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1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "source": [
6
+ "# Trinket! Stable Inversion\n",
7
+ "A cheap alternative to finetuning stable diffusion, by [crumb](https://twitter.com/aicrumb)\n",
8
+ "\n",
9
+ "Finetunes the embedding layer like Textual-Inversion does, but on CLIP Text/Image pairs instead of reconstruction loss from Stable Diffusion. Lower memory requirements + (sometimes) faster than finetuning the traditional way."
10
+ ],
11
+ "metadata": {
12
+ "id": "mSXDq5-qTSF6"
13
+ },
14
+ "id": "mSXDq5-qTSF6"
15
+ },
16
+ {
17
+ "cell_type": "code",
18
+ "source": [
19
+ "!pip install git+https://github.com/openai/CLIP -q\n",
20
+ "!pip install bitsandbytes -q"
21
+ ],
22
+ "metadata": {
23
+ "id": "yB-XeS51NmI2"
24
+ },
25
+ "id": "yB-XeS51NmI2",
26
+ "execution_count": 1,
27
+ "outputs": []
28
+ },
29
+ {
30
+ "cell_type": "code",
31
+ "source": [
32
+ "import bitsandbytes as bnb\n",
33
+ "import torchvision\n",
34
+ "from torchvision import transforms\n",
35
+ "from tqdm.auto import *\n",
36
+ "from torch import nn, optim\n",
37
+ "from torch.nn import functional as F\n",
38
+ "from PIL import Image\n",
39
+ "import requests\n",
40
+ "from io import BytesIO\n",
41
+ "import torch\n",
42
+ "import random\n",
43
+ "import clip\n",
44
+ "import pandas as pd"
45
+ ],
46
+ "metadata": {
47
+ "id": "x1OogszooEi6",
48
+ "colab": {
49
+ "base_uri": "https://localhost:8080/"
50
+ },
51
+ "outputId": "a55a628d-aafc-407e-a2a9-fb03b8a0896a"
52
+ },
53
+ "id": "x1OogszooEi6",
54
+ "execution_count": 2,
55
+ "outputs": [
56
+ {
57
+ "output_type": "stream",
58
+ "name": "stdout",
59
+ "text": [
60
+ "\n",
61
+ "===================================BUG REPORT===================================\n",
62
+ "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
63
+ "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
64
+ "================================================================================\n",
65
+ "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
66
+ "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
67
+ "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
68
+ "CUDA SETUP: Detected CUDA version 111\n",
69
+ "CUDA SETUP: Loading binary /usr/local/lib/python3.7/dist-packages/bitsandbytes/libbitsandbytes_cuda111.so...\n"
70
+ ]
71
+ },
72
+ {
73
+ "output_type": "stream",
74
+ "name": "stderr",
75
+ "text": [
76
+ "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:99: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
77
+ " f'{candidate_env_vars[\"LD_LIBRARY_PATH\"]} did not contain '\n",
78
+ "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:21: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('{\"kernelManagerProxyPort\"'), PosixPath('\"/usr/local/bin/dap_multiplexer\",\"enableLsp\"'), PosixPath('\"172.28.0.3\",\"jupyterArgs\"'), PosixPath('[\"--ip=172.28.0.2\"],\"debugAdapterMultiplexerPath\"'), PosixPath('true}'), PosixPath('6000,\"kernelManagerProxyHost\"')}\n",
79
+ " \"WARNING: The following directories listed in your path were found to \"\n",
80
+ "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:21: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
81
+ " \"WARNING: The following directories listed in your path were found to \"\n",
82
+ "/usr/local/lib/python3.7/dist-packages/bitsandbytes/cuda_setup/paths.py:21: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
83
