├── .github └── workflows │ └── publish.yml ├── README.md ├── __init__.py ├── example ├── flux Material transfer+depth.json ├── flux redux 2image.json └── redux semantic replacement image 1206.json ├── nodes.py └── pyproject.toml /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | paths: 9 | - "pyproject.toml" 10 | 11 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | # if this is a forked repository. Skipping the workflow. 16 | if: github.event.repository.fork == false 17 | steps: 18 | - name: Check out code 19 | uses: actions/checkout@v4 20 | with: 21 | submodules: true 22 | - name: Publish Custom Node 23 | uses: Comfy-Org/publish-node-action@main 24 | with: 25 | ## Add your own personal access token to your Github Repository secrets and reference it here. 26 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 27 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Comfyui_Flux_Style_Adjust (Redux) 2 | StyleModelApply adds more controls 3 | 4 | https://github.com/yichengup/Comfyui_Redux_Advanced 5 | Another Redux plugin of mine, this is an improved version, welcome to use it 6 | 7 | Note: Sorry, there is no basis for this weight separation, I just want to try it out for learning. 8 | The image weight is low, and the prompt word can only replace the image features with similar semantics. 9 | In addition, you can use it as a multi-node series to mix the images together for fun. 10 | This is an inaccurate node. 11 | 12 | ![image](https://github.com/user-attachments/assets/12fa2fb0-873c-4ca2-a53a-a417eb342c20) 13 | 14 | 2024/12/03 update：two image redux fusion+depth 15 | ![image](https://github.com/user-attachments/assets/5cff3165-05c3-47b0-816b-8ff644e0e83f) 16 | 17 | 2024/12/03 update：(Replace similar Redux image features with semantic features) 18 | -- prompt words：((( Snowy scene))),(((pig head))), 19 | ![image](https://github.com/user-attachments/assets/30d7655d-77c8-4b59-bfcc-c5e25c79dfdc) 20 | 21 | ![image](https://github.com/user-attachments/assets/36a37758-b7f7-4dd4-8934-c67609875879) 22 | 23 | two image redux fusion 24 | ![image](https://github.com/user-attachments/assets/29834407-01ce-4240-aabb-7dfea0a71e69) 25 | 26 | ![image](https://github.com/user-attachments/assets/ebfce090-fd92-4d25-b5a9-e4da8212a256) 27 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .nodes import StyleModelAdvancedApply 2 | 3 | NODE_CLASS_MAPPINGS = { 4 | "StyleModelAdvancedApply": StyleModelAdvancedApply, 5 | } 6 | 7 | NODE_DISPLAY_NAME_MAPPINGS = { 8 | "StyleModelAdvancedApply": "Style Model Advanced Apply", 9 | } 10 | 11 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 12 | -------------------------------------------------------------------------------- /example/flux Material transfer+depth.