Spaces:
Runtime error
Runtime error
File size: 18,120 Bytes
14f7e5e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 |
{
"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 directly [the app](https://www.craiyon.com/).\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": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uzjAM2GBYpZX",
"outputId": "9042b53c-1260-4ae6-ff54-be878c99d505"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"tensorflow-metal 0.5.0 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"# Install required libraries\n",
"!pip install -q dalle-mini\n",
"!pip install -q git+https://github.com/patil-suraj/vqgan-jax.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ozHzTkyv8cqU"
},
"source": [
"We load required models:\n",
"* DALL·E mini for text to encoded images\n",
"* VQGAN for decoding images\n",
"* CLIP for scoring predictions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "K6CxW2o42f-w"
},
"outputs": [],
"source": [
"# Model references\n",
"\n",
"# dalle-mega\n",
"DALLE_MODEL = \"dalle-mini/dalle-mini/mega-1-fp16:latest\" # can be wandb artifact or 🤗 Hub or local folder or google bucket\n",
"DALLE_COMMIT_ID = None\n",
"\n",
"# if the notebook crashes too often you can use dalle-mini instead by uncommenting below line\n",
"# DALLE_MODEL = \"dalle-mini/dalle-mini/mini-1:v0\"\n",
"\n",
"# VQGAN model\n",
"VQGAN_REPO = \"dalle-mini/vqgan_imagenet_f16_16384\"\n",
"VQGAN_COMMIT_ID = \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Yv-aR3t4Oe5v",
"outputId": "850b9a43-2506-432f-ae8e-b8b2598e4a98"
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"\n",
"# check how many devices are available\n",
"jax.local_device_count()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 240
},
"id": "92zYmvsQ38vL",
"outputId": "556dc277-a885-443b-8848-373696f5acc7"
},
"outputs": [
{
"ename": "NameError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-4a35db7a446b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Load dalle-mini\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m model, params = DalleBart.from_pretrained(\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mDALLE_MODEL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDALLE_COMMIT_ID\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_do_init\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'DALLE_MODEL' is not defined"
]
}
],
"source": [
"# Load models & tokenizer\n",
"from dalle_mini import DalleBart, DalleBartProcessor\n",
"from vqgan_jax.modeling_flax_vqgan import VQModel\n",
"from transformers import CLIPProcessor, FlaxCLIPModel\n",
"\n",
"# Load dalle-mini\n",
"model, params = DalleBart.from_pretrained(\n",
" DALLE_MODEL, revision=DALLE_COMMIT_ID, dtype=jnp.float16, _do_init=False\n",
")\n",
"\n",
"# Load VQGAN\n",
"vqgan, vqgan_params = VQModel.from_pretrained(\n",
" VQGAN_REPO, revision=VQGAN_COMMIT_ID, _do_init=False\n",
")"
]
},
{
"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",
"params = replicate(params)\n",
"vqgan_params = replicate(vqgan_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, 5, 6))\n",
"def p_generate(\n",
" tokenized_prompt, key, params, top_k, top_p, temperature, condition_scale\n",
"):\n",
" return model.generate(\n",
" **tokenized_prompt,\n",
" prng_key=key,\n",
" params=params,\n",
" top_k=top_k,\n",
" top_p=top_p,\n",
" temperature=temperature,\n",
" condition_scale=condition_scale,\n",
" )\n",
"\n",
"\n",
"# decode image\n",
"@partial(jax.pmap, axis_name=\"batch\")\n",
"def p_decode(indices, params):\n",
" return vqgan.decode_code(indices, params=params)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HmVN6IBwapBA"
},
"source": [
"Keys are passed to the model on each device to generate unique inference 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 requires processing prompts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YjjhUychOVxm"
},
"outputs": [],
"source": [
"from dalle_mini import DalleBartProcessor\n",
"\n",
"processor = DalleBartProcessor.from_pretrained(DALLE_MODEL, revision=DALLE_COMMIT_ID)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BQ7fymSPyvF_"
},
"source": [
"Let's define some text prompts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x_0vI9ge1oKr"
},
"outputs": [],
"source": [
"prompts = [\n",
" \"sunset over a lake in the mountains\",\n",
" \"the Eiffel tower landing on the moon\",\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XlZUG3SCLnGE"
},
"source": [
"Note: we could use the same prompt multiple times for faster inference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VKjEZGjtO49k"
},
"outputs": [],
"source": [
"tokenized_prompts = processor(prompts)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-CEJBnuJOe5z"
