├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── example_workflows └── dilight_example_workflow.json ├── modules └── neural_texture_embedding.py ├── nodes ├── load_dilight_controlnet_node.py └── prepare_dilight_cond_node.py └── pyproject.toml /.gitignore: -------------------------------------------------------------------------------- 1 | **/__pycache__ 2 | .DS_Store 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI-DiLightNet 2 | 3 | ComfyUI nodes to use [DiLightNet](https://github.com/iamNCJ/DiLightNet) 4 | 5 | These nodes can run DiLightNet, but the Dust3r or BlenderPy implementations to create lighting are not included. Expect those to be added to seperate repos when time allows. 6 | 7 | ## Installation 8 | You will need to download the [DiLightNet Controlnet](https://huggingface.co/dilightnet/DiLightNet/blob/main/diffusion_pytorch_model.safetensors) to your `ComfyUI/models/controlnet` directory. 9 | 10 | Additionally, you will need the [Stable Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2) model and clip text encoder. 11 | 12 | ## Examples 13 | Example workflow can be found in `example_workflows` 14 | 15 | The following output is based on example inputs from the DiLightNet repo given a lighting environment that has a reddish hue. 16 | ![dilight_example](https://github.com/user-attachments/assets/2c143014-0e4d-443b-9d75-401e084101a3) 17 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .nodes.load_dilight_controlnet_node import LoadDiLightControlNetNode 2 | from .nodes.prepare_dilight_cond_node import PrepareDiLightCondNode 3 | 4 | 5 | NODE_CLASS_MAPPINGS = { 6 | "LoadDiLightControlNet": LoadDiLightControlNetNode, 7 | "PrepareDiLightCond": PrepareDiLightCondNode, 8 | } 9 | 10 | NODE_DISPLAY_NAME_MAPPINGS = { 11 | "LoadDiLightControlNet": "Load DiLight ControlNet", 12 | "PrepareDiLightCond": "Prepare DiLight CN Image", 13 | } 14 | -------------------------------------------------------------------------------- /example_workflows/dilight_example_workflow.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 45, 3 | "last_link_id": 51, 4 | "nodes": [ 5 | { 6 | "id": 3, 7 | "type": "KSampler", 8 | "pos": { 9 | "0": 863, 10 | "1": 186 11 | }, 12 | "size": { 13 | "0": 210, 14 | "1": 262 15 | }, 16 | "flags": {}, 17 | "order": 20, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "model", 22 | "type": "MODEL", 23 | "link": 13 24 | }, 25 | { 26 | "name": "positive", 27 | "type": "CONDITIONING", 28 | "link": 18 29 | }, 30 | { 31 | "name": "negative", 32 | "type": "CONDITIONING", 33 | "link": 19 34 | }, 35 | { 36 | "name": "latent_image", 37 | "type": "LATENT", 38 | "link": 2 39 | } 40 | ], 41 | "outputs": [ 42 | { 43 | "name": "LATENT", 44 | "type": "LATENT", 45 | "links": [ 46 | 7 47 | ], 48 | "slot_index": 0 49 | } 50 | ], 51 | "properties": { 52 | "Node name for S&R": "KSampler" 53 | }, 54 | "widgets_values": [ 55 | 0, 56 | "fixed", 57 | 20, 58 | 3, 59 | "uni_pc", 60 | "normal", 61 | 1 62 | ] 63 | }, 64 | { 65 | "id": 