Spaces:
Build error
Build error
File size: 10,355 Bytes
29cdbe6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
{
"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",
"# Teaser: sequence of 3 interesting edits\n",
"%matplotlib inline\n",
"from notebook_init import *\n",
"\n",
"rand = lambda : np.random.randint(np.iinfo(np.int32).max)\n",
"outdir = Path('out/figures/teaser')\n",
"makedirs(outdir, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def setup_model(model_name, class_name, layer_name):\n",
" global inst, model, lat_comp, lat_mean, lat_std\n",
"\n",
" use_w = 'StyleGAN' in model_name\n",
" inst = get_instrumented_model(model_name, class_name, layer_name, device, use_w=use_w, inst=inst)\n",
" model = inst.model\n",
"\n",
" pc_config = Config(components=80, n=1_000_000, batch_size=200,\n",
" layer=layer_name, model=model_name, output_class=class_name, use_w=use_w)\n",
" dump_name = get_or_compute(pc_config, inst)\n",
"\n",
" with np.load(dump_name) as data:\n",
" lat_comp = data['lat_comp']\n",
" lat_mean = data['lat_mean']\n",
" lat_std = data['lat_stdev']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def perform_edit(seeds, edit_sequence, save_images=False, crop=None):\n",
" max_figs = 1000 if save_images else 10\n",
"\n",
" for seed in seeds[:max_figs]:\n",
" w = model.sample_latent(1, seed=seed).cpu().numpy()\n",
" w = [w]*model.get_max_latents()\n",
" imgs = []\n",
" \n",
" # Starting point\n",
" imgs.append(model.sample_np(w))\n",
" \n",
" # Perform edits in order\n",
" for edit in edit_sequence:\n",
" (idx, start, end, strength, invert) = configs[edit]\n",
" \n",
" # Find out coordinate of w along PC\n",
" w_centered = w[0] - lat_mean\n",
" w_coord = np.sum(w_centered.reshape(-1)*lat_comp[idx].reshape(-1)) / lat_std[idx]\n",
" \n",
" # Invert property if desired (e.g. flip rotation)\n",
" # Otherwise reinforce existing\n",
" if invert:\n",
" sign = w_coord / np.abs(w_coord)\n",
" target = -sign*strength # opposite side of mean\n",
" else:\n",
" target = strength\n",
" \n",
" delta = target - w_coord # offset vector\n",
" \n",
" for l in range(start, end):\n",
" w[l] = w[l] + lat_comp[idx]*lat_std[idx]*delta\n",
" imgs.append(model.sample_np(w))\n",
" \n",
" # Crop away black borders\n",
" if crop:\n",
" imgs = [img[crop[0]:-crop[1], crop[2]:-crop[3], :] for img in imgs]\n",
"\n",
" if save_images:\n",
" # Save to disk\n",
" for i, img in enumerate(imgs):\n",
" Image.fromarray((img*255).astype(np.uint8)).save(outdir / f'teaser_{seed}_{i}.png')\n",
" \n",
" # Show in notebook\n",
" strip = np.hstack(imgs)\n",
" #strip = strip[::2, ::2, :] # 2x downscale for preview\n",
" plt.figure(figsize=(30,5))\n",
" plt.imshow(strip, interpolation='bilinear')\n",
" plt.axis('off')\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# (idx, edit_start, edit_end, strength, invert)\n",
"configs = {\n",
" # StyleGAN2 cars W\n",
" 'Redness': (22, 9, 11, -8, False),\n",
" 'Horizontal flip': ( 0, 0, 5, 2.0, True),\n",
" 'Add grass': (41, 9, 11, -18, False),\n",
" 'Blocky shape': (16, 3, 6, 25, False),\n",
"\n",
" # BigGAN-512 irish_setter\n",
" 'Move right': ( 0, 0, 15, -1.5, False),\n",
" 'Rotate': ( 3, 0, 15, -0.5, False),\n",
" 'Move back': ( 4, 0, 15, 2.5, False),\n",
" 'Zoom in': ( 6, 0, 15, -2.0, False),\n",
" 'Zoom out': (12, 0, 15, -4.0, False),\n",
" 'Sharpen BG': (13, 6, 9, 20.0, False),\n",