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 253, 3 | "last_link_id": 415, 4 | "nodes": [ 5 | { 6 | "id": 101, 7 | "type": "CLIPVisionEncode", 8 | "pos": [ 9 | -593.3097534179688, 10 | -2602.016845703125 11 | ], 12 | "size": [ 13 | 380.4000244140625, 14 | 78 15 | ], 16 | "flags": {}, 17 | "order": 12, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "clip_vision", 22 | "type": "CLIP_VISION", 23 | "link": 186, 24 | "label": "clip_vision" 25 | }, 26 | { 27 | "name": "image", 28 | "type": "IMAGE", 29 | "link": 187, 30 | "label": "image" 31 | } 32 | ], 33 | "outputs": [ 34 | { 35 | "name": "CLIP_VISION_OUTPUT", 36 | "type": "CLIP_VISION_OUTPUT", 37 | "links": [ 38 | 196 39 | ], 40 | "label": "CLIP_VISION_OUTPUT" 41 | } 42 | ], 43 | "properties": { 44 | "Node name for S&R": "CLIPVisionEncode", 45 | "ttNbgOverride": { 46 | "color": "#af3535", 47 | "groupcolor": "#A88" 48 | } 49 | }, 50 | "widgets_values": [ 51 | "none" 52 | ], 53 | "color": "#af3535" 54 | }, 55 | { 56 | "id": 104, 57 | "type": "CLIPTextEncode", 58 | "pos": [ 59 | -549.6141357421875, 60 | -2235.318359375 61 | ], 62 | "size": [ 63 | 422.84503173828125, 64 | 164.31304931640625 65 | ], 66 | "flags": {}, 67 | "order": 11, 68 | "mode": 0, 69 | "inputs": [ 70 | { 71 | "name": "clip", 72 | "type": "CLIP", 73 | "link": 190, 74 | "label": "clip" 75 | }, 76 | { 77 | "name": "text", 78 | "type": "STRING", 79 | "link": 191, 80 | "widget": { 81 | "name": "text" 82 | }, 83 | "label": "text" 84 | } 85 | ], 86 | "outputs": [ 87 | { 88 | "name": "CONDITIONING", 89 | "type": "CONDITIONING", 90 | "links": [ 91 | 193, 92 | 202 93 | ], 94 | "slot_index": 0, 95 | "label": "CONDITIONING" 96 | } 97 | ], 98 | "properties": { 99 | "Node name for S&R": "CLIPTextEncode" 100 | }, 101 | "widgets_values": [ 102 | "1 girl,", 103 | [ 104 | false, 105 | true 106 | ] 107 | ], 108 | "color": "#232", 109 | "bgcolor": "#353" 110 | }, 111 | { 112 | "id": 115, 113 | "type": "InstructPixToPixConditioning", 114 | "pos": [ 115 | 860.5709838867188, 116 | -2595.61376953125 117 | ], 118 | "size": [ 119 | 235.1999969482422, 120 | 86 121 | ], 122 | "flags": {}, 123 | "order": 21, 124 | "mode": 0, 125 | "inputs": [ 126 | { 127 | "name": "positive", 128 | "type": "CONDITIONING", 129 | "link": 203, 130 | "label": "positive" 131 | }, 132 | { 133 | "name": "negative", 134 | "type": "CONDITIONING", 135 | "link": 204, 136 | "label": "negative" 137 | }, 138 | { 139 | "name": "vae", 140 | "type": "VAE", 141 | "link": 205, 142 | "label": "vae" 143 | }, 144 | { 145 | "name": "pixels", 146 | "type": "IMAGE", 147 | "link": 414, 148 | "label": "pixels" 149 | } 150 | ], 151 | "outputs": [ 152 | { 153 | "name": "positive", 154 | "type": "CONDITIONING", 155 | "links": [ 156 | 209 157 | ], 158 | "slot_index": 0, 159 | "label": "positive" 160 | }, 161 | { 162 | "name": "negative", 163 | "type": "CONDITIONING", 164 | "links": [ 165 | 210 166 | ], 167 | "slot_index": 1, 168 | "label": "negative" 169 | }, 170 | { 171 | "name": "latent", 172 | "type": "LATENT", 173 | "links": [ 174 | 211 175 | ], 176 | "slot_index": 2, 177 | "label": "latent" 178 | } 179 | ], 180 | "properties": { 181 | "Node name for S&R": "InstructPixToPixConditioning", 182 | "ttNbgOverride": { 183 | "color": "#af3535", 184 | "groupcolor": "#A88" 185 | } 186 | }, 187 | "widgets_values": [], 188 | "color": "#af3535" 189 | }, 190 | { 191 | "id": 119, 192 | "type": "VAEDecode", 193 | "pos": [ 194 | 1510.5672607421875, 195 | -2602.656005859375 196 | ], 197 | "size": [ 198 | 210, 199 | 46 200 | ], 201 | "flags": {}, 