10, 66 | "type": "UNETLoader", 67 | "pos": { 68 | "0": 866, 69 | "1": 492 70 | }, 71 | "size": { 72 | "0": 210, 73 | "1": 82 74 | }, 75 | "flags": {}, 76 | "order": 0, 77 | "mode": 0, 78 | "inputs": [], 79 | "outputs": [ 80 | { 81 | "name": "MODEL", 82 | "type": "MODEL", 83 | "links": [ 84 | 13 85 | ], 86 | "slot_index": 0, 87 | "shape": 3 88 | } 89 | ], 90 | "properties": { 91 | "Node name for S&R": "UNETLoader" 92 | }, 93 | "widgets_values": [ 94 | "sd2.safetensors", 95 | "default" 96 | ] 97 | }, 98 | { 99 | "id": 5, 100 | "type": "EmptyLatentImage", 101 | "pos": { 102 | "0": 863, 103 | "1": 626 104 | }, 105 | "size": { 106 | "0": 210, 107 | "1": 106 108 | }, 109 | "flags": {}, 110 | "order": 1, 111 | "mode": 0, 112 | "inputs": [], 113 | "outputs": [ 114 | { 115 | "name": "LATENT", 116 | "type": "LATENT", 117 | "links": [ 118 | 2 119 | ], 120 | "slot_index": 0 121 | } 122 | ], 123 | "properties": { 124 | "Node name for S&R": "EmptyLatentImage" 125 | }, 126 | "widgets_values": [ 127 | 512, 128 | 512, 129 | 1 130 | ] 131 | }, 132 | { 133 | "id": 30, 134 | "type": "LoadImage", 135 | "pos": { 136 | "0": -600, 137 | "1": 0 138 | }, 139 | "size": { 140 | "0": 315, 141 | "1": 314 142 | }, 143 | "flags": {}, 144 | "order": 2, 145 | "mode": 0, 146 | "inputs": [], 147 | "outputs": [ 148 | { 149 | "name": "IMAGE", 150 | "type": "IMAGE", 151 | "links": [ 152 | 38, 153 | 44 154 | ], 155 | "slot_index": 0, 156 | "shape": 3 157 | }, 158 | { 159 | "name": "MASK", 160 | "type": "MASK", 161 | "links": null, 162 | "shape": 3 163 | } 164 | ], 165 | "properties": { 166 | "Node name for S&R": "LoadImage" 167 | }, 168 | "widgets_values": [ 169 | "futuristic_soldier.png", 170 | "image" 171 | ] 172 | }, 173 | { 174 | "id": 8, 175 | "type": "VAEDecode", 176 | "pos": { 177 | "0": 1114, 178 | "1": 185 179 | }, 180 | "size": { 181 | "0": 210, 182 | "1": 46 183 | }, 184 | "flags": {}, 185 | "order": 21, 186 | "mode": 0, 187 | "inputs": [ 188 | { 189 | "name": "samples", 190 | "type": "LATENT", 191 | "link": 7 192 | }, 193 | { 194 | "name": "vae", 195 | "type": "VAE", 196 | "link": 10 197 | } 198 | ], 199 | "outputs": [ 200 | { 201 | "name": "IMAGE", 202 | "type": "IMAGE", 203 | "links": [ 204 | 45 205 | ], 206 | "slot_index": 0 207 | } 208 | ], 209 | "properties": { 210 | "Node name for S&R": "VAEDecode" 211 | } 212 | }, 213 | { 214 | "id": 7, 215 | "type": "CLIPTextEncode", 216 | "pos": { 217 | "0": 326, 218 | "1": 318 219 | }, 220 | "size": { 221 | "0": 210, 222 | "1": 174.67169189453125 223 | }, 224 | "flags": { 225 | "collapsed": true 226 | }, 227 | "order": 15, 228 | "mode": 0, 229 | "inputs": [ 230 | { 231 | "name": "clip", 232 | "type": "CLIP", 233 | "link": 11 234 | } 235 | ], 236 | "outputs": [ 237 | { 238 | "name": "CONDITIONING", 239 | "type": "CONDITIONING", 240 | "links": [ 241 | 16 242 | ], 243 | "slot_index": 0 244 | } 245 | ], 246 | "properties": { 247 | "Node name for S&R": "CLIPTextEncode" 248 | }, 249 | "widgets_values": [ 250 | "" 251 | ] 252 | }, 