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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2020 Erik Härkönen. All rights reserved.\n",
"# This file is licensed to you under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License. You may obtain a copy\n",
"# of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"# Unless required by applicable law or agreed to in writing, software distributed under\n",
"# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS\n",
"# OF ANY KIND, either express or implied. See the License for the specific language\n",
"# governing permissions and limitations under the License.\n",
"\n",
"# Recreate StyleGAN1 style mixing image grid\n",
"from IPython.display import Image as IPyImage\n",
"from IPython.core.display import HTML \n",
"#IPyImage('style_mixing.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from notebook_init import *\n",
"\n",
"layer_names = [f'generator.layers.{i}' for i in range(14)] # annotate all shapes\n",
"inst = get_instrumented_model('BigGAN-512', 'promontory', layer_names, device)\n",
"model = inst.model\n",
"inst.close()\n",
"\n",
"torch.manual_seed(0)\n",
"np.random.seed(0)\n",
"\n",
"makedirs('out', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def generate(trunc, cls, custom_seeds=[], layers=[0, 2, 4], N=5):\n",
" inst.remove_edits()\n",
" model.set_output_class(cls)\n",
" \n",
" custom_seeds = custom_seeds[:N] # limit to N images\n",
" seeds = np.random.randint(np.iinfo(np.int32).max, size=N)\n",
" seeds[:len(custom_seeds)] = custom_seeds\n",
" print(seeds, trunc, cls)\n",
" \n",
" latents = [model.sample_latent(1, truncation=trunc, seed=s) for s in seeds]\n",
" latent_a = latents[0]\n",
" out_a = model.sample_np(latent_a)\n",
"\n",
" outputs = [model.sample_np(z) for z in latents]\n",
" empty = np.ones_like(outputs[0])\n",
"\n",
" # Inputs B\n",
" row0 = np.hstack([empty] + outputs[1:])\n",
" rows = [row0]\n",
"\n",
" # Mix style starting from layer l\n",
" for layer_num in layers:\n",
" inst.close()\n",
" layer_name = f'generator.layers.{layer_num}'\n",
" inst.retain_layer(layer_name)\n",
"\n",
" imgs = []\n",
"\n",
" imgs.append(out_a)\n",
" model.partial_forward(latent_a, layer_name)\n",
" feat_a = inst.retained_features()[layer_name].detach()\n",
"\n",
" # Generate hybrids\n",
" for i in range(1, len(latents)):\n",
" # Use latent of B, early activations of A\n",
" inst.edit_layer(layer_name, ablation=1.0, replacement=feat_a)\n",
" out_b = model.sample_np(latents[i])\n",
" imgs.append(out_b)\n",
"\n",
" rows.append(np.hstack(imgs))\n",
"\n",
" grid = np.vstack(rows)\n",
" im = Image.fromarray((grid*255).astype(np.uint8))\n",
" im.save(f'out/grid_{cls}.png')\n",
"\n",
" plt.figure(figsize=(15,15))\n",
" plt.imshow(grid)\n",
" plt.axis('off')\n",
" plt.show()\n",
"\n",
" from IPython.display import Javascript, display\n",
" \n",
" if 0:\n",
" display(Javascript(\"\"\"\n",
" require(\n",
" [\"base/js/dialog\"], \n",
" function(dialog) {\n",
" dialog.modal({\n",
" title: 'Debug',\n",
" body: 'Please close viewer window before continuing',\n",
" buttons: {\n",
" 'Close': {}\n",
" }\n",
" });\n",
" }\n",
" );\n",
" \"\"\"))\n",
" im.show()\n",
" \n",
"\n",
"#generate(0.95, 'irish_setter', [716257571, 216337755, 602801999, 1027629257])\n",
"generate(0.95, 'barn', [237774802, 1498010115, 105741908, 857168362, 639216961])\n",
"#generate(0.95, 'coral_reef')\n",
"#generate(0.95, 'lighthouse', [1573600108])\n",
"#generate(0.95, 'seashore', [1891640828, 130794492, 1321047179, 750963629])\n",
"generate(0.95, 'castle', [995150904, 530702035])\n",
"#generate(0.95, 'golden_retriever', [])\n",
"#generate(0.95, 'goldfinch', [])\n",
"#generate(0.95, 'indigo_bunting', [1624898412])\n",
"#generate(0.95, 'red_wine', [])\n",
"#generate(0.95, 'anemone_fish', [11610217])\n",
"#generate(0.95, 'earthstar', [])\n",
"#generate(0.95, 'beer_bottle', [485603871, 527619953])\n",
"#generate(0.8, 'beer_glass', [])\n",
"#generate(0.95, 'church', [628962584, 1700971930]) # , 371570218, 1137007398, 1412786664\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show every layer for given content and style pair\n",
"def blend(cls, seed1, seed2):\n",
" inst.remove_edits()\n",
" model.set_output_class(cls)\n",
" z1 = model.sample_latent(seed=seed1)\n",
" z2 = model.sample_latent(seed=seed2)\n",
"\n",
" out1 = model.sample_np(z1)\n",
" out2 = model.sample_np(z2)\n",
"\n",
" intermed = []\n",
" for layer in range(0, 6, 1):\n",
" inst.close()\n",
" inst.remove_edits()\n",
" layer_name = f'generator.layers.{layer}'\n",
" inst.retain_layer(layer_name)\n",
"\n",
" # Content features up to layer\n",
" model.partial_forward(z1, layer_name)\n",
" feat = inst.retained_features()[layer_name].detach()\n",
"\n",
" # New style\n",
" inst.edit_layer(layer_name, ablation=1.0, replacement=feat)\n",
" intermed.append(model.sample_np(z2))\n",
"\n",
" imgs = np.hstack([out1] + intermed[::-1] + [out2])\n",
" im = Image.fromarray((imgs*255).astype(np.uint8))\n",
" im.save(f'out/{cls}_style_layer_comp.png')\n",
"\n",
" # Style blending by latent interpolation (does not keep geometry consistent)\n",
" inst.remove_edits()\n",
" lerp = lambda x,y,a : a*x+(1-a)*y\n",
" imgs_latent_interp = []\n",
" for a in np.linspace(0.0, 1.0, 8):\n",
" z = lerp(z2, z1, a)\n",
" imgs_latent_interp.append(model.sample_np(z))\n",
"\n",
" imgs_latent_interp = np.hstack(imgs_latent_interp)\n",
" im = Image.fromarray((imgs_latent_interp*255).astype(np.uint8))\n",
" im.save(f'out/{cls}_style_latent_comp.png')\n",
"\n",
"\n",
"blend('castle', 995150904, 1171165061)\n",
"blend('church', 628962584, 1700971930)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.5"
}
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"nbformat": 4,
"nbformat_minor": 2
}
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