+ " \"WARNING: The following directories listed in your path were found to \"\n"
84
+ ]
85
+ }
86
+ ]
87
+ },
88
+ {
89
+ "cell_type": "code",
90
+ "execution_count": 3,
91
+ "id": "22b6861e-289f-4779-bbf1-40bfd1327823",
92
+ "metadata": {
93
+ "colab": {
94
+ "base_uri": "https://localhost:8080/"
95
+ },
96
+ "id": "22b6861e-289f-4779-bbf1-40bfd1327823",
97
+ "outputId": "fd96d25d-8cbf-43d2-8d5b-983856bf2a3a"
98
+ },
99
+ "outputs": [
100
+ {
101
+ "output_type": "stream",
102
+ "name": "stdout",
103
+ "text": [
104
+ "Loaded CLIP\n"
105
+ ]
106
+ }
107
+ ],
108
+ "source": [
109
+ "# the stable diffusion model uses L/14 by default\n",
110
+ "clip_model, _ = clip.load(\"ViT-L/14\", jit=False)\n",
111
+ "clip_model = clip_model.cuda()\n",
112
+ "print(\"Loaded CLIP\")"
113
+ ]
114
+ },
115
+ {
116
+ "cell_type": "code",
117
+ "execution_count": 4,
118
+ "id": "eaa9bde8-3fdd-48df-aa36-8b88cec285be",
119
+ "metadata": {
120
+ "id": "eaa9bde8-3fdd-48df-aa36-8b88cec285be"
121
+ },
122
+ "outputs": [],
123
+ "source": [
124
+ "bs = 16 # need a large batch size for the contrastive loss to work properly\n",
125
+ "steps = 64\n",
126
+ "epochs = 4\n",
127
+ "lr = 1e-3"
128
+ ]
129
+ },
130
+ {
131
+ "cell_type": "code",
132
+ "execution_count": 5,
133
+ "id": "0667d187-2c3e-40b8-a2fd-b408d5f144c5",
134
+ "metadata": {
135
+ "colab": {
136
+ "base_uri": "https://localhost:8080/",
137
+ "height": 206
138
+ },
139
+ "id": "0667d187-2c3e-40b8-a2fd-b408d5f144c5",
140
+ "outputId": "b8f6056a-c2ac-4b89-e222-0d47705617e9"
141
+ },
142
+ "outputs": [
143
+ {
144
+ "output_type": "execute_result",
145
+ "data": {
146
+ "text/plain": [
147
+ " Unnamed: 0 url \\\n",
148
+ "0 0 https://cdn.donmai.us/original/51/bb/51bb44d69... \n",
149
+ "1 1 https://cdn.donmai.us/original/4f/8e/4f8e5eaba... \n",
150
+ "2 2 https://cdn.donmai.us/original/c8/54/c85428d89... \n",
151
+ "3 3 https://cdn.donmai.us/original/e9/fc/e9fcc788e... \n",
152
+ "4 4 https://cdn.donmai.us/original/8a/63/8a639d21b... \n",
153
+ "\n",
154
+ " prompt \n",
155
+ "0 genshin_impact boo_tao_(genshin_impact) hu_tao... \n",
156
+ "1 genshin_impact arlecchino_(genshin_impact) cap... \n",
157
+ "2 genshin_impact fischl_(ein_immernachtstraum)_(... \n",
158
+ "3 genshin_impact eula_(genshin_impact) 1girl :o ... \n",
159
+ "4 genshin_impact kuki_shinobu 1girl breasts brid... "
160
+ ],
161
+ "text/html": [
162
+ "\n",
163
+ " <div id=\"df-b1359ba3-d298-4260-9864-de09e0a61fd0\">\n",
164
+ " <div class=\"colab-df-container\">\n",
165
+ " <div>\n",
166
+ "<style scoped>\n",
167
+ " .dataframe tbody tr th:only-of-type {\n",
168
+ " vertical-align: middle;\n",
169
+ " }\n",
170
+ "\n",
171
+ " .dataframe tbody tr th {\n",
172
+ " vertical-align: top;\n",
173
+ " }\n",
174
+ "\n",
175
+ " .dataframe thead th {\n",
176
+ " text-align: right;\n",
177
+ " }\n",
178
+ "</style>\n",
179
+ "<table border=\"1\" class=\"dataframe\">\n",
180
+ " <thead>\n",
181
+ " <tr style=\"text-align: right;\">\n",
182
+ " <th></th>\n",
183
+ " <th>Unnamed: 0</th>\n",
184
+ " <th>url</th>\n",
185
+ " <th>prompt</th>\n",
186
+ " </tr>\n",
187
+ " </thead>\n",
188
+ " <tbody>\n",
189
+ " <tr>\n",
190
+ " <th>0</th>\n",
191
+ " <td>0</td>\n",
192
+ " <td>https://cdn.donmai.us/original/51/bb/51bb44d69...</td>\n",
193
+ " <td>genshin_impact boo_tao_(genshin_impact) hu_tao...</td>\n",
194
+ " </tr>\n",
195
+ " <tr>\n",
196
+ " <th>1</th>\n",
197
+ " <td>1</td>\n",
198
+ " <td>https://cdn.donmai.us/original/4f/8e/4f8e5eaba...</td>\n",
199
