Spaces:
Running
on
T4
Running
on
T4
File size: 65,529 Bytes
58c492c 1b22455 55c286f 1b22455 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 55c286f 58c492c 1b22455 55c286f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 |
import os
import gc
import re
import cv2
import numpy as np
import torch
import traceback
import math
import time
import zipfile
from collections import defaultdict
from facexlib.utils.misc import download_from_url
from basicsr.utils.realesrganer import RealESRGANer
from utils.dataops import auto_split_upscale
from typing import List, Optional
# FastAPI imports
from fastapi import FastAPI, UploadFile, File, Form, Depends, HTTPException, status
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from fastapi.responses import FileResponse, JSONResponse
# Mongo imports (optional)
_mongo_client = None
_mongo_collection = None
try:
from pymongo import MongoClient
_mongo_uri = os.getenv("MONGODB_URI")
_mongo_db = os.getenv("MONGODB_DB", "face_upscale")
_mongo_col = os.getenv("MONGODB_COLLECTION", "submits")
if _mongo_uri:
_mongo_client = MongoClient(_mongo_uri, connect=False)
_mongo_collection = _mongo_client[_mongo_db][_mongo_col]
except Exception:
_mongo_client = None
_mongo_collection = None
input_images_limit = 5
# Define URLs and their corresponding local storage paths
face_models = {
"GFPGANv1.4.pth": ["https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",
"https://github.com/TencentARC/GFPGAN/",
"""GFPGAN: Towards Real-World Blind Face Restoration and Upscalling of the image with a Generative Facial Prior.
GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration."""],
"RestoreFormer++.ckpt": ["https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer++.ckpt",
"https://github.com/wzhouxiff/RestoreFormerPlusPlus",
"""RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs.
RestoreFormer++ is an extension of RestoreFormer. It proposes to restore a degraded face image with both fidelity and realness by using the powerful fully-spacial attention mechanisms to model the abundant contextual information in the face and its interplay with reconstruction-oriented high-quality priors."""],
"CodeFormer.pth": ["https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth",
"https://github.com/sczhou/CodeFormer",
"""CodeFormer: Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022).
CodeFormer is a Transformer-based model designed to tackle the challenging problem of blind face restoration, where inputs are often severely degraded.
By framing face restoration as a code prediction task, this approach ensures both improved mapping from degraded inputs to outputs and the generation of visually rich, high-quality faces."""],
"GPEN-BFR-512.pth": ["https://huggingface.co/akhaliq/GPEN-BFR-512/resolve/main/GPEN-BFR-512.pth",
"https://github.com/yangxy/GPEN",
"""GPEN: GAN Prior Embedded Network for Blind Face Restoration in the Wild.
GPEN addresses blind face restoration (BFR) by embedding a GAN into a U-shaped DNN, combining GAN’s ability to generate high-quality images with DNN’s feature extraction.
This design reconstructs global structure, fine details, and backgrounds from degraded inputs.
Simple yet effective, GPEN outperforms state-of-the-art methods, delivering realistic results even for severely degraded images."""],
"GPEN-BFR-1024.pt": ["https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/resolve/master/pytorch_model.pt",
"https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/files",
"""The same as GPEN but for 1024 resolution."""],
"GPEN-BFR-2048.pt": ["https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/resolve/master/pytorch_model-2048.pt",
"https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/files",
"""The same as GPEN but for 2048 resolution."""],
"GFPGANv1.3.pth": ["https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth",
"https://github.com/TencentARC/GFPGAN/",
"The same as GFPGAN but legacy model"],
"GFPGANv1.2.pth": ["https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth",
"https://github.com/TencentARC/GFPGAN/",
"The same as GFPGAN but legacy model"],
"RestoreFormer.ckpt": ["https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer.ckpt",
"https://github.com/wzhouxiff/RestoreFormerPlusPlus",
"The same as RestoreFormer++ but legacy model"],
}
upscale_models = {
"realesr-general-x4v3.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.3.0",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: add realesr-general-x4v3 and realesr-general-wdn-x4v3. They are very tiny models for general scenes, and they may more robust. But as they are tiny models, their performance may be limited."""],
"realesr-animevideov3.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.5.0",
"""Anime, Cartoon, Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: update the RealESRGAN AnimeVideo-v3 model, which can achieve better results with a faster inference speed."""],
"4xLSDIRCompact.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact/4xLSDIRCompact.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact",
"""Realistic
Phhofm: Upscale small good quality photos to 4x their size. This is my first ever released self-trained sisr upscaling model."""],
"4xLSDIRCompactC.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompactC/4xLSDIRCompactC.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompactC",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler that handler jpg compression. Trying to extend my previous model to be able to handle compression (JPG 100-30) by manually altering the training dataset, since 4xLSDIRCompact cant handle compression. Use this instead of 4xLSDIRCompact if your photo has compression (like an image from the web)."""],
"4xLSDIRCompactR.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompactC/4xLSDIRCompactR.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompactC",
"""Compression Removal, Realistic, Restoration
Phhofm: 4x photo uspcaler that handles jpg compression, noise and slight. Extending my last 4xLSDIRCompact model to Real-ESRGAN, meaning trained on synthetic data instead to handle more kinds of degradations, it should be able to handle compression, noise, and slight blur."""],
