File size: 13,395 Bytes
1d8a799
741bf32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
af807f7
741bf32
 
 
 
 
 
 
 
 
 
af807f7
741bf32
 
 
 
af807f7
741bf32
af807f7
 
 
 
 
741bf32
af807f7
741bf32
af807f7
 
 
 
 
 
741bf32
 
 
 
af807f7
741bf32
 
af807f7
 
 
 
741bf32
 
 
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
25862e8
af807f7
 
 
25862e8
 
af807f7
 
25862e8
af807f7
 
741bf32
 
 
 
af807f7
 
741bf32
af807f7
 
 
 
71c4de3
 
 
 
 
 
 
af807f7
741bf32
 
 
af807f7
741bf32
 
af807f7
 
741bf32
af807f7
 
741bf32
af807f7
741bf32
af807f7
 
 
 
 
 
 
25862e8
af807f7
 
 
25862e8
 
 
 
 
 
 
 
 
af807f7
25862e8
 
 
af807f7
 
25862e8
 
 
af807f7
 
 
 
 
 
 
 
741bf32
 
 
 
 
af807f7
741bf32
 
af807f7
741bf32
 
 
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
 
 
 
 
 
 
741bf32
 
 
 
 
af807f7
741bf32
 
af807f7
741bf32
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
25862e8
af807f7
 
 
 
 
 
 
 
 
 
 
25862e8
af807f7
 
 
 
 
25862e8
af807f7
 
 
 
 
741bf32
 
 
 
 
af807f7
741bf32
 
af807f7
741bf32
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
25862e8
af807f7
741bf32
 
 
 
 
af807f7
741bf32
 
af807f7
741bf32
 
 
af807f7
741bf32
af807f7
741bf32
 
af807f7
 
741bf32
 
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
741bf32
 
 
af807f7
741bf32
af807f7
741bf32
 
af807f7
741bf32
 
 
 
 
 
af807f7
741bf32
af807f7
741bf32
25862e8
af807f7
741bf32
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
741bf32
71c4de3
 
741bf32
 
 
 
 
af807f7
741bf32
 
af807f7
741bf32
 
 
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
 
25862e8
 
 
 
 
 
 
af807f7
 
741bf32
 
 
 
af807f7
741bf32
 
af807f7
 
 
 
 
741bf32
 
 
af807f7
741bf32
af807f7
741bf32
 
af807f7
 
741bf32
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
741bf32
 
 
 
 
af807f7
741bf32
 
af807f7
741bf32
af807f7
741bf32
 
 
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
71c4de3
af807f7
 
 
 
741bf32
 
 
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
 
 
 
 
 
 
 
 
 
 
25862e8
 
 
af807f7
 
 
 
25862e8
af807f7
 
 
741bf32
 
 
 
af807f7
741bf32
 
af807f7
741bf32
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
741bf32
af807f7
25862e8
 
 
 
 
 
 
 
af807f7
71c4de3
af807f7
741bf32
 
af807f7
741bf32
af807f7
741bf32
 
af807f7
741bf32
 
 
 