" 'Camera down': (15, 1, 6, -4.0, False),\n",
" 'Light right': (28, 7, 8, 30, False),\n",
" 'Pixelate': (46, 10, 11, -25, False),\n",
" 'Reeds': (61, 4, 8, -15, False),\n",
" 'Dry BG': (65, 6, 8, -30, False),\n",
" 'Grass length': (69, 5, 8, 15, False),\n",
"\n",
" # StyleGAN2 ffhq\n",
" 'frizzy_hair': (31, 2, 6, 20, False),\n",
" 'background_blur': (49, 6, 9, 20, False),\n",
" 'bald': (21, 2, 5, 20, False),\n",
" 'big_smile': (19, 4, 5, 20, False),\n",
" 'caricature_smile': (26, 3, 8, 13, False),\n",
" 'scary_eyes': (33, 6, 8, 20, False),\n",
" 'curly_hair': (47, 3, 6, 20, False),\n",
" 'dark_bg_shiny_hair': (13, 8, 9, 20, False),\n",
" 'dark_hair_and_light_pos': (14, 8, 9, 20, False),\n",
" 'dark_hair': (16, 8, 9, 20, False),\n",
" 'disgusted': (43, 6, 8, -30, False),\n",
" 'displeased': (36, 4, 7, 20, False),\n",
" 'eye_openness': (54, 7, 8, 20, False),\n",
" 'eye_wrinkles': (28, 6, 8, 20, False),\n",
" 'eyebrow_thickness': (37, 8, 9, 20, False),\n",
" 'face_roundness': (37, 0, 5, 20, False),\n",
" 'fearful_eyes': (54, 4, 10, 20, False),\n",
" 'hairline': (21, 4, 5, -20, False),\n",
" 'happy_frizzy_hair': (30, 0, 8, 20, False),\n",
" 'happy_elderly_lady': (27, 4, 7, 20, False),\n",
" 'head_angle_up': (11, 1, 4, 20, False),\n",
" 'huge_grin': (28, 4, 6, 20, False),\n",
" 'in_awe': (23, 3, 6, -15, False),\n",
" 'wide_smile': (23, 3, 6, 20, False),\n",
" 'large_jaw': (22, 3, 6, 20, False),\n",
" 'light_lr': (15, 8, 9, 10, False),\n",
" 'lipstick_and_age': (34, 6, 11, 20, False),\n",
" 'lipstick': (34, 10, 11, 20, False),\n",
" 'mascara_vs_beard': (41, 6, 9, 20, False),\n",
" 'nose_length': (51, 4, 5, -20, False),\n",
" 'elderly_woman': (34, 6, 7, 20, False),\n",
" 'overexposed': (27, 8, 18, 15, False),\n",
" 'screaming': (35, 3, 7, -15, False),\n",
" 'short_face': (32, 2, 6, -20, False),\n",
" 'show_front_teeth': (59, 4, 5, 40, False),\n",
" 'smile': (46, 4, 5, -20, False),\n",
" 'straight_bowl_cut': (20, 4, 5, -20, False),\n",
" 'sunlight_in_face': (10, 8, 9, 10, False),\n",
" 'trimmed_beard': (58, 7, 9, 20, False),\n",
" 'white_hair': (57, 7, 10, -24, False),\n",
" 'wrinkles': (20, 6, 7, -18, False),\n",
" 'boyishness': (8, 2, 5, 20, False),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# StyleGAN2 faces - emphasis on novel edits\n",
"setup_model('StyleGAN2', 'ffhq', 'style')\n",
"model.truncation = 0.7\n",
"model.use_w()\n",
"\n",
"seeds = [6293435, 2105448342] # + [rand() for _ in range(1)]\n",
"print(seeds)\n",
"edits = ['wrinkles', 'white_hair', 'in_awe', 'overexposed']\n",
"perform_edit(seeds, edits, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# StyleGAN2 cars\n",
"setup_model('StyleGAN2', 'car', 'style')\n",
"model.truncation = 0.6\n",
"model.use_w()\n",
"\n",
"seeds = [440749230] # + [rand() for _ in range(10)]\n",
"edits = ['Redness', 'Horizontal flip', 'Add grass', 'Blocky shape']\n",
"perform_edit(seeds, edits, True, crop=[64, 64, 1, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# BigGAN-512 irish setter\n",
"setup_model('BigGAN-512', 'husky', 'generator.gen_z')\n",
"model.set_output_class('irish_setter')\n",
"model.truncation = 0.6\n",
"\n",
"seeds = [489408325]# + [rand() for _ in range(10)]\n",
"edits = ['Rotate', 'Zoom out', 'Camera down', 'Reeds']\n",
"perform_edit(seeds, edits, True)"
]
},
{
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|