"4xLSDIRCompactN.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactC3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Realistic
Phhofm: Upscale good quality input photos to x4 their size. The original 4xLSDIRCompact a bit more trained, cannot handle degradation."""],
"4xLSDIRCompactC3.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactC3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: Upscale compressed photos to x4 their size. Able to handle JPG compression (30-100)."""],
"4xLSDIRCompactR3.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactR3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Realistic, Restoration
Phhofm: Upscale (degraded) photos to x4 their size. Trained on synthetic data, meant to handle more degradations."""],
"4xLSDIRCompactCR3.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactCR3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Phhofm: I am releasing the Series 3 from my 4xLSDIRCompact models. In general my suggestion is, if you have good quality input images use 4xLSDIRCompactN3, otherwise try 4xLSDIRCompactC3 which will be able to handle jpg compression and a bit of blur, or then 4xLSDIRCompactCR3, which is an interpolation between C3 and R3 to be able to handle a bit of noise additionally."""],
"2xParimgCompact.pth": ["https://github.com/Phhofm/models/releases/download/2xParimgCompact/2xParimgCompact.pth",
"https://github.com/Phhofm/models/releases/tag/2xParimgCompact",
"""Realistic
Phhofm: A 2x photo upscaling compact model based on Microsoft's ImagePairs. This was one of the earliest models I started training and finished it now for release. As can be seen in the examples, this model will affect colors."""],
"1xExposureCorrection_compact.pth": ["https://github.com/Phhofm/models/releases/download/1xExposureCorrection_compact/1xExposureCorrection_compact.pth",
"https://github.com/Phhofm/models/releases/tag/1xExposureCorrection_compact",
"""Restoration
Phhofm: This model is meant as an experiment to see if compact can be used to train on photos to exposure correct those using the pixel, perceptual, color, color and ldl losses. There is no brightness loss. Still it seems to kinda work."""],
"1xUnderExposureCorrection_compact.pth": ["https://github.com/Phhofm/models/releases/download/1xExposureCorrection_compact/1xUnderExposureCorrection_compact.pth",
"https://github.com/Phhofm/models/releases/tag/1xExposureCorrection_compact",
"""Restoration
Phhofm: This model is meant as an experiment to see if compact can be used to train on underexposed images to exposure correct those using the pixel, perceptual, color, color and ldl losses. There is no brightness loss. Still it seems to kinda work."""],
"1xOverExposureCorrection_compact.pth": ["https://github.com/Phhofm/models/releases/download/1xExposureCorrection_compact/1xOverExposureCorrection_compact.pth",
"https://github.com/Phhofm/models/releases/tag/1xExposureCorrection_compact",
"""Restoration
Phhofm: This model is meant as an experiment to see if compact can be used to train on overexposed images to exposure correct those using the pixel, perceptual, color, color and ldl losses. There is no brightness loss. Still it seems to kinda work."""],
"2x-sudo-UltraCompact.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/2x-sudo-UltraCompact.pth",
"https://openmodeldb.info/models/2x-sudo-UltraCompact",
"""Anime, Cartoon, Restoration
sudo: Realtime animation restauration and doing stuff like deblur and compression artefact removal."""],
"2x_AnimeJaNai_HD_V3_SuperUltraCompact.pth": ["https://github.com/the-database/mpv-upscale-2x_animejanai/releases/download/3.0.0/2x_AnimeJaNai_HD_V3_ModelsOnly.zip",
"https://openmodeldb.info/models/2x-AnimeJaNai-HD-V3-SuperUltraCompact",
"""Anime, Compression Removal, Restoration
the-database: Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K."""],
"2x_AnimeJaNai_HD_V3_UltraCompact.pth": ["https://github.com/the-database/mpv-upscale-2x_animejanai/releases/download/3.0.0/2x_AnimeJaNai_HD_V3_ModelsOnly.zip",
"https://openmodeldb.info/models/2x-AnimeJaNai-HD-V3-UltraCompact",
"""Anime, Compression Removal, Restoration
the-database: Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K."""],
"2x_AnimeJaNai_HD_V3_Compact.pth": ["https://github.com/the-database/mpv-upscale-2x_animejanai/releases/download/3.0.0/2x_AnimeJaNai_HD_V3_ModelsOnly.zip",
"https://openmodeldb.info/models/2x-AnimeJaNai-HD-V3-Compact",
"""Anime, Compression Removal, Restoration
the-database: Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K."""],
"RealESRGAN_x4plus_anime_6B.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.2.4",
"""Anime, Cartoon, Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: We add RealESRGAN_x4plus_anime_6B.pth, which is optimized for anime images with much smaller model size."""],
"RealESRGAN_x2plus.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.1",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: Add RealESRGAN_x2plus.pth model"""],
"RealESRNet_x4plus.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.1.1",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: This release is mainly for storing pre-trained models and executable files."""],
"RealESRGAN_x4plus.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.1.0",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: This release is mainly for storing pre-trained models and executable files."""],
"4x-AnimeSharp.pth": ["https://huggingface.co/utnah/esrgan/resolve/main/4x-AnimeSharp.pth?download=true",
"https://openmodeldb.info/models/4x-AnimeSharp",
"""Anime, Cartoon, Text
Kim2091: Interpolation between 4x-UltraSharp and 4x-TextSharp-v0.5. Works amazingly on anime."""],
"4x_IllustrationJaNai_V1_ESRGAN_135k.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1qpioSqBkB_IkSBhEAewSSNFt6qgkBimP",
"https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2",
"""Anime, Cartoon, Compression Removal, Dehalftone, General Upscaler, JPEG, Manga, Restoration
the-database: Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more."""],
"2x-sudo-RealESRGAN.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/2x-sudo-RealESRGAN.pth",