+ " <td>genshin_impact arlecchino_(genshin_impact) cap...</td>\n",
200
+ " </tr>\n",
201
+ " <tr>\n",
202
+ " <th>2</th>\n",
203
+ " <td>2</td>\n",
204
+ " <td>https://cdn.donmai.us/original/c8/54/c85428d89...</td>\n",
205
+ " <td>genshin_impact fischl_(ein_immernachtstraum)_(...</td>\n",
206
+ " </tr>\n",
207
+ " <tr>\n",
208
+ " <th>3</th>\n",
209
+ " <td>3</td>\n",
210
+ " <td>https://cdn.donmai.us/original/e9/fc/e9fcc788e...</td>\n",
211
+ " <td>genshin_impact eula_(genshin_impact) 1girl :o ...</td>\n",
212
+ " </tr>\n",
213
+ " <tr>\n",
214
+ " <th>4</th>\n",
215
+ " <td>4</td>\n",
216
+ " <td>https://cdn.donmai.us/original/8a/63/8a639d21b...</td>\n",
217
+ " <td>genshin_impact kuki_shinobu 1girl breasts brid...</td>\n",
218
+ " </tr>\n",
219
+ " </tbody>\n",
220
+ "</table>\n",
221
+ "</div>\n",
222
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1359ba3-d298-4260-9864-de09e0a61fd0')\"\n",
223
+ " title=\"Convert this dataframe to an interactive table.\"\n",
224
+ " style=\"display:none;\">\n",
225
+ " \n",
226
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
227
+ " width=\"24px\">\n",
228
+ " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
229
+ " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
230
+ " </svg>\n",
231
+ " </button>\n",
232
+ " \n",
233
+ " <style>\n",
234
+ " .colab-df-container {\n",
235
+ " display:flex;\n",
236
+ " flex-wrap:wrap;\n",
237
+ " gap: 12px;\n",
238
+ " }\n",
239
+ "\n",
240
+ " .colab-df-convert {\n",
241
+ " background-color: #E8F0FE;\n",
242
+ " border: none;\n",
243
+ " border-radius: 50%;\n",
244
+ " cursor: pointer;\n",
245
+ " display: none;\n",
246
+ " fill: #1967D2;\n",
247
+ " height: 32px;\n",
248
+ " padding: 0 0 0 0;\n",
249
+ " width: 32px;\n",
250
+ " }\n",
251
+ "\n",
252
+ " .colab-df-convert:hover {\n",
253
+ " background-color: #E2EBFA;\n",
254
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
255
+ " fill: #174EA6;\n",
256
+ " }\n",
257
+ "\n",
258
+ " [theme=dark] .colab-df-convert {\n",
259
+ " background-color: #3B4455;\n",
260
+ " fill: #D2E3FC;\n",
261
+ " }\n",
262
+ "\n",
263
+ " [theme=dark] .colab-df-convert:hover {\n",
264
+ " background-color: #434B5C;\n",
265
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
266
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
267
+ " fill: #FFFFFF;\n",
268
+ " }\n",
269
+ " </style>\n",
270
+ "\n",
271
+ " <script>\n",
272
+ " const buttonEl =\n",
273
+ " document.querySelector('#df-b1359ba3-d298-4260-9864-de09e0a61fd0 button.colab-df-convert');\n",
274
+ " buttonEl.style.display =\n",
275
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
276
+ "\n",
277
+ " async function convertToInteractive(key) {\n",
278
+ " const element = document.querySelector('#df-b1359ba3-d298-4260-9864-de09e0a61fd0');\n",
279
+ " const dataTable =\n",
280
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
281
+ " [key], {});\n",
282
+ " if (!dataTable) return;\n",
283
+ "\n",
284
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
285
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
286
+ " + ' to learn more about interactive tables.';\n",
287
+ " element.innerHTML = '';\n",
288
+ " dataTable['output_type'] = 'display_data';\n",
289
+ " await google.colab.output.renderOutput(dataTable, element);\n",
290
+ " const docLink = document.createElement('div');\n",
291
+ " docLink.innerHTML = docLinkHtml;\n",
292
+ " element.appendChild(docLink);\n",
293
+ " }\n",
294
+ " </script>\n",
295
+ " </div>\n",
296