253 | { 254 | "id": 29, 255 | "type": "PrepareDiLightCond", 256 | "pos": { 257 | "0": 324, 258 | "1": 375 259 | }, 260 | "size": { 261 | "0": 201.60000610351562, 262 | "1": 126 263 | }, 264 | "flags": {}, 265 | "order": 18, 266 | "mode": 0, 267 | "inputs": [ 268 | { 269 | "name": "mask", 270 | "type": "MASK", 271 | "link": 42 272 | }, 273 | { 274 | "name": "image", 275 | "type": "IMAGE", 276 | "link": 38 277 | }, 278 | { 279 | "name": "diff_image", 280 | "type": "IMAGE", 281 | "link": 47 282 | }, 283 | { 284 | "name": "ggx5", 285 | "type": "IMAGE", 286 | "link": 49 287 | }, 288 | { 289 | "name": "ggx13", 290 | "type": "IMAGE", 291 | "link": 50 292 | }, 293 | { 294 | "name": "ggx34", 295 | "type": "IMAGE", 296 | "link": 51 297 | } 298 | ], 299 | "outputs": [ 300 | { 301 | "name": "IMAGE", 302 | "type": "IMAGE", 303 | "links": [ 304 | 37 305 | ], 306 | "slot_index": 0, 307 | "shape": 3 308 | } 309 | ], 310 | "properties": { 311 | "Node name for S&R": "PrepareDiLightCond" 312 | } 313 | }, 314 | { 315 | "id": 35, 316 | "type": "ImageConcanate", 317 | "pos": { 318 | "0": 1111, 319 | "1": 27 320 | }, 321 | "size": { 322 | "0": 210, 323 | "1": 102 324 | }, 325 | "flags": {}, 326 | "order": 22, 327 | "mode": 0, 328 | "inputs": [ 329 | { 330 | "name": "image1", 331 | "type": "IMAGE", 332 | "link": 44 333 | }, 334 | { 335 | "name": "image2", 336 | "type": "IMAGE", 337 | "link": 45 338 | } 339 | ], 340 | "outputs": [ 341 | { 342 | "name": "IMAGE", 343 | "type": "IMAGE", 344 | "links": [ 345 | 46 346 | ], 347 | "slot_index": 0, 348 | "shape": 3 349 | } 350 | ], 351 | "properties": { 352 | "Node name for S&R": "ImageConcanate" 353 | }, 354 | "widgets_values": [ 355 | "right", 356 | false 357 | ] 358 | }, 359 | { 360 | "id": 11, 361 | "type": "CLIPLoader", 362 | "pos": { 363 | "0": 62, 364 | "1": 108 365 | }, 366 | "size": { 367 | "0": 210, 368 | "1": 82 369 | }, 370 | "flags": {}, 371 | "order": 3, 372 | "mode": 0, 373 | "inputs": [], 374 | "outputs": [ 375 | { 376 | "name": "CLIP", 377 | "type": "CLIP", 378 | "links": [ 379 | 11, 380 | 12 381 | ], 382 | "slot_index": 0, 383 | "shape": 3 384 | } 385 | ], 386 | "properties": { 387 | "Node name for S&R": "CLIPLoader" 388 | }, 389 | "widgets_values": [ 390 | "open_clip_pytorch_model.bin", 391 | "stable_diffusion" 392 | ] 393 | }, 394 | { 395 | "id": 37, 396 | "type": "Note", 397 | "pos": { 398 | "0": -572, 399 | "1": -169 400 | }, 401 | "size": { 402 | "0": 278.365478515625, 403 | "1": 108.98002624511719 404 | }, 405 | "flags": {}, 406 | "order": 4, 407 | "mode": 0, 408 | "inputs": [], 409 | "outputs": [], 410 | "properties": {}, 411 | "widgets_values": [ 412 | "Images taken from:\nhttps://github.com/iamNCJ/DiLightNet/tree/main/examples/input" 413 | ], 414 | "color": "#432", 415 | "bgcolor": "#653" 416 | }, 417 | { 418 | "id": 34, 419 | "type": "LoadDiLightControlNet", 420 | "pos": { 421 | "0": 593, 422 | "1": 416 423 | }, 424 | "size": [ 425 | 210, 426 | 58 427 | ], 428 | "flags": {}, 429 | "order": 5, 430 | "mode": 0, 431 | "inputs": [], 432 | "outputs": [ 433 | { 434 | "name": "CONTROL_NET", 435 | "type": "CONTROL_NET", 436 | "links": [ 437 | 43 438 | ], 439 | "slot_index": 0, 440 | "shape": 3 441 | } 442 | ], 443 | "properties": { 444 | "Node name for S&R": "LoadDiLightControlNet" 445 | }, 446 | "widgets_values": [ 447 | "dilight.safetensors" 448 | ] 449 | }, 450 | { 451 | "id": 13, 452 | "type": "PreviewImage", 453 | "pos": { 454 | "0": 1355, 455 | "1": 22 456 | }, 457 | "size": { 458 | "0": 850.4505615234375, 459 | "1": 461.093505859375 460 | }, 461 | "flags": {}, 462 | "order": 23, 463 | "mode": 0, 464 | "inputs": [ 465 | { 466 | "name": "images", 467 | "type": "IMAGE", 468 | "link": 46 469 | } 470 | ], 471 | "outputs": [], 472 | "properties": { 473 | "Node name for S&R": "PreviewImage" 474 | } 475 | }, 476 | { 477 | "id": 15, 478 | "type": "ControlNetApplyAdvanced", 479 | "pos": { 480 | "0": 589, 481 | "1": 204 482 | }, 483 | "size": { 484 | "0": 226.8000030517578, 485 | "1": 166 486 | }, 487 | "flags": {}, 488 | "order": 19, 489 | "mode": 0, 490 | "inputs": [ 491 | { 492 | "name": "positive", 493 | "type": "CONDITIONING", 494 | "link": 17 495 | }, 496 | { 497 | "name": "negative", 498 | "type": "CONDITIONING", 499 | "link": 16 500 | }, 501 | { 502 | "name": "control_net", 503 | "type": "CONTROL_NET", 504 | "link": 43 505 | }, 506 | { 507 | "name": "image", 508 | "type": "IMAGE", 509 | "link": 37 510 | } 511 | ], 512 | "outputs": [ 513 | { 514 | "name": "positive", 515 | "type": "CONDITIONING", 516 | "links": [ 517 | 18 518 | ], 519 | "slot_index": 0, 520 | "shape": 3 521 | }, 522 | { 523 | "name": "negative", 524 | "type": "CONDITIONING", 525 | "links": [ 526 | 19 527 | ], 528 | "slot_index": 1, 529 | "shape": 3 530 | } 531 | ], 532 | "properties": { 533 | "Node name for S&R": "ControlNetApplyAdvanced" 534 | }, 535 | "widgets_values": [ 536 | 1, 537 | 0, 538 | 1 539 | ] 540 | }, 541 | { 542 | "id": 39, 543 | "type": "LoadImage", 544 | "pos": { 545 | "0": -601, 546 | "1": 359 547 | }, 548 | "size": [ 549 | 315, 550 | 314 551 | ], 552 | "flags": {}, 553 | "order": 6, 554 | "mode": 0, 555 | "inputs": [], 556 | "outputs": [ 557 | { 558 | "name": "IMAGE", 559 | "type": "IMAGE", 560 | "links": [ 561 | 47 562 | ], 563 | "shape": 3, 564 | "slot_index": 0 565 | }, 566 | { 567 | "name": "MASK", 568 | "type": "MASK", 569 | "links": [ 570 | 48 571 | ], 572 | "shape": 3, 573 | "slot_index": 1 574 | } 575 | ], 576 | "properties": { 577 | "Node name for S&R": "LoadImage" 578 | }, 579 | "widgets_values": [ 580 | "futuristic_soldier_diffuse.png", 581 | "image" 582 | ] 583 | }, 584 | { 585 | "id": 40, 586 | "type": "LoadImage", 587 | "pos": { 588 | "0": -607, 589 | "1": 726 590 | }, 591 | "size": { 592 | "0": 315, 593 | "1": 314 594 | }, 595 | "flags": {}, 596 | "order": 7, 597 | "mode": 0, 598 | "inputs": [], 599 | "outputs": [ 600 | { 601 | "name": "IMAGE", 602 | "type": "IMAGE", 603 | "links": [ 604 | 49 605 | ], 606 | "shape": 3, 607 | "slot_index": 