"https://openmodeldb.info/models/2x-sudo-RealESRGAN",
"""Anime, Cartoon
sudo: Tried to make the best 2x model there is for drawings."""],
"2x-sudo-RealESRGAN-Dropout.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/2x-sudo-RealESRGAN-Dropout.pth",
"https://openmodeldb.info/models/2x-sudo-RealESRGAN-Dropout",
"""Anime, Cartoon
sudo: Tried to make the best 2x model there is for drawings."""],
"4xNomos2_otf_esrgan.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos2_otf_esrgan/4xNomos2_otf_esrgan.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_otf_esrgan",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: Restoration, 4x ESRGAN model for photography, trained using the Real-ESRGAN otf degradation pipeline."""],
"4xNomosWebPhoto_esrgan.pth": ["https://github.com/Phhofm/models/releases/download/4xNomosWebPhoto_esrgan/4xNomosWebPhoto_esrgan.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomosWebPhoto_esrgan",
"""Realistic, Restoration
Phhofm: Restoration, 4x ESRGAN model for photography, trained with realistic noise, lens blur, jpg and webp re-compression."""],
"4x_foolhardy_Remacri.pth": ["https://civitai.com/api/download/models/164821?type=Model&format=PickleTensor",
"https://openmodeldb.info/models/4x-Remacri",
"""Original
FoolhardyVEVO: A creation of BSRGAN with more details and less smoothing."""],
"4x_foolhardy_Remacri_ExtraSmoother.pth": ["https://civitai.com/api/download/models/164822?type=Model&format=PickleTensor",
"https://openmodeldb.info/models/4x-Remacri",
"""ExtraSmoother
FoolhardyVEVO: A creation of BSRGAN with more details and less smoothing."""],
"4xNomos8kDAT.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos8kDAT/4xNomos8kDAT.pth",
"https://openmodeldb.info/models/4x-Nomos8kDAT",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: A 4x photo upscaler with otf jpg compression, blur and resize, trained on musl's Nomos8k_sfw dataset for realisic sr."""],
"4x-DWTP-DS-dat2-v3.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/4x-DWTP-DS-dat2-v3.pth",
"https://openmodeldb.info/models/4x-DWTP-DS-dat2-v3",
"""Dehalftone, Restoration
umzi.x.dead: DAT descreenton model, designed to reduce discrepancies on tiles due to too much loss of the first version."""],
"4xBHI_dat2_real.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_real/4xBHI_dat2_real.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_real",
"""Compression Removal, JPEG, Realistic
Phhofm: 4x dat2 upscaling model for web and realistic images."""],
"4xBHI_dat2_otf.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_otf/4xBHI_dat2_otf.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_otf",
"""Compression Removal, JPEG
Phhofm: 4x dat2 upscaling model, trained with the real-esrgan otf pipeline on my bhi dataset."""],
"4xBHI_dat2_multiblur.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_multiblurjpg/4xBHI_dat2_multiblur.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_multiblurjpg",
"""Phhofm: the 4xBHI_dat2_multiblur checkpoint (trained to 250000 iters), which cannot handle compression but might give just slightly better output on non-degraded input."""],
"4xBHI_dat2_multiblurjpg.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_multiblurjpg/4xBHI_dat2_multiblurjpg.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_multiblurjpg",
"""Compression Removal, JPEG
Phhofm: 4x dat2 upscaling model, trained with down_up,linear, cubic_mitchell, lanczos, gauss and box scaling algos, some average, gaussian and anisotropic blurs and jpg compression."""],
"4x_IllustrationJaNai_V1_DAT2_190k.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1qpioSqBkB_IkSBhEAewSSNFt6qgkBimP",
"https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2",
"""Anime, Cartoon, Compression Removal, Dehalftone, General Upscaler, JPEG, Manga, Restoration
the-database: Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more."""],
"4x-PBRify_UpscalerDAT2_V1.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/4x-PBRify_UpscalerDAT2_V1/4x-PBRify_UpscalerDAT2_V1.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/4x-PBRify_UpscalerDAT2_V1",
"""Compression Removal, DDS, Game Textures, Restoration
Kim2091: Yet another model in the PBRify_Remix series."""],
"4xBHI_dat2_otf_nn.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_otf_nn/4xBHI_dat2_otf_nn.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_otf_nn",
"""Compression Removal, JPEG
Phhofm: 4x dat2 upscaling model, trained with the real-esrgan otf pipeline but without noise, on my bhi dataset."""],
"4xNomos8kSCHAT-L.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos8kSCHAT/4xNomos8kSCHAT-L.pth",
"https://openmodeldb.info/models/4x-Nomos8kSCHAT-L",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr."""],
"4xNomos8kSCHAT-S.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos8kSCHAT/4xNomos8kSCHAT-S.pth",
"https://openmodeldb.info/models/4x-Nomos8kSCHAT-S",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr."""],
"4xNomos8kHAT-L_otf.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos8kHAT-L_otf/4xNomos8kHAT-L_otf.pth",
"https://openmodeldb.info/models/4x-Nomos8kHAT-L-otf",
"""Faces, General Upscaler, Realistic, Restoration
Phhofm: 4x photo upscaler trained with otf, handles some jpg compression, some blur and some noise."""],
"4xBHI_small_hat-l.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_small_hat-l/4xBHI_small_hat-l.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_small_hat-l",
"""Phhofm: 4x hat-l upscaling model for good quality input. This model does not handle any degradations."""],
"4xHFA2k_ludvae_realplksr_dysample.pth": ["https://github.com/Phhofm/models/releases/download/4xHFA2k_ludvae_realplksr_dysample/4xHFA2k_ludvae_realplksr_dysample.pth",
"https://openmodeldb.info/models/4x-HFA2k-ludvae-realplksr-dysample",
"""Anime, Compression Removal, Restoration
Phhofm: A Dysample RealPLKSR 4x upscaling model for anime single-image resolution."""],
"4xArtFaces_realplksr_dysample.pth": ["https://github.com/Phhofm/models/releases/download/4xArtFaces_realplksr_dysample/4xArtFaces_realplksr_dysample.pth",
"https://openmodeldb.info/models/4x-ArtFaces-realplksr-dysample",