+ " </div>\n",
297
+ " "
298
+ ]
299
+ },
300
+ "metadata": {},
301
+ "execution_count": 5
302
+ }
303
+ ],
304
+ "source": [
305
+ "# ultimately you just need >1000 prompt+url pairs in lists named prompts and urls, however you load it is fine\n",
306
+ "# this is how i scraped them so this is how i load them\n",
307
+ "# you can scrape danbooru/safebooru/others with pybooru with this script from waifu-diffusion https://github.com/harubaru/waifu-diffusion/blob/main/danbooru_data/scrape.py\n",
308
+ "df = pd.read_csv(\"/content/genshin.csv\")\n",
309
+ "prompts = df['prompt']#[:steps*bs]\n",
310
+ "urls = df['url']#[:steps*bs\n",
311
+ "df.head()"
312
+ ]
313
+ },
314
+ {
315
+ "cell_type": "code",
316
+ "source": [
317
+ "print(len(prompts))"
318
+ ],
319
+ "metadata": {
320
+ "colab": {
321
+ "base_uri": "https://localhost:8080/"
322
+ },
323
+ "id": "6lYQYqKEZKhv",
324
+ "outputId": "7d26b946-ce1e-41ed-abaf-a4efed5d02cc"
325
+ },
326
+ "id": "6lYQYqKEZKhv",
327
+ "execution_count": 6,
328
+ "outputs": [
329
+ {
330
+ "output_type": "stream",
331
+ "name": "stdout",
332
+ "text": [
333
+ "1200\n"
334
+ ]
335
+ }
336
+ ]
337
+ },
338
+ {
339
+ "cell_type": "code",
340
+ "execution_count": 7,
341
+ "id": "12fce080-73f3-430f-ba2c-99f265316b66",
342
+ "metadata": {
343
+ "id": "12fce080-73f3-430f-ba2c-99f265316b66"
344
+ },
345
+ "outputs": [],
346
+ "source": [
347
+ "clip_model.token_embedding.weight.requires_grad = True\n",
348
+ "# opt = optim.Adam([clip_model.token_embedding.weight], lr)\n",
349
+ "opt = bnb.optim.AdamW8bit([clip_model.token_embedding.weight], lr)"
350
+ ]
351
+ },
352
+ {
353
+ "cell_type": "code",
354
+ "execution_count": 8,
355
+ "id": "9a4b1696-a8d9-4eea-a8e0-ba372d450619",
356
+ "metadata": {
357
+ "id": "9a4b1696-a8d9-4eea-a8e0-ba372d450619"
358
+ },
359
+ "outputs": [],
360
+ "source": [
361
+ "# functions from another project of mine that's a bit messy\n",
362
+ "def fix_to_224(pil_image):\n",
363
+ " width, height = pil_image.size\n",
364
+ " if width < height:\n",
365
+ " new_width = 224\n",
366
+ " new_height = int(new_width * height / width)\n",
367
+ " else:\n",
368
+ " new_height = 224\n",
369
+ " new_width = int(new_height * width / height)\n",
370
+ " return pil_image.resize((new_width, new_height))\n",
371
+ "def to_tensor_and_center_crop(pil_image):\n",
372
+ " tensor = torchvision.transforms.functional.to_tensor(pil_image)\n",
373
+ " center_crop = torchvision.transforms.functional.center_crop(tensor, (224, 224))\n",
374
+ " return center_crop\n",
375
+ "def fix(img):\n",
376
+ " return to_tensor_and_center_crop(fix_to_224(img))\n",
377
+ "\n",
378
+ "iter_prompts = iter(prompts)\n",
379
+ "iter_urls = iter(urls)\n",
380
+ "def get_batch(size=8, step=1, steps_per_epoch=128, epoch=1, total_epochs=1):\n",
381
+ " to_tensor = transforms.ToTensor()\n",
382
+ " x = []\n",
383
+ " y = []\n",
384
+ " total_steps = total_epochs*steps_per_epoch\n",
385
+ " current_fraction = (epoch*steps_per_epoch-steps_per_epoch+step) / total_steps\n",
386
+ "\n",
387
+ " while len(x) < size:\n",
388
+ " # uncomment these and space the big block right one tab\n",
389
+ " # if you're using a set that might have dead urls\n",
390
+ " try:\n",
391
+ " url = next(iter_urls)\n",
392
+ " response = requests.get(url)\n",
393
+ " img = Image.open(BytesIO(response.content))\n",
394
+ " img = img.convert(\"RGB\")\n",
395
+ " img = fix(img)\n",
396
+ " x.append(img.unsqueeze(0))\n",
397
+ "\n",
398
+ " p = next(iter_prompts)\n",