202 | "order": 23, 203 | "mode": 0, 204 | "inputs": [ 205 | { 206 | "name": "samples", 207 | "type": "LATENT", 208 | "link": 212, 209 | "label": "samples" 210 | }, 211 | { 212 | "name": "vae", 213 | "type": "VAE", 214 | "link": 213, 215 | "label": "vae" 216 | } 217 | ], 218 | "outputs": [ 219 | { 220 | "name": "IMAGE", 221 | "type": "IMAGE", 222 | "links": [ 223 | 214 224 | ], 225 | "slot_index": 0, 226 | "label": "IMAGE" 227 | } 228 | ], 229 | "properties": { 230 | "Node name for S&R": "VAEDecode", 231 | "ttNbgOverride": { 232 | "color": "#af3535", 233 | "groupcolor": "#A88" 234 | } 235 | }, 236 | "widgets_values": [], 237 | "color": "#af3535" 238 | }, 239 | { 240 | "id": 116, 241 | "type": "VAELoader", 242 | "pos": [ 243 | -991.9190673828125, 244 | -2504.16162109375 245 | ], 246 | "size": [ 247 | 315, 248 | 58 249 | ], 250 | "flags": {}, 251 | "order": 0, 252 | "mode": 0, 253 | "inputs": [], 254 | "outputs": [ 255 | { 256 | "name": "VAE", 257 | "type": "VAE", 258 | "links": [ 259 | 205, 260 | 213 261 | ], 262 | "slot_index": 0, 263 | "label": "VAE" 264 | } 265 | ], 266 | "properties": { 267 | "Node name for S&R": "VAELoader", 268 | "ttNbgOverride": { 269 | "color": "#af3535", 270 | "groupcolor": "#A88" 271 | } 272 | }, 273 | "widgets_values": [ 274 | "ae.sft" 275 | ], 276 | "color": "#af3535" 277 | }, 278 | { 279 | "id": 100, 280 | "type": "CLIPVisionLoader", 281 | "pos": [ 282 | -970.6244506835938, 283 | -2593.681884765625 284 | ], 285 | "size": [ 286 | 315, 287 | 58 288 | ], 289 | "flags": {}, 290 | "order": 1, 291 | "mode": 0, 292 | "inputs": [], 293 | "outputs": [ 294 | { 295 | "name": "CLIP_VISION", 296 | "type": "CLIP_VISION", 297 | "links": [ 298 | 186, 299 | 188 300 | ], 301 | "slot_index": 0, 302 | "label": "CLIP_VISION" 303 | } 304 | ], 305 | "properties": { 306 | "Node name for S&R": "CLIPVisionLoader", 307 | "ttNbgOverride": { 308 | "color": "#af3535", 309 | "groupcolor": "#A88" 310 | } 311 | }, 312 | "widgets_values": [ 313 | "sigclip_vision_patch14_384.safetensors" 314 | ], 315 | "color": "#af3535" 316 | }, 317 | { 318 | "id": 103, 319 | "type": "UnetLoaderGGUF", 320 | "pos": [ 321 | -1002.0895385742188, 322 | -2398.105712890625 323 | ], 324 | "size": [ 325 | 315, 326 | 58 327 | ], 328 | "flags": {}, 329 | "order": 2, 330 | "mode": 0, 331 | "inputs": [], 332 | "outputs": [ 333 | { 334 | "name": "MODEL", 335 | "type": "MODEL", 336 | "links": [ 337 | 412 338 | ], 339 | "slot_index": 0, 340 | "label": "MODEL" 341 | } 342 | ], 343 | "properties": { 344 | "Node name for S&R": "UnetLoaderGGUF", 345 | "ttNbgOverride": { 346 | "color": "#af3535", 347 | "groupcolor": "#A88" 348 | } 349 | }, 350 | "widgets_values": [ 351 | "flux1-depth-dev-fp16-Q8_0-GGUF.gguf" 352 | ], 353 | "color": "#af3535" 354 | }, 355 | { 356 | "id": 105, 357 | "type": "DualCLIPLoader", 358 | "pos": [ 359 | -988.5557861328125, 360 | -2141.2802734375 361 | ], 362 | "size": [ 363 | 315, 364 | 106 365 | ], 366 | "flags": {}, 367 | "order": 3, 368 | "mode": 0, 369 | "inputs": [], 370 | "outputs": [ 371 | { 372 | "name": "CLIP", 373 | "type": "CLIP", 374 | "links": [ 375 | 190 376 | ], 377 | "label": "CLIP" 378 | } 379 | ], 380 | "properties": { 381 | "Node name for S&R": "DualCLIPLoader", 382 | "ttNbgOverride": { 383 | "color": "#af3535", 384 | "groupcolor": "#A88" 385 | } 386 | }, 387 | "widgets_values": [ 388 | "clip_l.safetensors", 389 | "t5xxl_fp8_e4m3fn.safetensors", 390 | "flux" 391 | ], 392 | "color": "#af3535" 393 | }, 394 | { 395 | "id": 107, 396 | "type": "StyleModelLoader", 397 | "pos": [ 398 | -973.1683959960938, 399 | -1704.2919921875 400 | ], 401 | "size": [ 402 | 315, 403 | 58 404 | ], 405 | "flags": {}, 406 | "order": 4, 407 | "mode": 0, 408 | "inputs": [], 409 | "outputs": [ 410 | { 411 | "name": "STYLE_MODEL", 412 | "type": "STYLE_MODEL", 413 | "links": [ 414 | 195, 415 | 198 416 | ], 417 | "slot_index": 0, 418 | "label": "STYLE_MODEL" 419 | } 420 | ], 421 | "properties": { 422 | "Node name for S&R": "StyleModelLoader", 423 | "ttNbgOverride": { 424 | "color": "#af3535", 425 | "groupcolor": "#A88" 426 | } 427 | }, 428 | "widgets_values": [ 429 | "flux1-redux-dev.safetensors" 430 | ], 431 | "color": "#af3535" 432 | }, 433 | { 434 | "id": 112, 435 | "type": "LayerUtility: ImageScaleByAspectRatio V2", 436 | "pos": [ 437 | -1021.8609619140625, 438 | -1604.9833984375 439 | ], 440 | "size": [ 441 | 336, 442 | 330 443 | ], 444 | "flags": {}, 445 | "order": 14, 446 | "mode": 0, 447 | "inputs": [ 448 | { 449 | "name": "image", 450 | "type": "IMAGE", 451 | "link": 200, 452 | "shape": 7, 453 | "label": "image" 454 | }, 455 | { 456 | "name": "mask", 457 | "type": "MASK", 458 | "link": null, 459 | "shape": 7, 460 | "label": "mask" 461 | } 462 | ], 463 | "outputs": [ 464 | { 465 | "name": "image", 466 | "type": "IMAGE", 467 | "links": [ 468 | 413 469 | ], 470 | "slot_index": 0, 471 | "label": "image" 472 | }, 473 | { 474 | "name": "mask", 475 | "type": "MASK", 476 | "links": null, 477 | "label": "mask" 478 | }, 479 | { 480 | "name": "original_size", 481 | "type": "BOX", 482 | "links": null, 483 | "label": "original_size" 484 | }, 485 | { 486 | "name": "width", 487 | "type": "INT", 488 | "links": null, 489 | "label": "width" 490 | }, 491 | { 492 | "name": "height", 493 | "type": "INT", 494 | "links": null, 495 | "label": "height" 496 | } 497 | ], 498 | "properties": { 499 | "Node name for S&R": "LayerUtility: ImageScaleByAspectRatio V2" 500 | }, 501 | "widgets_values": [ 502 | "original", 503 | 1, 504 | 1, 505 | "letterbox", 506 | "lanczos", 507 | "8", 508 | "longest", 509 | 1024, 510 | "#000000" 511 | ], 512 | "color": "rgba(38, 73, 116, 0.7)" 513 | }, 514 | { 515 | "id": 109, 516 | "type": "StyleModelAdvancedApply", 517 | "pos": [ 518 | -67.29872131347656, 519 | -2600.173583984375 520 | ], 521 | "size": [ 522 | 393, 523 | 242 524 | ], 525 | "flags": {}, 526 | "order": 18, 527 | "mode": 0, 528 | "inputs": [ 529 | { 530 | "name": "conditioning", 531 | "type": "CONDITIONING", 532 | "link": 194, 533 | "label": "conditioning" 534 | }, 535 | { 536 | "name": "style_model", 537 | "type": "STYLE_MODEL", 538 | "link": 195, 539 | "label": "style_model" 540 | }, 541 | { 542 | "name": "clip_vision_output", 543 | "type": "CLIP_VISION_OUTPUT", 544 | "link": 196, 545 | "label": "clip_vision_output" 546 | } 547 | ], 548 | "outputs": [ 549 | { 550 | "name": "CONDITIONING", 551 | "type": "CONDITIONING", 552 | "links": [ 553 | 197 554 | ], 555 | "slot_index": 0, 556 | "label": "CONDITIONING" 557 | } 558 | ], 559 | "properties": { 560 | "Node name for S&R": "StyleModelAdvancedApply", 561 | "ttNbgOverride": { 562 | "color": "#af3535", 563 | "groupcolor": "#A88" 564 | } 565 | }, 566 | "widgets_values": [ 567 | 1, 568 | 0.7000000000000001, 569 | 1, 570 | 0.7000000000000001, 571 | 0.8, 572 | 0.7000000000000001, 573 | 1.5 574 | ], 575 | "color": "#af3535" 576 | }, 577 | { 578 | "id": 99, 579 | "type": "LayerUtility: TextBox", 580 | "pos": [ 581 | -995.66650390625, 582 | -1975.4219970703125 583 | ], 584 | "size": [ 585 | 400, 586 | 200 587 | ], 588 | "flags": {}, 589 | "order": 5, 590 | "mode": 0, 591 | "inputs": [], 592 | "outputs": [ 593 | { 594 | "name": "text", 595 | "type": "STRING", 596 | "links": [ 597 | 191 598 | ], 599 | "slot_index": 0, 600 | "label": "text" 601 | } 602 | ], 603 | "properties": { 604 | "Node name for S&R": "LayerUtility: TextBox" 605 | }, 606 | "widgets_values": [ 607 | "((((a car))))", 608 | [ 609 | false, 610 | true 611 | ] 612 | ], 613 | "color": "rgba(38, 73, 116, 0.7)" 614 | }, 615 | { 616 | "id": 122, 617 | "type": "SaveImage", 618 | "pos": [ 619 | 593.1557006835938, 620 | -1934.9884033203125 621 | ], 622 | "size": [ 623 | 993.8319702148438, 624 | 1057.352783203125 625 | ], 626 | "flags": {}, 627 | "order": 24, 628 | "mode": 0, 629 | "inputs": [ 630 | { 631 | "name": "images", 632 | "type": "IMAGE", 633 | "link": 214, 634 | "label": "images" 635 | } 636 | ], 637 | "outputs": [], 638 | "properties": { 639 | "Node name for S&R": "SaveImage", 640 | "ttNbgOverride": { 641 | "color": "#af3535", 642 | "groupcolor": "#A88" 643 | } 644 | }, 645 | "widgets_values": [ 646 | "ComfyUI" 647 | ], 648 | "color": "#af3535" 649 | }, 650 | { 651 | "id": 110, 652 | "type": "StyleModelAdvancedApply", 653 | "pos": [ 654 | 414.768798828125, 655 | -2600.00146484375 656 | ], 657 | "size": [ 658 | 393, 659 | 242 660 | ], 661 | "flags": {}, 662 | "order": 20, 663 | "mode": 0, 664 | "inputs": [ 665 | { 666 | "name": "conditioning", 667 | "type": "CONDITIONING", 668 | "link": 197, 669 | "label": "conditioning" 670 | }, 671 | { 672 | "name": "style_model", 673 | "type": "STYLE_MODEL", 674 | "link": 198, 675 | "label": "style_model" 676 | }, 677 | { 678 | "name": "clip_vision_output", 679 | "type": "CLIP_VISION_OUTPUT", 680 | "link": 199, 681 | "label": "clip_vision_output" 682 | } 683 | ], 684 | "outputs": [ 685 | { 686 | "name": "CONDITIONING", 687 | "type": "CONDITIONING", 688 | "links": [ 689 | 203 690 | ], 691 | "slot_index": 0, 692 | "label": "CONDITIONING" 693 | } 694 | ], 695 | "properties": { 696 | "Node name for S&R": "StyleModelAdvancedApply", 697 | "ttNbgOverride": { 698 | "color": "#af3535", 699 | "groupcolor": "#A88" 700 | } 701 | }, 702 | "widgets_values": [ 703 | 0.3, 704 | 0.2, 705 | 0.5, 706 | 0.11000000000000001, 707 | 0.3, 708 | 0.3, 709 | 1 710 | ], 711 | "color": "#af3535" 712 | }, 713 | { 714 | "id": 118, 715 | "type": "KSampler", 716 | "pos": [ 717 | 1191.5052490234375, 718 | -2537.238525390625 719 | ], 720 | "size": [ 721 | 301.69000244140625, 722 | 474 723 | ], 724 | "flags": {}, 725 | "order": 22, 726 | "mode": 0, 727 | "inputs": [ 728 | { 729 | "name": "model", 730 | "type": "MODEL", 731 | "link": 412, 732 | "label": "model" 733 | }, 734 | { 735 | 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-------------------------------------------------------------------------------- /nodes.