"""ArtFaces
Phhofm: A Dysample RealPLKSR 4x upscaling model for art / painted faces."""],
"4x-PBRify_RPLKSRd_V3.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/4x-PBRify_RPLKSRd_V3/4x-PBRify_RPLKSRd_V3.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/4x-PBRify_RPLKSRd_V3",
"""Compression Removal, DDS, Debanding, Dedither, Dehalo, Game Textures, Restoration
Kim2091: This update brings a new upscaling model, 4x-PBRify_RPLKSRd_V3."""],
"4xNomos2_realplksr_dysample.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos2_realplksr_dysample/4xNomos2_realplksr_dysample.pth",
"https://openmodeldb.info/models/4x-Nomos2-realplksr-dysample",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: A Dysample RealPLKSR 4x upscaling model that was trained with / handles jpg compression down to 70 on the Nomosv2 dataset."""],
"2x-AnimeSharpV2_RPLKSR_Sharp.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_RPLKSR_Sharp.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Sharp: For heavily degraded sources."""],
"2x-AnimeSharpV2_RPLKSR_Soft.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_RPLKSR_Soft.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Soft: For cleaner sources."""],
"4xPurePhoto-RealPLSKR.pth": ["https://github.com/starinspace/StarinspaceUpscale/releases/download/Models/4xPurePhoto-RealPLSKR.pth",
"https://openmodeldb.info/models/4x-PurePhoto-RealPLSKR",
"""AI Generated, Compression Removal, JPEG, Realistic, Restoration
asterixcool: Skilled in working with cats, hair, parties, and creating clear images."""],
"2x_Text2HD_v.1-RealPLKSR.pth": ["https://github.com/starinspace/StarinspaceUpscale/releases/download/Models/2x_Text2HD_v.1-RealPLKSR.pth",
"https://openmodeldb.info/models/2x-Text2HD-v-1",
"""Compression Removal, Denoise, General Upscaler, JPEG, Restoration, Text
asterixcool: The upscale model is specifically designed to enhance lower-quality text images."""],
"2xVHS2HD-RealPLKSR.pth": ["https://github.com/starinspace/StarinspaceUpscale/releases/download/Models/2xVHS2HD-RealPLKSR.pth",
"https://openmodeldb.info/models/2x-VHS2HD",
"""Compression Removal, Dehalo, Realistic, Restoration, Video Frame
asterixcool: An advanced VHS recording model designed to enhance video quality by reducing artifacts."""],
"4xNomosWebPhoto_RealPLKSR.pth": ["https://github.com/Phhofm/models/releases/download/4xNomosWebPhoto_RealPLKSR/4xNomosWebPhoto_RealPLKSR.pth",
"https://openmodeldb.info/models/4x-NomosWebPhoto-RealPLKSR",
"""Realistic, Restoration
Phhofm: 4x RealPLKSR model for photography, trained with realistic noise, lens blur, jpg and webp re-compression."""],
"4xNomos2_hq_drct-l.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos2_hq_drct-l/4xNomos2_hq_drct-l.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_drct-l",
"""General Upscaler, Realistic
Phhofm: An drct-l 4x upscaling model, similiar to the 4xNomos2_hq_atd, 4xNomos2_hq_dat2 and 4xNomos2_hq_mosr models."""],
"4xNomos2_hq_atd.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos2_hq_atd/4xNomos2_hq_atd.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_atd",
"""General Upscaler, Realistic
Phhofm: An atd 4x upscaling model, similiar to the 4xNomos2_hq_dat2 or 4xNomos2_hq_mosr models."""],
"4xNomos2_hq_mosr.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos2_hq_mosr/4xNomos2_hq_mosr.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr",
"""General Upscaler, Realistic
Phhofm: A 4x MoSR upscaling model, meant for non-degraded input."""],
"2x-AnimeSharpV2_MoSR_Sharp.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_MoSR_Sharp.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Sharp: For heavily degraded sources."""],
"2x-AnimeSharpV2_MoSR_Soft.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_MoSR_Soft.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Soft: For cleaner sources."""],
"4xNomos8kSCSRFormer.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos8kSCSRFormer/4xNomos8kSCSRFormer.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos8kSCSRFormer",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr."""],
"4xFrankendataFullDegradation_SRFormer460K_g.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1PZrj-8ofxhORv_OgTVSoRt3dYi-BtiDj",
"https://openmodeldb.info/models/4x-Frankendata-FullDegradation-SRFormer",
"""Compression Removal, Denoise, Realistic, Restoration
Crustaceous D: 4x realistic upscaler that may also work for general purpose usage."""],
"4xFrankendataPretrainer_SRFormer400K_g.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1SaKvpYYIm2Vj2m9GifUMlNCbmkE6JZmr",
"https://openmodeldb.info/models/4x-FrankendataPretainer-SRFormer",
"""Realistic, Restoration
Crustaceous D: 4x realistic upscaler that may also work for general purpose usage."""],
"1xFrankenfixer_SRFormerLight_g.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1UJ0iyFn4IGNhPIgNgrQrBxYsdDloFc9I",
"https://openmodeldb.info/models/1x-Frankenfixer-SRFormerLight",
"""Realistic, Restoration
Crustaceous D: A 1x model designed to reduce artifacts and restore detail to images upscaled by 4xFrankendata_FullDegradation_SRFormer."""],
}
def get_model_type(model_name):
model_type = "other"
if any(value in model_name.lower() for value in ("4x-animesharp.pth", "sudo-realesrgan", "remacri")):
model_type = "ESRGAN"
elif "srformer" in model_name.lower():
model_type = "SRFormer"
elif ("realplksr" in model_name.lower() and "dysample" in model_name.lower()) or "rplksrd" in model_name.lower():
model_type = "RealPLKSR_dysample"
elif any(value in model_name.lower() for value in ("realplksr", "rplksr", "realplskr")):
model_type = "RealPLKSR"
elif any(value in model_name.lower() for value in ("realesrgan", "realesrnet")):
model_type = "RRDB"
elif any(value in model_name.lower() for value in ("realesr", "compact")):
model_type = "SRVGG"
elif "esrgan" in model_name.lower():
model_type = "ESRGAN"
elif "dat" in model_name.lower():
model_type = "DAT"
elif "hat" in model_name.lower():
model_type = "HAT"
elif "drct" in model_name.lower():
model_type = "DRCT"
elif "atd" in model_name.lower():
model_type = "ATD"
elif "mosr" in model_name.lower():
model_type = "MoSR"
return f"{model_type}, {model_name}"