},
"source": [
"Finally we replicate the prompts onto each device."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lQePgju5Oe5z"
},
"outputs": [],
"source": [
"tokenized_prompt = replicate(tokenized_prompts)"
]
},
{
"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 per prompt\n",
"n_predictions = 8\n",
"\n",
"# We can customize generation parameters (see https://huggingface.co/blog/how-to-generate)\n",
"gen_top_k = None\n",
"gen_top_p = None\n",
"temperature = None\n",
"cond_scale = 10.0"
]
},
{
"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",
"print(f\"Prompts: {prompts}\\n\")\n",
"# generate images\n",
"images = []\n",
"for i in trange(max(n_predictions // jax.device_count(), 1)):\n",
" # get a new key\n",
" key, subkey = jax.random.split(key)\n",
" # generate images\n",
" encoded_images = p_generate(\n",
" tokenized_prompt,\n",
" shard_prng_key(subkey),\n",
" params,\n",
" gen_top_k,\n",
" gen_top_p,\n",
" temperature,\n",
" cond_scale,\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 decoded_img in decoded_images:\n",
" img = Image.fromarray(np.asarray(decoded_img * 255, dtype=np.uint8))\n",
" images.append(img)\n",
" display(img)\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tw02wG9zGmyB"
},
"source": [
"## 🏅 Optional: Rank images by CLIP score\n",
"\n",
"We can rank images according to CLIP.\n",
"\n",
"**Note: your session may crash if you don't have a subscription to Colab Pro.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RGjlIW_f6GA0"
},
"outputs": [],
"source": [
"# CLIP model\n",
"CLIP_REPO = \"openai/clip-vit-base-patch32\"\n",
"CLIP_COMMIT_ID = None\n",
"\n",
"# Load CLIP\n",
"clip, clip_params = FlaxCLIPModel.from_pretrained(\n",
" CLIP_REPO, revision=CLIP_COMMIT_ID, dtype=jnp.float16, _do_init=False\n",
")\n",
"clip_processor = CLIPProcessor.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)\n",
"clip_params = replicate(clip_params)\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": "code",
"execution_count": null,
"metadata": {
"id": "FoLXpjCmGpju"
},
"outputs": [],
"source": [
"from flax.training.common_utils import shard\n",
"\n",
"# get clip scores\n",
"clip_inputs = clip_processor(\n",
" text=prompts * 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",
"\n",
"# organize scores per prompt\n",
"p = len(prompts)\n",
"logits = np.asarray([logits[:, i::p, i] for i in range(p)]).squeeze()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4AAWRm70LgED"
},
"source": [
"Let's now display images ranked by CLIP score."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zsgxxubLLkIu"
},
"outputs": [],
"source": [
"for i, prompt in enumerate(prompts):\n",
" print(f\"Prompt: {prompt}\\n\")\n",
" for idx in logits[i].argsort()[::-1]:\n",
" display(images[idx * p + i])\n",
" print(f\"Score: {jnp.asarray(logits[i][idx], dtype=jnp.float32):.2f}\\n\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oZT9i3jCjir0"
},
"source": [
"## 🪄 Optional: Save your Generated Images as W&B Tables\n",
"\n",
"W&B Tables is an interactive 2D grid with support to rich media logging. Use this to save the generated images on W&B dashboard and share with the world."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-pSiv6Vwjkn0"
},
"outputs": [],
"source": [
"import wandb\n",
"\n",
"# Initialize a W&B run.\n",
"project = 'dalle-mini-tables-colab'\n",
"run = wandb.init(project=project)\n",
"\n",
"# Initialize an empty W&B Tables.\n",
"columns = [\"captions\"] + [f\"image_{i+1}\" for i in range(n_predictions)]\n",
"gen_table = wandb.Table(columns=columns)\n",
"\n",
"# Add data to the table.\n",
"for i, prompt in enumerate(prompts):\n",
" # If CLIP scores exist, sort the Images\n",
" if logits is not None:\n",
" idxs = logits[i].argsort()[::-1]\n",
" tmp_imgs = images[i::len(prompts)]\n",
" tmp_imgs = [tmp_imgs[idx] for idx in idxs]\n",
" else:\n",
" tmp_imgs = images[i::len(prompts)]\n",
"\n",
" # Add the data to the table.\n",
" gen_table.add_data(prompt, *[wandb.Image(img) for img in tmp_imgs])\n",
"\n",
"# Log the Table to W&B dashboard.\n",
"wandb.log({\"Generated Images\": gen_table})\n",
"\n",
"# Close the W&B run.\n",
"run.finish()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ck2ZnHwVjnRd"
},
"source": [
"Click on the link above to check out your generated images."
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"machine_shape": "hm",
"name": "DALL·E mini - Inference pipeline.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3.9.13 ('base')",
"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": "3.9.13"
},
"vscode": {
"interpreter": {
"hash": "3e91440bae70fe36b08f2decfecf198c5281689ed89adf5e1c2c93a1bdd6e28e"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|