0 608 | }, 609 | { 610 | "name": "MASK", 611 | "type": "MASK", 612 | "links": [], 613 | "shape": 3, 614 | "slot_index": 1 615 | } 616 | ], 617 | "properties": { 618 | "Node name for S&R": "LoadImage" 619 | }, 620 | "widgets_values": [ 621 | "futuristic_soldier_ggx0.05.png", 622 | "image" 623 | ] 624 | }, 625 | { 626 | "id": 41, 627 | "type": "LoadImage", 628 | "pos": { 629 | "0": -612, 630 | "1": 1086 631 | }, 632 | "size": { 633 | "0": 315, 634 | "1": 314 635 | }, 636 | "flags": {}, 637 | "order": 8, 638 | "mode": 0, 639 | "inputs": [], 640 | "outputs": [ 641 | { 642 | "name": "IMAGE", 643 | "type": "IMAGE", 644 | "links": [ 645 | 50 646 | ], 647 | "shape": 3, 648 | "slot_index": 0 649 | }, 650 | { 651 | "name": "MASK", 652 | "type": "MASK", 653 | "links": [], 654 | "shape": 3, 655 | "slot_index": 1 656 | } 657 | ], 658 | "properties": { 659 | "Node name for S&R": "LoadImage" 660 | }, 661 | "widgets_values": [ 662 | "futuristic_soldier_ggx0.13.png", 663 | "image" 664 | ] 665 | }, 666 | { 667 | "id": 42, 668 | "type": "LoadImage", 669 | "pos": { 670 | "0": -616, 671 | "1": 1448 672 | }, 673 | "size": { 674 | "0": 315, 675 | "1": 314 676 | }, 677 | "flags": {}, 678 | "order": 9, 679 | "mode": 0, 680 | "inputs": [], 681 | "outputs": [ 682 | { 683 | "name": "IMAGE", 684 | "type": "IMAGE", 685 | "links": [ 686 | 51 687 | ], 688 | "shape": 3, 689 | "slot_index": 0 690 | }, 691 | { 692 | "name": 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805 | }, 806 | "size": { 807 | "0": 210, 808 | "1": 78.14202117919922 809 | }, 810 | "flags": {}, 811 | "order": 12, 812 | "mode": 0, 813 | "inputs": [], 814 | "outputs": [], 815 | "properties": {}, 816 | "widgets_values": [ 817 | "CLIP: This is the SD2 Clip\n\nhttps://huggingface.co/stabilityai/stable-diffusion-2" 818 | ], 819 | "color": "#432", 820 | "bgcolor": "#653" 821 | }, 822 | { 823 | "id": 45, 824 | "type": "Note", 825 | "pos": { 826 | "0": 864, 827 | "1": 784 828 | }, 829 | "size": [ 830 | 210, 831 | 78.14202117919922 832 | ], 833 | "flags": {}, 834 | "order": 13, 835 | "mode": 0, 836 | "inputs": [], 837 | "outputs": [], 838 | "properties": {}, 839 | "widgets_values": [ 840 | "Model: This is SD2\n\nhttps://huggingface.co/stabilityai/stable-diffusion-2" 841 | ], 842 | "color": "#432", 843 | "bgcolor": "#653" 844 | }, 845 | { 846 | "id": 44, 847 | "type": "Note", 848 | "pos": { 849 | "0": 301, 850 | "1": -135 851 | }, 852 | "size": [ 853 | 237.05928259357813, 854 | 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"links": [ 908 | [ 909 | 2, 910 | 5, 911 | 0, 912 | 3, 913 | 3, 914 | "LATENT" 915 | ], 916 | [ 917 | 7, 918 | 3, 919 | 0, 920 | 8, 921 | 0, 922 | "LATENT" 923 | ], 924 | [ 925 | 10, 926 | 12, 927 | 0, 928 | 8, 929 | 1, 930 | "VAE" 931 | ], 932 | [ 933 | 11, 934 | 11, 935 | 0, 936 | 7, 937 | 0, 938 | "CLIP" 939 | ], 940 | [ 941 | 12, 942 | 11, 943 | 0, 944 | 6, 945 | 0, 946 | "CLIP" 947 | ], 948 | [ 949 | 13, 950 | 10, 951 | 0, 952 | 3, 953 | 0, 954 | "MODEL" 955 | ], 956 | [ 957 | 16, 958 | 7, 959 | 0, 960 | 15, 961 | 1, 962 | "CONDITIONING" 963 | ], 964 | [ 965 | 17, 966 | 6, 967 | 0, 968 | 15, 969 | 0, 970 | "CONDITIONING" 971 | ], 972 | [ 973 | 18, 974 | 15, 975 | 0, 976 | 3, 977 | 1, 978 | "CONDITIONING" 979 | ], 980 | [ 981 | 19, 982 | 15, 983 | 1, 984 | 3, 985 | 2, 986 | "CONDITIONING" 987 | ], 988 | [ 989 | 37, 990 | 29, 991 | 0, 992 | 15, 993 | 3, 994 | "IMAGE" 995 | ], 996 | [ 997 | 38, 998 | 30, 999 | 0, 1000 | 29, 1001 | 1, 1002 | "IMAGE" 1003 | ], 1004 | [ 1005 | 42, 1006 | 32, 1007 | 0, 1008 | 29, 1009 | 0, 1010 | "MASK" 1011 | ], 1012 | [ 1013 | 43, 1014 | 34, 1015 | 0, 1016 | 15, 1017 | 2, 1018 | "CONTROL_NET" 1019 | ], 1020 | [ 1021 | 44, 1022 | 30, 1023 | 0, 1024 | 35, 1025 | 0, 1026 | "IMAGE" 1027 | ], 1028 | [ 1029 | 45, 1030 | 8, 1031 | 0, 1032 | 35, 1033 | 1, 1034 | "IMAGE" 1035 | ], 1036 | [ 1037 | 46, 1038 | 35, 1039 | 0, 1040 | 13, 1041 | 0, 1042 | "IMAGE" 1043 | ], 1044 | [ 1045 | 47, 1046 | 39, 1047 | 0, 1048 | 29, 1049 | 2, 1050 | "IMAGE" 1051 | ], 1052 | [ 1053 | 48, 1054 | 39, 1055 | 1, 1056 | 32, 1057 | 0, 1058 | "MASK" 1059 | ], 1060 | [ 1061 | 49, 1062 | 40, 1063 | 0, 1064 | 29, 1065 | 3, 1066 | "IMAGE" 1067 | ], 1068 | [ 1069 | 50, 1070 | 41, 1071 | 0, 1072 | 29, 1073 | 4, 1074 | "IMAGE" 1075 | ], 1076 | [ 1077 | 51, 1078 | 42, 1079 | 0, 1080 | 29, 1081 | 5, 1082 | "IMAGE" 1083 | ] 1084 | ], 1085 | "groups": [], 1086 | "config": {}, 1087 | "extra": { 1088 | "ds": { 1089 | "scale": 0.42409761837248483, 1090 | "offset": [ 1091 | 624.2141525153642, 1092 | 570.4821303384275 1093 | ] 1094 | } 1095 | }, 1096 | "version": 0.4 1097 | } -------------------------------------------------------------------------------- /modules/neural_texture_embedding.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple 2 | 3 | import torch 4 | import torch.nn.functional as F 5 | from torch import nn 6 | 7 | import comfy.ops 8 | ops = comfy.ops.disable_weight_init 9 | 10 | 11 | class ResBlock(nn.Module): 12 | def __init__(self, dim): 13 | super().__init__() 14 | self.conv = nn.Sequential( 15 | ops.Conv2d(dim, dim, 3, 1, 1), 16 | ops.GroupNorm(num_groups=8, num_channels=dim), 17 | nn.SiLU(inplace=True), 18 | ops.Conv2d(dim, dim, 3, 1, 1), 19 | ) 20 | 21 | def forward(self, x): 22 | return x + self.conv(x) 23 | 24 | 25 | class NeuralTextureEncoder(nn.Module): 26 | def __init__(self, in_dim=3, out_dim=16, dims=(32, 64, 128), groups=8): 27 | super().__init__() 28 | self.model = nn.Sequential( 29 | ops.Conv2d(in_dim, dims[0], kernel_size=3, padding=1), 30 | nn.SiLU(inplace=True), 31 | 32 | # down 1 33 | ops.Conv2d(dims[0], dims[1], kernel_size=3, padding=1, stride=2), 34 | ops.GroupNorm(num_groups=groups, num_channels=dims[1]), 35 | nn.SiLU(inplace=True), 36 | 37 | # down 2 38 | ops.Conv2d(dims[1], dims[2], kernel_size=3, padding=1, stride=2), 39 | ops.GroupNorm(num_groups=groups, num_channels=dims[2]), 40 | nn.SiLU(inplace=True), 41 | 42 | # res blocks 43 | ResBlock(dims[2]), 44 | ResBlock(dims[2]), 45 | ResBlock(dims[2]), 46 | ResBlock(dims[2]), 47 | 48 | # up 1 49 | ops.ConvTranspose2d(dims[2], dims[1], kernel_size=4, padding=1, stride=2), 50 | ops.GroupNorm(num_groups=groups, num_channels=dims[1]), 51 | nn.SiLU(inplace=True), 52 | 53 | # up 2 54 | ops.ConvTranspose2d(dims[1], dims[0], kernel_size=4, padding=1, stride=2), 55 | ops.GroupNorm(num_groups=groups, num_channels=dims[0]), 56 | nn.SiLU(inplace=True), 57 | 58 | # out 59 | ops.Conv2d(dims[0], out_dim, kernel_size=3, padding=1), 60 | ) 61 | self.gradient_checkpointing = False 62 | 63 | def forward(self, x): 64 | x = self.model(x) 65 | return x 66 | 67 | 68 | class NeuralTextureEmbedding(nn.Module): 69 | def __init__( 70 | self, 71 | conditioning_embedding_channels: int, 72 | conditioning_channels: int = 3, 73 | block_out_channels: Tuple[int] = (16, 32, 96, 256), 74 | shading_hint_channels: int = 12, # diffuse + 3 * ggx 75 | ): 76 | super().__init__() 77 | self.conditioning_channels = conditioning_channels 78 | self.shading_hint_channels = shading_hint_channels 79 | 80 | self.conv_in = ops.Conv2d(shading_hint_channels, block_out_channels[0], kernel_size=3, padding=1) 81 | self.neural_texture_encoder = NeuralTextureEncoder(in_dim=conditioning_channels, out_dim=shading_hint_channels) 82 | 83 | self.blocks = nn.ModuleList([]) 84 | 85 | for i in range(len(block_out_channels) - 1): 86 | channel_in = block_out_channels[i] 87 | channel_out = block_out_channels[i + 1] 88 | self.blocks.append(ops.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) 89 | self.blocks.append(ops.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) 90 | 91 | self.conv_out = ops.Conv2d( 92 | block_out_channels[-1], 93 | conditioning_embedding_channels, 94 | kernel_size=3, 95 | padding=1 96 | ) 97 | 98 | def forward(self, all_conditioning, emb=None, context=None): 99 | # conditioning: [BS, 4 + 12, 512, 512] # RGB ref image + shading hint (diffuse + 3 * ggx) 100 | conditioning, shading_hint = torch.split( 101 | all_conditioning, 102 | [self.conditioning_channels, self.shading_hint_channels], 103 | dim=1 104 | ) 105 | embedding = self.neural_texture_encoder(conditioning) # [BS, 15, 512, 512] 106 | 107 | # multiply shading hint to each channel 108 | embedding = embedding * shading_hint 109 | embedding = self.conv_in(embedding) 110 | embedding = F.silu(embedding) 111 | 112 | for block in self.blocks: 113 | embedding = block(embedding) 114 | embedding = F.silu(embedding) 115 | 116 | embedding = self.conv_out(embedding) 117 | 118 | return embedding 119 | -------------------------------------------------------------------------------- /nodes/load_dilight_controlnet_node.py: -------------------------------------------------------------------------------- 1 | 2 | import torch 3 | 4 | import folder_paths 5 | import comfy.utils 6 | from comfy.cldm.cldm import ControlNet 7 | from nodes import ControlNetLoader 8 | 9 | from ..modules.neural_texture_embedding import NeuralTextureEmbedding 10 | 11 | 12 | class DiLightControlnet(ControlNet): 