typed_upscale_models = {get_model_type(key): value[0] for key, value in upscale_models.items()}
class Upscale:
def __init__(self):
self.scale = 4
self.modelInUse = ""
self.realesrganer = None
self.face_enhancer = None
def initBGUpscaleModel(self, upscale_model):
upscale_type, upscale_model = upscale_model.split(", ", 1)
download_from_url(upscale_models[upscale_model][0], upscale_model, os.path.join("weights", "upscale"))
self.modelInUse = f"_{os.path.splitext(upscale_model)[0]}"
netscale = 1 if any(sub in upscale_model.lower() for sub in ("x1", "1x")) else (2 if any(sub in upscale_model.lower() for sub in ("x2", "2x")) else 4)
model = None
half = True if torch.cuda.is_available() else False
if upscale_type:
from basicsr.archs.rrdbnet_arch import RRDBNet
loadnet = torch.load(os.path.join("weights", "upscale", upscale_model), map_location=torch.device('cpu'), weights_only=True)
if 'params_ema' in loadnet or 'params' in loadnet:
loadnet = loadnet['params_ema'] if 'params_ema' in loadnet else loadnet['params']
if upscale_type == "SRVGG":
from basicsr.archs.srvgg_arch import SRVGGNetCompact
body_max_num = self.find_max_numbers(loadnet, "body")
num_feat = loadnet["body.0.weight"].shape[0]
num_in_ch = loadnet["body.0.weight"].shape[1]
num_conv = body_max_num // 2 - 1
model = SRVGGNetCompact(num_in_ch=num_in_ch, num_out_ch=3, num_feat=num_feat, num_conv=num_conv, upscale=netscale, act_type='prelu')
elif upscale_type == "RRDB" or upscale_type == "ESRGAN":
if upscale_type == "RRDB":
num_block = self.find_max_numbers(loadnet, "body") + 1
num_feat = loadnet["conv_first.weight"].shape[0]
else:
num_block = self.find_max_numbers(loadnet, "model.1.sub")
num_feat = loadnet["model.0.weight"].shape[0]
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=num_feat, num_block=num_block, num_grow_ch=32, scale=netscale, is_real_esrgan=upscale_type == "RRDB")
elif upscale_type == "DAT":
from basicsr.archs.dat_arch import DAT
half = False
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depth = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depth[i] = self.find_max_numbers(loadnet, f"layers.{i}.blocks") + 1
num_heads[i] = loadnet[f"layers.{i}.blocks.1.attn.temperature"].shape[0] if depth[i] >= 2 else \
loadnet[f"layers.{i}.blocks.0.attn.attns.0.pos.pos3.2.weight"].shape[0] * 2
upsampler = "pixelshuffle" if "conv_last.weight" in loadnet else "pixelshuffledirect"
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else "3conv"
qkv_bias = "layers.0.blocks.0.attn.qkv.bias" in loadnet
expansion_factor = float(loadnet["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim)
img_size = 64
if "layers.0.blocks.2.attn.attn_mask_0" in loadnet:
attn_mask_0_x, attn_mask_0_y, _attn_mask_0_z = loadnet["layers.0.blocks.2.attn.attn_mask_0"].shape
img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y))
split_size = [2, 4]
if "layers.0.blocks.0.attn.attns.0.rpe_biases" in loadnet:
split_sizes = loadnet["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1
split_size = [int(x) for x in split_sizes]
model = DAT(img_size=img_size, in_chans=in_chans, embed_dim=embed_dim, split_size=split_size, depth=depth, num_heads=num_heads, expansion_factor=expansion_factor,
qkv_bias=qkv_bias, resi_connection=resi_connection, upsampler=upsampler, upscale=netscale)
elif upscale_type == "HAT":
half = False
from basicsr.archs.hat_arch import HAT
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
window_size = int(math.sqrt(loadnet["relative_position_index_SA"].shape[0]))
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depths[i] = self.find_max_numbers(loadnet, f"layers.{i}.residual_group.blocks") + 1
num_heads[i] = loadnet[f"layers.{i}.residual_group.overlap_attn.relative_position_bias_table"].shape[1]
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else "identity"
compress_ratio = self.find_divisor_for_quotient(embed_dim, loadnet["layers.0.residual_group.blocks.0.conv_block.cab.0.weight"].shape[0])
squeeze_factor = self.find_divisor_for_quotient(embed_dim, loadnet["layers.0.residual_group.blocks.0.conv_block.cab.3.attention.1.weight"].shape[0])
qkv_bias = "layers.0.residual_group.blocks.0.attn.qkv.bias" in loadnet
patch_norm = "patch_embed.norm.weight" in loadnet
ape = "absolute_pos_embed" in loadnet
mlp_hidden_dim = int(loadnet["layers.0.residual_group.blocks.0.mlp.fc1.weight"].shape[0])
mlp_ratio = mlp_hidden_dim / embed_dim
upsampler = "pixelshuffle"
model = HAT(img_size=64, patch_size=1, in_chans=in_chans, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, compress_ratio=compress_ratio,
squeeze_factor=squeeze_factor, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ape=ape, patch_norm=patch_norm,
upsampler=upsampler, resi_connection=resi_connection, upscale=netscale)
elif "RealPLKSR" in upscale_type:
from basicsr.archs.realplksr_arch import realplksr
half = False if "RealPLSKR" in upscale_model else half
use_ea = "feats.1.attn.f.0.weight" in loadnet
dim = loadnet["feats.0.weight"].shape[0]
num_feats = self.find_max_numbers(loadnet, "feats") + 1
n_blocks = num_feats - 3
kernel_size = loadnet["feats.1.lk.conv.weight"].shape[2]
split_ratio = loadnet["feats.1.lk.conv.weight"].shape[0] / dim
use_dysample = "to_img.init_pos" in loadnet
model = realplksr(upscaling_factor=netscale, dim=dim, n_blocks=n_blocks, kernel_size=kernel_size, split_ratio=split_ratio, use_ea=use_ea, dysample=use_dysample)
elif upscale_type == "DRCT":
half = False
from basicsr.archs.DRCT_arch import DRCT
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = (6,) * num_layers
num_heads = []
for i in range(num_layers):
num_heads.append(loadnet[f"layers.{i}.swin1.attn.relative_position_bias_table"].shape[1])
mlp_ratio = loadnet["layers.0.swin1.mlp.fc1.weight"].shape[0] / embed_dim
window_square = loadnet["layers.0.swin1.attn.relative_position_bias_table"].shape[0]
window_size = (math.isqrt(window_square) + 1) // 2
upsampler = "pixelshuffle" if "conv_last.weight" in loadnet else ""
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else ""
qkv_bias = "layers.0.swin1.attn.qkv.bias" in loadnet
gc_adjust1 = loadnet["layers.0.adjust1.weight"].shape[0]
patch_norm = "patch_embed.norm.weight" in loadnet