af807f7
741bf32
af807f7
741bf32
af807f7
741bf32
25862e8
af807f7
 
25862e8
741bf32
 
af807f7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "118UKH5bWCGa"
   },
   "source": [
    "# DALL·E mini - Inference pipeline\n",
    "\n",
    "*Generate images from a text prompt*\n",
    "\n",
    "<img src=\"https://github.com/borisdayma/dalle-mini/blob/main/img/logo.png?raw=true\" width=\"200\">\n",
    "\n",
    "This notebook illustrates [DALL·E mini](https://github.com/borisdayma/dalle-mini) inference pipeline.\n",
    "\n",
    "Just want to play? Use [the demo](https://huggingface.co/spaces/flax-community/dalle-mini).\n",
    "\n",
    "For more understanding of the model, refer to [the report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dS8LbaonYm3a"
   },
   "source": [
    "## 🛠️ Installation and set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uzjAM2GBYpZX",
    "outputId": "70550075-5204-4c56-dce4-4fff061a096c"
   },
   "outputs": [],
   "source": [
    "# Install required libraries\n",
    "!pip install -q transformers\n",
    "!pip install -q git+https://github.com/patil-suraj/vqgan-jax.git\n",
    "!pip install -q git+https://github.com/borisdayma/dalle-mini.git\n",
    "!pip install -q wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ozHzTkyv8cqU"
   },
   "source": [
    "We load required models:\n",
    "* dalle·mini for text to encoded images\n",
    "* VQGAN for decoding images\n",
    "* CLIP for scoring predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K6CxW2o42f-w"
   },
   "outputs": [],
   "source": [
    "# Model references\n",
    "\n",
    "# dalle-mini\n",
    "DALLE_MODEL = \"dalle-mini/dalle-mini/model-3bqwu04f:latest\"  # can be wandb artifact or 🤗 Hub or local folder\n",
    "DALLE_COMMIT_ID = None  # used only with 🤗 hub\n",
    "\n",
    "# VQGAN model\n",
    "VQGAN_REPO = \"dalle-mini/vqgan_imagenet_f16_16384\"\n",
    "VQGAN_COMMIT_ID = \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\"\n",
    "\n",
    "# CLIP model\n",
    "CLIP_REPO = \"openai/clip-vit-base-patch16\"\n",
    "CLIP_COMMIT_ID = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "# type used for computation - use bfloat16 on TPU's\n",
    "dtype = jnp.bfloat16 if jax.local_device_count() == 8 else jnp.float32\n",
    "\n",
    "# TODO:\n",
    "# - we currently have an issue with model.generate() in bfloat16\n",
    "# - https://github.com/google/jax/pull/9089 should fix it\n",
    "# - remove below line and test on TPU with next release of JAX\n",
    "dtype = jnp.float32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "92zYmvsQ38vL",
    "outputId": "909b0a3c-14cb-4722-8eb2-f876ff50257c"
   },
   "outputs": [],
   "source": [
    "# Load models & tokenizer\n",
    "from dalle_mini.model import DalleBart\n",
    "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
    "from transformers import AutoTokenizer, CLIPProcessor, FlaxCLIPModel\n",
    "import wandb\n",
    "\n",
    "# Load dalle-mini\n",
    "if \":\" in DALLE_MODEL:\n",
    "    # wandb artifact\n",
    "    artifact = wandb.Api().artifact(DALLE_MODEL)\n",
    "    # we only download required files (no need for opt_state which is large)\n",
    "    model_files = [\n",
    "        \"config.json\",\n",
    "        \"flax_model.msgpack\",\n",
    "        \"merges.txt\",\n",
    "        \"special_tokens_map.json\",\n",
    "        \"tokenizer.json\",\n",
    "        \"tokenizer_config.json\",\n",
    "        \"vocab.json\",\n",
    "    ]\n",
    "    for f in model_files:\n",
    "        artifact.get_path(f).download(\"model\")\n",
    "    model = DalleBart.from_pretrained(\"model\", dtype=dtype, abstract_init=True)\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\"model\")\n",
    "else:\n",
    "    # local folder or 🤗 Hub\n",
    "    model = DalleBart.from_pretrained(\n",
    "        DALLE_MODEL, revision=DALLE_COMMIT_ID, dtype=dtype, abstract_init=True\n",
    "    )\n",
    "    tokenizer = AutoTokenizer.from_pretrained(DALLE_MODEL, revision=DALLE_COMMIT_ID)\n",
    "\n",
    "# Load VQGAN\n",
    "vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
    "\n",
    "# Load CLIP\n",
    "clip = FlaxCLIPModel.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)\n",
    "processor = CLIPProcessor.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o_vH2X1tDtzA"
   },
   "source": [
    "Model parameters are replicated on each device for faster inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wtvLoM48EeVw"
   },
   "outputs": [],
   "source": [
    "from flax.jax_utils import replicate\n",
    "\n",
    "# convert model parameters for inference if requested\n",
    "if dtype == jnp.bfloat16:\n",
    "    model.params = model.to_bf16(model.params)\n",
    "\n",
    "model_params = replicate(model.params)\n",
    "vqgan_params = replicate(vqgan.params)\n",
    "clip_params = replicate(clip.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0A9AHQIgZ_qw"
   },
   "source": [
    "Model functions are compiled and parallelized to take advantage of multiple devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sOtoOmYsSYPz"
   },
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "# model inference\n",
    "@partial(jax.pmap, axis_name=\"batch\", static_broadcasted_argnums=(3, 4))\n",
    "def p_generate(tokenized_prompt, key, params, top_k, top_p):\n",
    "    return model.generate(\n",
    "        **tokenized_prompt,\n",
    "        do_sample=True,\n",
    "        num_beams=1,\n",
    "        prng_key=key,\n",
    "        params=params,\n",
    "        top_k=top_k,\n",
    "        top_p=top_p\n",
    "    )\n",
    "\n",
    "\n",
    "# decode images\n",
