop(\n",
Expand Down Expand Up @@ -245,6 +260,7 @@
" inpaint_mask=source_mask_256.repeat(batch_size, 1, 1, 1).to(device),\n",
")\n",
"\n",
"\n",
"def denoised_fn(x_start):\n",
" # Force the model to have the exact right x_start predictions\n",
" # for the part of the image which is known.\n",
Expand All @@ -253,6 +269,7 @@
" + model_kwargs['inpaint_image'] * model_kwargs['inpaint_mask']\n",
" )\n",
"\n",
"\n",
"# Sample from the base model.\n",
"model_up.del_cache()\n",
"up_shape = (batch_size, 3, options_up[\"image_size\"], options_up[\"image_size\"])\n",
Expand Down