ape = "absolute_pos_embed" in loadnet
model = DRCT(in_chans=in_chans, img_size=64, window_size=window_size, compress_ratio=3, squeeze_factor=30,
conv_scale=0.01, overlap_ratio=0.5, img_range=1., depths=depths, embed_dim=embed_dim, num_heads=num_heads,
mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ape=ape, patch_norm=patch_norm, use_checkpoint=False,
upscale=netscale, upsampler=upsampler, resi_connection=resi_connection, gc=gc_adjust1)
elif upscale_type == "ATD":
half = False
from basicsr.archs.atd_arch import ATD
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
window_size = math.isqrt(loadnet["relative_position_index_SA"].shape[0])
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depths[i] = self.find_max_numbers(loadnet, f"layers.{i}.residual_group.layers") + 1
num_heads[i] = loadnet[f"layers.{i}.residual_group.layers.0.attn_win.relative_position_bias_table"].shape[1]
num_tokens = loadnet["layers.0.residual_group.layers.0.attn_atd.scale"].shape[0]
reducted_dim = loadnet["layers.0.residual_group.layers.0.attn_atd.wq.weight"].shape[0]
convffn_kernel_size = loadnet["layers.0.residual_group.layers.0.convffn.dwconv.depthwise_conv.0.weight"].shape[2]
mlp_ratio = (loadnet["layers.0.residual_group.layers.0.convffn.fc1.weight"].shape[0] / embed_dim)
qkv_bias = "layers.0.residual_group.layers.0.wqkv.bias" in loadnet
ape = "absolute_pos_embed" in loadnet
patch_norm = "patch_embed.norm.weight" in loadnet
resi_connection = "1conv" if "layers.0.conv.weight" in loadnet else "3conv"
if "conv_up1.weight" in loadnet:
upsampler = "nearest+conv"
elif "conv_before_upsample.0.weight" in loadnet:
upsampler = "pixelshuffle"
elif "conv_last.weight" in loadnet:
upsampler = ""
else:
upsampler = "pixelshuffledirect"
is_light = upsampler == "pixelshuffledirect" and embed_dim == 48
category_size = 128 if is_light else 256
model = ATD(in_chans=in_chans, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, category_size=category_size,
num_tokens=num_tokens, reducted_dim=reducted_dim, convffn_kernel_size=convffn_kernel_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ape=ape,
patch_norm=patch_norm, use_checkpoint=False, upscale=netscale, upsampler=upsampler, resi_connection='1conv')
elif upscale_type == "MoSR":
from basicsr.archs.mosr_arch import mosr
n_block = self.find_max_numbers(loadnet, "gblocks") - 5
in_ch = loadnet["gblocks.0.weight"].shape[1]
out_ch = loadnet["upsampler.end_conv.weight"].shape[0] if "upsampler.init_pos" in loadnet else in_ch
dim = loadnet["gblocks.0.weight"].shape[0]
expansion_ratio = (loadnet["gblocks.1.fc1.weight"].shape[0] / loadnet["gblocks.1.fc1.weight"].shape[1]) / 2
conv_ratio = loadnet["gblocks.1.conv.weight"].shape[0] / dim
kernel_size = loadnet["gblocks.1.conv.weight"].shape[2]
upsampler = "dys" if "upsampler.init_pos" in loadnet else ("gps" if "upsampler.in_to_k.weight" in loadnet else "ps")
model = mosr(in_ch=in_ch, out_ch=out_ch, upscale=netscale, n_block=n_block, dim=dim,
upsampler=upsampler, kernel_size=kernel_size, expansion_ratio=expansion_ratio, conv_ratio=conv_ratio)
elif upscale_type == "SRFormer":
half = False
from basicsr.archs.srformer_arch import SRFormer
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
ape = "absolute_pos_embed" in loadnet
patch_norm = "patch_embed.norm.weight" in loadnet
qkv_bias = "layers.0.residual_group.blocks.0.attn.q.bias" in loadnet
mlp_ratio = float(loadnet["layers.0.residual_group.blocks.0.mlp.fc1.weight"].shape[0] / embed_dim)
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depths[i] = self.find_max_numbers(loadnet, f"layers.{i}.residual_group.blocks") + 1
num_heads[i] = loadnet[f"layers.{i}.residual_group.blocks.0.attn.relative_position_bias_table"].shape[1]
if "conv_hr.weight" in loadnet:
upsampler = "nearest+conv"
elif "conv_before_upsample.0.weight" in loadnet:
upsampler = "pixelshuffle"
elif "upsample.0.weight" in loadnet:
upsampler = "pixelshuffledirect"
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else "3conv"
window_size = int(math.sqrt(loadnet["layers.0.residual_group.blocks.0.attn.relative_position_bias_table"].shape[0])) + 1
if "layers.0.residual_group.blocks.1.attn_mask" in loadnet:
attn_mask_0 = loadnet["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
patches_resolution = int(math.sqrt(attn_mask_0) * window_size)
else:
patches_resolution = window_size
if ape:
pos_embed_value = loadnet.get("absolute_pos_embed", [None, None])[1]
if pos_embed_value:
patches_resolution = int(math.sqrt(pos_embed_value))
img_size = patches_resolution
if img_size % window_size != 0:
for nice_number in [512, 256, 128, 96, 64, 48, 32, 24, 16]:
if nice_number % window_size != 0:
nice_number += window_size - (nice_number % window_size)
if nice_number == patches_resolution:
img_size = nice_number
break
model = SRFormer(img_size=img_size, in_chans=in_chans, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=None, ape=ape, patch_norm=patch_norm, upscale=netscale, upsampler=upsampler, resi_connection=resi_connection)
if model:
self.realesrganer = RealESRGANer(scale=netscale, model_path=os.path.join("weights", "upscale", upscale_model), model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
elif upscale_model:
import PIL
from image_gen_aux import UpscaleWithModel
class UpscaleWithModel_Gfpgan(UpscaleWithModel):
def cv2pil(self, image):
new_image = image.copy()
if new_image.ndim == 2:
pass
elif new_image.shape[2] == 3:
new_image = cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB)
elif new_image.shape[2] == 4:
new_image = cv2.cvtColor(new_image, cv2.COLOR_BGRA2RGBA)
new_image = PIL.Image.fromarray(new_image)
return new_image
def pil2cv(self, image):
new_image = np.array(image, dtype=np.uint8)
if new_image.ndim == 2:
pass
elif new_image.shape[2] == 3:
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
elif new_image.shape[2] == 4:
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA)
return new_image
def enhance(self, img, outscale=None):
h_input, w_input = img.shape[0:2]
pil_img = self.cv2pil(img)
pil_img = self.__call__(pil_img)
cv_image = self.pil2cv(pil_img)
if outscale is not None and outscale != float(netscale):
interpolation = cv2.INTER_AREA if outscale < float(netscale) else cv2.INTER_LANCZOS4