13 | def set_input_hint_block(self, input_hint_block): 14 | self.input_hint_block = input_hint_block 15 | 16 | def to(self, device): 17 | self.input_hint_block.to(device) 18 | 19 | return super().to(device) 20 | 21 | 22 | class LoadDiLightControlNetNode: 23 | @classmethod 24 | def INPUT_TYPES(s): 25 | return {"required": { 26 | "control_net_name": (folder_paths.get_filename_list("controlnet"), ), 27 | }} 28 | RETURN_TYPES = ("CONTROL_NET",) 29 | FUNCTION = "apply" 30 | 31 | CATEGORY = "dilight" 32 | 33 | def apply(self, control_net_name): 34 | controlnet, = ControlNetLoader().load_controlnet(control_net_name) 35 | controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) 36 | controlnet_data = comfy.utils.load_torch_file(controlnet_path, safe_load=True) 37 | state_dict = {} 38 | for key in list(controlnet_data.keys()): 39 | if 'controlnet_cond_embedding.' in key: 40 | new_key = key.replace('controlnet_cond_embedding.', '') 41 | state_dict[new_key] = controlnet_data[key] 42 | 43 | # https://huggingface.co/dilightnet/DiLightNet/blob/main/config.json 44 | embedder = NeuralTextureEmbedding( 45 | conditioning_embedding_channels=320, 46 | conditioning_channels=4, 47 | block_out_channels=[16, 32, 96, 256], 48 | shading_hint_channels=12 49 | ).to(torch.float16) 50 | embedder.load_state_dict(state_dict) 51 | 52 | controlnet.control_model.__class__ = DiLightControlnet 53 | controlnet.control_model.set_input_hint_block(embedder) 54 | 55 | return (controlnet,) 56 | -------------------------------------------------------------------------------- /nodes/prepare_dilight_cond_node.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class PrepareDiLightCondNode: 5 | @classmethod 6 | def INPUT_TYPES(s): 7 | return {"required": { 8 | "mask": ("MASK",), 9 | "image": ("IMAGE",), 10 | "diff_image": ("IMAGE",), 11 | "ggx5": ("IMAGE",), 12 | "ggx13": ("IMAGE",), 13 | "ggx34": ("IMAGE",), 14 | }} 15 | RETURN_TYPES = ("IMAGE",) 16 | FUNCTION = "prepare" 17 | 18 | CATEGORY = "dilight" 19 | 20 | def prepare(self, mask, image, diff_image, ggx5, ggx13, ggx34): 21 | # shape: mask + [1, 3, 512, 512] * 4 -> [1, 16, 512, 512] 22 | mask = mask.unsqueeze(-1) 23 | cond = torch.cat([diff_image, ggx5, ggx13, ggx34], dim=3) * mask 24 | cond = torch.cat([mask, image, cond], dim=3) 25 | return (cond,) 26 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui-dilightnet" 3 | description = "ComfyUI nodes to use [a/DiLightNet](https://github.com/iamNCJ/DiLightNet).\nThese nodes can run DiLightNet, but the Dust3r or BlenderPy implementations to create lighting are not included. Expect those to be added to seperate repos when time allows." 4 | version = "1.0.0" 5 | license = {file = "LICENSE"} 6 | 7 | [project.urls] 8 | Repository = "https://github.com/logtd/ComfyUI-DiLightNet" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "logtd" 13 | DisplayName = "ComfyUI-DiLightNet" 14 | Icon = "" 15 | --------------------------------------------------------------------------------