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import comfy.ops 3 | from comfy.ldm.flux.redux import ReduxImageEncoder 4 | import math 5 | 6 | # 获取ops引用 7 | ops = comfy.ops.manual_cast 8 | 9 | class StyleModelAdvancedApply: 10 | @classmethod 11 | def INPUT_TYPES(s): 12 | return {"required": { 13 | "conditioning": ("CONDITIONING",), 14 | "style_model": ("STYLE_MODEL",), 15 | "clip_vision_output": ("CLIP_VISION_OUTPUT",), 16 | "style_weight": ("FLOAT", { 17 | "default": 1.0, 18 | "min": 0.0, 19 | "max": 10.0, 20 | "step": 0.01, 21 | "tooltip": "控制整体艺术风格的权重" 22 | }), 23 | "color_weight": ("FLOAT", { 24 | "default": 1.0, 25 | "min": 0.0, 26 | "max": 10.0, 27 | "step": 0.01, 28 | "tooltip": "控制颜色特征的权重" 29 | }), 30 | "content_weight": ("FLOAT", { 31 | "default": 1.0, 32 | "min": 0.0, 33 | "max": 10.0, 34 | "step": 0.01, 35 | "tooltip": "控制内容语义的权重" 36 | }), 37 | "structure_weight": ("FLOAT", { 38 | "default": 1.0, 39 | "min": 0.0, 40 | "max": 10.0, 41 | "step": 0.01, 42 | "tooltip": "控制结构布局的权重" 43 | }), 44 | "texture_weight": ("FLOAT", { 45 | "default": 1.0, 46 | "min": 0.0, 47 | "max": 10.0, 48 | "step": 0.01, 49 | "tooltip": "控制纹理细节的权重" 50 | }), 51 | "similarity_threshold": ("FLOAT", { 52 | "default": 0.7, 53 | "min": 0.0, 54 | "max": 1.0, 55 | "step": 0.01, 56 | "tooltip": "特征相似度阈值，超过此值的区域将被替换" 57 | }), 58 | "enhancement_base": ("FLOAT", { 59 | "default": 1.5, 60 | "min": 1.0, 61 | "max": 3.0, 62 | "step": 0.1, 63 | "tooltip": "文本特征替换的基础增强系数" 64 | }) 65 | }} 66 | 67 | RETURN_TYPES = ("CONDITIONING",) 68 | FUNCTION = "apply_style" 69 | CATEGORY = "conditioning/style_model" 70 | 71 | def __init__(self): 72 | self.text_projector = ops.Linear(4096, 4096) # 保持维度一致 73 | # 为不同类型特征设置增强系数 74 | self.enhancement_factors = { 75 | 'style': 1.2, # 风格特征增强系数 76 | 'color': 1.0, # 颜色特征增强系数 77 | 'content': 1.1, # 内容特征增强系数 78 | 'structure': 1.3, # 结构特征增强系数 79 | 'texture': 1.0 # 纹理特征增强系数 80 | } 81 | 82 | def compute_similarity(self, text_feat, image_feat): 83 | """计算多种相似度的组合""" 84 | # 1. 余弦相似度 85 | cos_sim = torch.cosine_similarity(text_feat, image_feat, dim=-1) 86 | 87 | # 2. L2距离相似度（归一化后的欧氏距离） 88 | l2_dist = torch.norm(text_feat - image_feat, p=2, dim=-1) 89 | l2_sim = 1 / (1 + l2_dist) # 转换为相似度 90 | 91 | # 3. 点积相似度（考虑特征的强度） 92 | dot_sim = torch.sum(text_feat * image_feat, dim=-1) 93 | dot_sim = torch.tanh(dot_sim) # 归一化到[-1,1] 94 | 95 | # 4. 