cv_image = cv2.resize(
cv_image, (
int(w_input * outscale),
int(h_input * outscale),
), interpolation=interpolation)
return cv_image, None
device = "cuda" if torch.cuda.is_available() else "cpu"
upscaler = UpscaleWithModel.from_pretrained(os.path.join("weights", "upscale", upscale_model)).to(device)
upscaler.__class__ = UpscaleWithModel_Gfpgan
self.realesrganer = upscaler
def initFaceEnhancerModel(self, face_restoration, face_detection):
model_rootpath = os.path.join("weights", "face")
model_path = os.path.join(model_rootpath, face_restoration)
download_from_url(face_models[face_restoration][0], face_restoration, model_rootpath)
self.modelInUse = f"_{os.path.splitext(face_restoration)[0]}" + self.modelInUse
from gfpgan.utils import GFPGANer
resolution = 512
channel_multiplier = None
if face_restoration and face_restoration.startswith("GFPGANv1."):
arch = "clean"
channel_multiplier = 2
elif face_restoration and face_restoration.startswith("RestoreFormer"):
arch = "RestoreFormer++" if face_restoration.startswith("RestoreFormer++") else "RestoreFormer"
elif face_restoration == 'CodeFormer.pth':
arch = "CodeFormer"
elif face_restoration.startswith("GPEN-BFR-"):
arch = "GPEN"
channel_multiplier = 2
if "1024" in face_restoration:
arch = "GPEN-1024"
resolution = 1024
elif "2048" in face_restoration:
arch = "GPEN-2048"
resolution = 2048
self.face_enhancer = GFPGANer(model_path=model_path, upscale=self.scale, arch=arch, channel_multiplier=channel_multiplier, model_rootpath=model_rootpath, det_model=face_detection, resolution=resolution)
def inference(self, gallery, face_restoration, upscale_model, scale: float, face_detection, face_detection_threshold: any, face_detection_only_center: bool, outputWithModelName: bool, save_as_png: bool, progress=None):
try:
if not gallery or (not face_restoration and not upscale_model):
raise ValueError("Invalid parameter setting")
gallery_len = len(gallery)
print(face_restoration, upscale_model, scale, f"gallery length: {gallery_len}")
timer = Timer()
self.scale = scale
progressTotal = gallery_len + 1
progressRatio = 0.5 if upscale_model and face_restoration else 1
print(f"progressRatio: {progressRatio}")
current_progress = 0
if progress:
progress(0, desc="Initializing models...")
if upscale_model:
self.initBGUpscaleModel(upscale_model)
current_progress += progressRatio/progressTotal
if progress:
progress(current_progress, desc="BG upscale model initialized.")
timer.checkpoint(f"Initialize BG upscale model")
if face_restoration:
self.initFaceEnhancerModel(face_restoration, face_detection)
current_progress += progressRatio/progressTotal
if progress:
progress(current_progress, desc="Face enhancer model initialized.")
timer.checkpoint(f"Initialize face enhancer model")
timer.report()
if not outputWithModelName:
self.modelInUse = ""
files = []
unique_id = str(int(time.time()))
zip_cropf_path = f"output/{unique_id}_cropped_faces{self.modelInUse}.zip"
zipf_cropf = zipfile.ZipFile(zip_cropf_path, 'w', zipfile.ZIP_DEFLATED)
zip_restoref_path = f"output/{unique_id}_restored_faces{self.modelInUse}.zip"
zipf_restoref = zipfile.ZipFile(zip_restoref_path, 'w', zipfile.ZIP_DEFLATED)
zip_cmp_path = f"output/{unique_id}_cmp{self.modelInUse}.zip"
zipf_cmp = zipfile.ZipFile(zip_cmp_path, 'w', zipfile.ZIP_DEFLATED)
zip_restore_path = f"output/{unique_id}_restored_images{self.modelInUse}.zip"
zipf_restore = zipfile.ZipFile(zip_restore_path, 'w', zipfile.ZIP_DEFLATED)
is_auto_split_upscale = True
name_counters = defaultdict(int)
for gallery_idx, value in enumerate(gallery):
img_path = None
try:
if value is None or not value:
print(f"Warning: Invalid gallery item at index {gallery_idx}. Skipping.")
continue
img_path = str(value[0])
img_name = os.path.basename(img_path)
basename, extension = os.path.splitext(img_name)
name_counters[img_name] += 1
if name_counters[img_name] > 1:
basename = f"{basename}_{name_counters[img_name] - 1:02d}"
img_cv2 = cv2.imdecode(np.fromfile(img_path, np.uint8), cv2.IMREAD_UNCHANGED)
if img_cv2 is None:
print(f"Warning: Could not read or decode image '{img_path}'. Skipping this image.")
continue
img_mode = "RGBA" if len(img_cv2.shape) == 3 and img_cv2.shape[2] == 4 else None
if len(img_cv2.shape) == 2:
img_cv2 = cv2.cvtColor(img_cv2, cv2.COLOR_GRAY2BGR)
print(f"> Processing image {gallery_idx:02d}, Shape: {img_cv2.shape}")
bg_upsample_img = None
if upscale_model and self.realesrganer and hasattr(self.realesrganer, "enhance"):
bg_upsample_img, _ = auto_split_upscale(img_cv2, self.realesrganer.enhance, self.scale) if is_auto_split_upscale else self.realesrganer.enhance(img_cv2, outscale=self.scale)
current_progress += progressRatio/progressTotal
if progress:
progress(current_progress, desc=f"Image {gallery_idx:02d}: Background upscaling...")
timer.checkpoint(f"Image {gallery_idx:02d}: Background upscale section")
if face_restoration and self.face_enhancer:
cropped_faces, restored_aligned, bg_upsample_img = self.face_enhancer.enhance(img_cv2, has_aligned=False, only_center_face=face_detection_only_center, paste_back=True, bg_upsample_img=bg_upsample_img, eye_dist_threshold=face_detection_threshold)
if cropped_faces and restored_aligned:
for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_aligned)):
save_crop_path = f"output/{basename}_{idx:02d}_cropped_faces{self.modelInUse}.png"
self.imwriteUTF8(save_crop_path, cropped_face)
zipf_cropf.write(save_crop_path, arcname=os.path.basename(save_crop_path))
save_restore_path = f"output/{basename}_{idx:02d}_restored_faces{self.modelInUse}.png"
self.imwriteUTF8(save_restore_path, restored_face)
zipf_restoref.write(save_restore_path, arcname=os.path.basename(save_restore_path))
save_cmp_path = f"output/{basename}_{idx:02d}_cmp{self.modelInUse}.png"
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
self.imwriteUTF8(save_cmp_path, cmp_img)
zipf_cmp.write(save_cmp_path, arcname=os.path.basename(save_cmp_path))
files.append(save_crop_path)
files.append(save_restore_path)
files.append(save_cmp_path)
current_progress += progressRatio/progressTotal
if progress:
progress(current_progress, desc=f"Image {gallery_idx:02d}: Face enhancement...")