399
+ "\n",
400
+ " # comment out these lines if you aren't using danbooru tags\n",
401
+ " p = p.split(\" \")\n",
402
+ " if current_fraction < 0.75: random.shuffle(p)\n",
403
+ " if current_fraction > 0.5: # halfway through training, start dropping half of the tags\n",
404
+ " p = p[:len(p)//2]\n",
405
+ " if current_fraction > 0.75: # 3/4 through training, start dropping 3/4 of the tags\n",
406
+ " p = p[:4]\n",
407
+ " p = \" \".join(p)\n",
408
+ " y.append(p)\n",
409
+ " except KeyboardInterrupt:\n",
410
+ " print('Interrupted')\n",
411
+ " break\n",
412
+ " except:\n",
413
+ " pass\n",
414
+ " \n",
415
+ " x = torch.cat(x, 0)\n",
416
+ " x = clip_model.encode_image(x.cuda())\n",
417
+ " \n",
418
+ " y = clip.tokenize(y, truncate=True)\n",
419
+ " y = clip_model.encode_text(y.cuda())\n",
420
+ " return x, y"
421
+ ]
422
+ },
423
+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "203637dd-441e-4b43-944d-48ffa126e826",
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 182,
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+ "referenced_widgets": [
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+ ]
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+ },
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+ "id": "203637dd-441e-4b43-944d-48ffa126e826",
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+ "outputId": "4ea5f3da-86fa-4910-d919-fc1ef0d8f007"
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+ },
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+ "outputs": [
471
+ {
472
+ "output_type": "display_data",
473
+ "data": {
474
+ "text/plain": [
475
+ " 0%| | 0/4 [00:00<?, ?it/s]"
476
+ ],
477
+ "application/vnd.jupyter.widget-view+json": {
478
+ "version_major": 2,
479
+ "version_minor": 0,
480
+ "model_id": "cfab11c900c44070b2dc032034b9a65d"
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+ }
482
+ },
483
+ "metadata": {}
484
+ },
485
+ {
486
+ "output_type": "display_data",
487
+ "data": {
488
+ "text/plain": [
489
+ " 0%| | 0/64 [00:00<?, ?it/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "39487ee2262647a79ed05bd89413ec25"
495
+ }
496
+ },
497
+ "metadata": {}
498
+ },
499
+ {
500
+ "output_type": "stream",
501
+ "name": "stderr",
502
+ "text": [
503
+ "/usr/local/lib/python3.7/dist-packages/PIL/Image.py:960: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
504
+ " \"Palette images with Transparency expressed in bytes should be \"\n",
505
+ "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. \n",
506
+ " warnings.warn(str(msg))\n"
507
+ ]
508
+ },
509
+ {
510
+ "output_type": "display_data",
511
+ "data": {
512
+ "text/plain": [
513
+ " 0%| | 0/64 [00:00<?, ?it/s]"
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+ ],
515
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "94c994a72e6e47a581ef47eafc4f8621"
519
+ }
520
+ },
521
+ "metadata": {}
522
+ }
523
+ ],
524
+ "source": [
525
+ "losses = []\n",
526
+ "\n",
527
+ "for epoch in trange(epochs):\n",
528
+ " pbar = trange(steps)\n",
529
+ " iter_prompts = iter(prompts)\n",
530
+ " iter_urls = iter(urls)\n",
531
+ "\n",
532
+ " for i in pbar:\n",
533
+ " x, y = get_batch(bs, i, steps, epoch, epochs)\n",
534
+ " loss = (x-y).pow(2).mean()\n",
535
+ " # loss = spherical_distance_loss(x,y).mean() # TODO: try whatever loss they use in the CLIP paper instead\n",
536
+ " loss.backward()\n",
537
+ " opt.step()\n",
538
+ " opt.zero_grad()\n",
539
+ "\n",
540
+ " pbar.set_description(str(loss.item()))\n",