    "@partial(jax.pmap, axis_name=\"batch\")\n",
    "def p_decode(indices, params):\n",
    "    return vqgan.decode_code(indices, params=params)\n",
    "\n",
    "\n",
    "# score images\n",
    "@partial(jax.pmap, axis_name=\"batch\")\n",
    "def p_clip(inputs, params):\n",
    "    logits = clip(params=params, **inputs).logits_per_image\n",
    "    return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HmVN6IBwapBA"
   },
   "source": [
    "Keys are passed to the model on each device to generate unique inferences per device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4CTXmlUkThhX"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# create a random key\n",
    "seed = random.randint(0, 2 ** 32 - 1)\n",
    "key = jax.random.PRNGKey(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BrnVyCo81pij"
   },
   "source": [
    "## 🖍 Text Prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rsmj0Aj5OQox"
   },
   "source": [
    "Our model may require to normalize the prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YjjhUychOVxm"
   },
   "outputs": [],
   "source": [
    "from dalle_mini.text import TextNormalizer\n",
    "\n",
    "text_normalizer = TextNormalizer() if model.config.normalize_text else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BQ7fymSPyvF_"
   },
   "source": [
    "Let's define a text prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x_0vI9ge1oKr"
   },
   "outputs": [],
   "source": [
    "prompt = \"a red T-shirt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VKjEZGjtO49k"
   },
   "outputs": [],
   "source": [
    "processed_prompt = text_normalizer(prompt) if model.config.normalize_text else prompt\n",
    "processed_prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFVOyYboP0L-"
   },
   "source": [
    "We repeat the prompt on each device and tokenize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rii_FJ7POw1y"
   },
   "outputs": [],
   "source": [
    "# repeat the prompt on each device\n",
    "repeated_prompts = [processed_prompt] * jax.device_count()\n",
    "\n",
    "# tokenize\n",
    "tokenized_prompt = tokenizer(\n",
    "    repeated_prompts,\n",
    "    return_tensors=\"jax\",\n",
    "    padding=\"max_length\",\n",
    "    truncation=True,\n",
    "    max_length=128,\n",
    ").data\n",
    "tokenized_prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Y5dqFj7prMQ"
   },
   "source": [
    "Notes:\n",
    "\n",
    "* `0`: BOS, special token representing the beginning of a sequence\n",
    "* `2`: EOS, special token representing the end of a sequence\n",
    "* `1`: special token representing the padding of a sequence when requesting a specific length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2wiDtG3_SH2u"
   },
   "source": [
    "Finally we distribute the tokenized prompt onto the devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AImyrxHtR9TG"
   },
   "outputs": [],
   "source": [
    "from flax.training.common_utils import shard\n",
    "\n",
    "tokenized_prompt = shard(tokenized_prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "phQ9bhjRkgAZ"
   },
   "source": [
    "## 🎨 Generate images\n",
    "\n",
    "We generate images using dalle-mini model and decode them with the VQGAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d0wVkXpKqnHA"
   },
   "outputs": [],
   "source": [
    "# number of predictions\n",
    "n_predictions = 32\n",
    "\n",
    "# We can customize top_k/top_p used for generating samples\n",
    "gen_top_k = None\n",
    "gen_top_p = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SDjEx9JxR3v8"
   },
   "outputs": [],
   "source": [
    "from flax.training.common_utils import shard_prng_key\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from tqdm.notebook import trange\n",
    "\n",
    "# generate images\n",
    "images = []\n",
    "for i in trange(n_predictions // jax.device_count()):\n",
    "    # get a new key\n",
    "    key, subkey = jax.random.split(key)\n",
    "    # generate images\n",
    "    encoded_images = p_generate(\n",
    "        tokenized_prompt, shard_prng_key(subkey), model_params, gen_top_k, gen_top_p\n",
    "    )\n",
    "    # remove BOS\n",
    "    encoded_images = encoded_images.sequences[..., 1:]\n",
    "    # decode images\n",
    "    decoded_images = p_decode(encoded_images, vqgan_params)\n",
    "    decoded_images = decoded_images.clip(0.0, 1.0).reshape((-1, 256, 256, 3))\n",
    "    for img in decoded_images:\n",
    "        images.append(Image.fromarray(np.asarray(img * 255, dtype=np.uint8)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tw02wG9zGmyB"
   },
   "source": [
    "Let's calculate their score with CLIP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FoLXpjCmGpju"
   },
   "outputs": [],
   "source": [
    "# get clip scores\n",
    "clip_inputs = processor(\n",
    "    text=[prompt] * jax.device_count(),\n",
    "    images=images,\n",
    "    return_tensors=\"np\",\n",
    "    padding=\"max_length\",\n",
    "    max_length=77,\n",
    "    truncation=True,\n",
    ").data\n",
    "logits = p_clip(shard(clip_inputs), clip_params)\n",
    "logits = logits.squeeze().flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4AAWRm70LgED"
   },
   "source": [
    "Let's display images ranked by CLIP score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zsgxxubLLkIu"
   },
   "outputs": [],
   "source": [
    "print(f\"Prompt: {prompt}\\n\")\n",
    "for idx in logits.argsort()[::-1]:\n",
    "    display(images[idx])\n",
    "    print(f\"Score: {logits[idx]:.2f}\\n\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Copy of DALL·E mini - Inference pipeline.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}