timer.checkpoint(f"Image {gallery_idx:02d}: Face enhancer section")
restored_img = bg_upsample_img
timer.report()
if restored_img is None:
print(f"Warning: Processing resulted in no image for '{img_path}'. Skipping output.")
continue
if save_as_png:
final_extension = ".png"
else:
final_extension = ".png" if img_mode == "RGBA" else (extension if extension else ".jpg")
save_path = f"output/{basename}{self.modelInUse}{final_extension}"
self.imwriteUTF8(save_path, restored_img)
zipf_restore.write(save_path, arcname=os.path.basename(save_path))
restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
files.append(save_path)
except RuntimeError as error:
print(f"Runtime Error while processing image {gallery_idx} ({img_path or 'unknown path'}): {error}")
print(traceback.format_exc())
except Exception as general_error:
print(f"An unexpected error occurred while processing image {gallery_idx} ({img_path or 'unknown path'}): {general_error}")
print(traceback.format_exc())
continue
if progress:
progress(1, desc=f"Processing complete.")
timer.report_all()
zipf_cropf.close()
zipf_restoref.close()
zipf_cmp.close()
zipf_restore.close()
except Exception as error:
print(f"Global exception occurred: {error}")
print(traceback.format_exc())
return None, None
finally:
if hasattr(self, 'face_enhancer') and self.face_enhancer:
self.face_enhancer._cleanup()
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
return files, [zip_cropf_path, zip_restoref_path, zip_cmp_path, zip_restore_path] if face_restoration else [zip_restore_path]
def find_max_numbers(self, state_dict, findkeys):
if isinstance(findkeys, str):
findkeys = [findkeys]
max_values = defaultdict(lambda: None)
patterns = {findkey: re.compile(rf"^{re.escape(findkey)}\.(\d+)\.") for findkey in findkeys}
for key in state_dict:
for findkey, pattern in patterns.items():
if match := pattern.match(key):
num = int(match.group(1))
max_values[findkey] = max(num, max_values[findkey] if max_values[findkey] is not None else num)
return tuple(max_values[findkey] for findkey in findkeys) if len(findkeys) > 1 else max_values[findkeys[0]]
def find_divisor_for_quotient(self, a: int, c: int):
if c == 0:
raise ValueError("c cannot be zero to avoid division by zero.")
b_float = a / c
if b_float.is_integer():
return int(b_float)
ceil_b = math.ceil(b_float)
floor_b = math.floor(b_float)
if a // ceil_b == c:
return ceil_b if ceil_b == b_float else float(ceil_b)
if a // floor_b == c:
return floor_b if floor_b == b_float else float(floor_b)
if c == a // b_float:
return b_float
if c == a // (b_float - 0.01):
return b_float - 0.01
if c == a // (b_float + 0.01):
return b_float + 0.01
raise ValueError(f"Could not find a number b such that a // b == c. a={a}, c={c}")
def imwriteUTF8(self, save_path, image):
img_name = os.path.basename(save_path)
_, extension = os.path.splitext(img_name)
is_success, im_buf_arr = cv2.imencode(extension, image)
if is_success:
im_buf_arr.tofile(save_path)
class Timer:
def __init__(self):
self.start_time = time.perf_counter()
self.checkpoints = [("Start", self.start_time)]
def checkpoint(self, label="Checkpoint"):
now = time.perf_counter()
self.checkpoints.append((label, now))
def report(self, is_clear_checkpoints=True):
max_label_length = max(len(label) for label, _ in self.checkpoints)
prev_time = self.checkpoints[0][1]
for label, curr_time in self.checkpoints[1:]:
elapsed = curr_time - prev_time
print(f"{label.ljust(max_label_length)}: {elapsed:.3f} seconds")
prev_time = curr_time
if is_clear_checkpoints:
self.checkpoints.clear()
self.checkpoint()
def report_all(self):
print("\n> Execution Time Report:")
max_label_length = max(len(label) for label, _ in self.checkpoints) if len(self.checkpoints) > 0 else 0
prev_time = self.start_time
for label, curr_time in self.checkpoints[1:]:
elapsed = curr_time - prev_time
print(f"{label.ljust(max_label_length)}: {elapsed:.3f} seconds")
prev_time = curr_time
total_time = self.checkpoints[-1][1] - self.start_time
print(f"{'Total Execution Time'.ljust(max_label_length)}: {total_time:.3f} seconds\n")
self.checkpoints.clear()
def restart(self):
self.start_time = time.perf_counter()
self.checkpoints = [("Start", self.start_time)]
if __name__ == "__main__":
if torch.cuda.is_available():
torch.cuda.set_per_process_memory_fraction(0.900, device='cuda:0')
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
os.makedirs('output', exist_ok=True)
os.makedirs('input', exist_ok=True)
fastapi_app = FastAPI(title="Face Upscale API")
_bearer_scheme = HTTPBearer(auto_error=False)
_api_bearer_token = os.getenv("API_BEARER_TOKEN", "changeme")
def _verify_bearer_token(credentials: Optional[HTTPAuthorizationCredentials] = Depends(_bearer_scheme)):
if not _bearer_scheme:
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Unauthorized")
if not credentials or credentials.scheme.lower() != "bearer":
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid auth scheme")
token = credentials.credentials
if not _api_bearer_token or token != _api_bearer_token:
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token")
return True
DEFAULT_FACE_MODEL = 'GFPGANv1.4.pth'
DEFAULT_UPSCALE_MODEL = 'SRVGG, realesr-general-x4v3.pth'
DEFAULT_SCALE = 4.0
DEFAULT_FACE_DET = 'retinaface_resnet50'
DEFAULT_FACE_DET_THRESHOLD = 10 |