541
+ " losses.append(loss.item())"
542
+ ]
543
+ },
544
+ {
545
+ "cell_type": "code",
546
+ "source": [
547
+ "import matplotlib.pyplot as plt\n",
548
+ "\n",
549
+ "# hacky ema plot\n",
550
+ "losses_df = pd.DataFrame(losses, columns=['loss'])\n",
551
+ "losses_plot = losses_df.ewm(alpha=0.05).mean()['loss']\n",
552
+ "plt.plot(losses_plot)\n",
553
+ "plt.plot(losses)"
554
+ ],
555
+ "metadata": {
556
+ "id": "J5QkXxh5PI04"
557
+ },
558
+ "id": "J5QkXxh5PI04",
559
+ "execution_count": null,
560
+ "outputs": []
561
+ },
562
+ {
563
+ "cell_type": "code",
564
+ "source": [
565
+ "torch.save(clip_model.token_embedding.weight, \"token_embeddings.pt\")"
566
+ ],
567
+ "metadata": {
568
+ "id": "MdEjBt3hRIRB"
569
+ },
570
+ "id": "MdEjBt3hRIRB",
571
+ "execution_count": null,
572
+ "outputs": []
573
+ },
574
+ {
575
+ "cell_type": "code",
576
+ "source": [
577
+ "#@markdown 🔔\n",
578
+ "from google.colab import output\n",
579
+ "output.eval_js('new Audio(\"https://freesound.org/data/previews/80/80921_1022651-lq.ogg\").play()')"
580
+ ],
581
+ "metadata": {
582
+ "cellView": "form",
583
+ "id": "IU0mqpyVTQzt"
584
+ },
585
+ "id": "IU0mqpyVTQzt",
586
+ "execution_count": null,
587
+ "outputs": []
588
+ },
589
+ {
590
+ "cell_type": "markdown",
591
+ "source": [
592
+ "### Upload to 🤗"
593
+ ],
594
+ "metadata": {
595
+ "id": "E5TP5qjAkTjL"
596
+ },
597
+ "id": "E5TP5qjAkTjL"
598
+ },
599
+ {
600
+ "cell_type": "code",
601
+ "source": [
602
+ "#@markdown Log in\n",
603
+ "!pip install huggingface-hub -q\n",
604
+ "from huggingface_hub import notebook_login\n",
605
+ "notebook_login()"
606
+ ],
607
+ "metadata": {
608
+ "id": "bUdMc0o4kBYx",
609
+ "cellView": "form"
610
+ },
611
+ "id": "bUdMc0o4kBYx",
612
+ "execution_count": null,
613
+ "outputs": []
614
+ },
615
+ {
616
+ "cell_type": "code",
617
+ "source": [
618
+ "from huggingface_hub import HfApi\n",
619
+ "api = HfApi()\n",
620
+ "\n",
621
+ "#@markdown go ahead and create the repository on 🤗 before running this\n",
622
+ "my_repo = \"user/my-stable-inversion\" #@param {type:\"string\"}\n",
623
+ "\n",
624
+ "api.upload_file(\n",
625
+ " path_or_fileobj=\"token_embeddings.pt\",\n",
626
+ " path_in_repo=\"token_embeddings.pt\",\n",
627
+ " repo_id=my_repo,\n",
628
+ " repo_type=\"model\",\n",
629
+ ")"
630
+ ],
631
+ "metadata": {
632
+ "id": "WJ30dShA7koO"
633
+ },
634
+ "id": "WJ30dShA7koO",
635
+ "execution_count": null,
636
+ "outputs": []
637
+ },
638
+ {
639
+ "cell_type": "markdown",
640
+ "source": [
641
+ "how you can load the stable inversions into a diffusers-based notebook like [Doohickey](https://github.com/aicrumb/doohickey) might look something like this\n",
642
+ "\n",
643
+ "```\n",
644
+ "from huggingface_hub import hf_hub_download\n",
645
+ "\n",
646
+ "stable_inversion = \"user/my-stable-inversion\" #@param {type:\"string\"}\n",
647
+ "if len(stable_inversion)>1:\n",
648
+ " g = hf_hub_download(repo_id=stable_inversion, filename=\"token_embeddings.pt\")\n",
649
+ " text_encoder.text_model.embeddings.token_embedding.weight = torch.load(g)\n",
650
+ "```"
651
+ ],
652
+ "metadata": {
653
+ "id": "tIKiCVkHkFET"
654
+ },
655
+ "id": "tIKiCVkHkFET"
656
+ }
657
+ ],
658
+ "metadata": {
659
+ "kernelspec": {
660
+ "display_name": "Python 3 (ipykernel)",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.10.4"
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+ "collapsed_sections": []
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