注意力相似度 96 | attn_weights = torch.softmax(torch.matmul(text_feat, image_feat.transpose(-2, -1)) / math.sqrt(text_feat.size(-1)), dim=-1) 97 | attn_sim = torch.mean(attn_weights, dim=-1) 98 | 99 | # 组合所有相似度（可以调整权重） 100 | combined_sim = ( 101 | 0.4 * cos_sim + 102 | 0.2 * l2_sim + 103 | 0.2 * dot_sim + 104 | 0.2 * attn_sim 105 | ) 106 | 107 | return combined_sim.mean() 108 | 109 | def apply_style(self, clip_vision_output, style_model, conditioning, 110 | style_weight=1.0, color_weight=1.0, content_weight=1.0, 111 | structure_weight=1.0, texture_weight=1.0, 112 | similarity_threshold=0.7, enhancement_base=1.5): 113 | 114 | # 获取图像特征并展平 115 | image_cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1) 116 | 117 | # 获取文本特征并调整维度 118 | text_features = conditioning[0][0] # [batch_size, seq_len, 4096] 119 | text_features = text_features.mean(dim=1) # [batch_size, 4096] 120 | 121 | # 投影文本特征（保持4096维度） 122 | text_features = self.text_projector(text_features) # [batch_size, 4096] 123 | 124 | # 确保batch维度匹配 125 | if text_features.shape[0] != image_cond.shape[0]: 126 | text_features = text_features.expand(image_cond.shape[0], -1) 127 | 128 | # 分解图像特征为5个区域 129 | feature_size = image_cond.shape[-1] # 4096 130 | splits = feature_size // 5 # 每部分约819维 131 | 132 | # 分离图像的不同类型特征 133 | image_features = { 134 | 'style': image_cond[..., :splits], 135 | 'color': image_cond[..., splits:splits*2], 136 | 'content': image_cond[..., splits*2:splits*3], 137 | 'structure': image_cond[..., splits*3:splits*4], 138 | 'texture': image_cond[..., splits*4:] 139 | } 140 | 141 | # 计算每个区域与文本特征的相似度 142 | similarities = {} 143 | for key, region_features in image_features.items(): 144 | # 将文本特征调整为对应区域的维度 145 | region_text_features = text_features[..., :region_features.shape[-1]] 146 | # 使用多种相似度度量的组合 147 | similarities[key] = self.compute_similarity(region_text_features, region_features) 148 | 149 | # 根据相似度和阈值决定替换，并应用增强 150 | final_features = {} 151 | weights = { 152 | 'style': style_weight, 153 | 'color': color_weight, 154 | 'content': content_weight, 155 | 'structure': structure_weight, 156 | 'texture': texture_weight 157 | } 158 | 159 | for key in image_features: 160 | if similarities[key] > similarity_threshold: 161 | # 相似度高的区域，用增强后的文本特征替换 162 | region_size = image_features[key].shape[-1] 163 | # 计算动态增强系数 164 | dynamic_factor = enhancement_base * self.enhancement_factors[key] 165 | # 应用特征替换和增强 166 | final_features[key] = text_features[..., :region_size] * weights[key] * dynamic_factor 167 | else: 168 | # 保持原图像特征 169 | final_features[key] = image_features[key] * weights[key] 170 | 171 | # 合并所有特征 172 | combined_cond = torch.cat([ 173 | final_features['style'], 174 | final_features['color'], 175 | final_features['content'], 176 | final_features['structure'], 177 | final_features['texture'] 178 | ], dim=-1).unsqueeze(dim=0) 179 | 180 | # 构建新的条件 181 | c = [] 182 | for t in conditioning: 183 | n = [torch.cat((t[0], combined_cond), dim=1), t[1].copy()] 184 | c.append(n) 185 | 186 | return (c,) 187 | 188 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "flux_style_adjust" 3 | description = "StyleModelApply adds more controls" 4 | version = "1.0.0" 5 | license = {file = "LICENSE"} 6 | 7 | [project.urls] 8 | Repository = "https://github.com/yichengup/Comfyui_Flux_Style_Adjust" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "" 13 | DisplayName = "Comfyui_Flux_Style_Adjust" 14 | Icon = "" 15 | --------------------------------------------------------------------------------