工作流 /
图像 /
RIP SEEDVR2🪦. LC Relay VSR Upscale Model. (works for low VRAM) | Relay VSR 视频超分放大模型
RIP SEEDVR2🪦. LC Relay VSR Upscale Model. (works for low VRAM) | Relay VSR 视频超分放大模型
RIP SEEDVR2🪦. LC Relay VSR Upscale Model. (works for low VRAM) | Relay VSR 视频超分放大模型
Comfy Manager is SLOW to upload. sometimes a day or so. If you get ANY missing nodes, just get them from my repo Here: https://github.com/lonecatone23All LC Notes now translate to the following:English, 中文, 繁體中文, Русский…
图像
39 个节点 372 次使用 lonecatone23 118 KB
需要的权重
这个工作流引用了 1 个权重文件，其中 0 个能在本站模型库里找到。
Relay Upscale Workflow V3.0.2.json
模型库暂未收录
节点构成
GetNode
× 7
SetNode
× 6
Fast Groups Bypasser (rgthree)
× 3
LCEasyFolder
× 2
SaveImage
× 2
LCLabel
× 2
LCRelayVSRUpscale
× 2
LCNote
× 2
LCLoadVideo
× 1
LoadImage
× 1
LCSeed
× 1
LCBatchImageComparer
× 1
LCTimer
× 1
LCTextOverlay
× 1
PrimitiveString
× 1
LCJoinStrings
× 1
工作流 JSON
和上面「下载工作流 JSON」是同一份文件。
1{
2 "id": "d66dedb4-ed78-4b75-b7bf-4814fd4ab600",
3 "revision": 0,
4 "last_node_id": 73,
5 "last_link_id": 101,
6 "nodes": [
7 {
8 "id": 6,
9 "type": "GetNode",
10 "pos": [
11 2150,
12 -2250
13 ],
14 "size": [
15 210,
16 40
17 ],
18 "flags": {
19 "collapsed": true
20 },
21 "order": 0,
22 "mode": 0,
23 "inputs": [],
24 "outputs": [
25 {
26 "name": "INT",
27 "type": "INT",
28 "links": [
29 73
30 ]
31 }
32 ],
33 "title": "Get_Seed",
34 "properties": {
35 "Node name for S&R": "GetNode",
36 "aux_id": "kijai/ComfyUI-KJNodes",
37 "ue_properties": {
38 "widget_ue_connectable": [],
39 "version": "7.8",
40 "input_ue_unconnectable": []
41 }
42 },
43 "widgets_values": [
44 "Seed"
45 ],
46 "widgets_values_named": {
47 "Constant": "Seed"
48 },
49 "color": "#1b4669",
50 "bgcolor": "#29699c"
51 },
52 {
53 "id": 11,
54 "type": "Fast Groups Bypasser (rgthree)",
55 "pos": [
56 2150,
57 -2350
58 ],
59 "size": [
60 220,
61 60
62 ],
63 "flags": [],
64 "order": 1,
65 "mode": 0,
66 "inputs": [],
67 "outputs": [
68 {
69 "label": "🤫",
70 "name": "OPT_CONNECTION",
71 "type": "*",
72 "links": []
73 }
74 ],
75 "title": "Relay Bypasser",
76 "properties": {
77 "matchColors": "",
78 "matchTitle": "#Relay Upscale Video",
79 "showNav": false,
80 "showAllGraphs": true,
81 "sort": "position",
82 "customSortAlphabet": "",
83 "toggleRestriction": "default",
84 "ue_properties": {
85 "widget_ue_connectable": [],
86 "input_ue_unconnectable": [],
87 "version": "7.8"
88 }
89 },
90 "color": "#c3782d",
91 "bgcolor": "#af6419"
92 },
93 {
94 "id": 12,
95 "type": "GetNode",
96 "pos": [
97 2150,
98 -2210
99 ],
100 "size": [
101 210,
102 50
103 ],
104 "flags": {
105 "collapsed": true
106 },
107 "order": 2,
108 "mode": 0,
109 "inputs": [],
110 "outputs": [
111 {
112 "name": "IMAGE",
113 "type": "IMAGE",
114 "links": [
115 72,
116 95
117 ]
118 }
119 ],
120 "title": "Get_Org Video",
121 "properties": {
122 "Node name for S&R": "GetNode",
123 "aux_id": "kijai/ComfyUI-KJNodes",
124 "ue_properties": {
125 "widget_ue_connectable": [],
126 "version": "7.8",
127 "input_ue_unconnectable": []
128 }
129 },
130 "widgets_values": [
131 "Org Video"
132 ],
133 "widgets_values_named": {
134 "Constant": "Org Video"
135 },
136 "color": "#2a363b",
137 "bgcolor": "#3f5159"
138 },
139 {
140 "id": 13,
141 "type": "LCLoadVideo",
142 "pos": [
143 1670,
144 -2400
145 ],
146 "size": [
147 270,
148 330
149 ],
150 "flags": [],
151 "order": 3,
152 "mode": 0,
153 "inputs": [],
154 "outputs": [
155 {
156 "name": "images",
157 "type": "IMAGE",
158 "links": [
159 55
160 ]
161 },
162 {
163 "name": "audio",
164 "type": "AUDIO",
165 "links": [
166 53
167 ]
168 },
169 {
170 "name": "fps",
171 "type": "FLOAT",
172 "links": [
173 54
174 ]
175 },
176 {
177 "name": "frame_count",
178 "type": "INT",
179 "links": null
180 }
181 ],
182 "properties": {
183 "cnr_id": "ComfyUI_LC_AV_nodes",
184 "ver": "23ab7e5f491f9d01fe37e024791bcd0bf3f0757b",
185 "Node name for S&R": "LCLoadVideo",
186 "ue_properties": {
187 "widget_ue_connectable": [],
188 "input_ue_unconnectable": [],
189 "version": "7.8"
190 }
191 },
192 "widgets_values": [
193 "\"H:\\ComfyUI\\output\\Videos\\Minimax_2026-09-17_00006.mp4\""
194 ],
195 "widgets_values_named": {
196 "path": "\"H:\\ComfyUI\\output\\Videos\\Minimax_2026-09-17_00006.mp4\""
197 },
198 "color": "#1a5a7a",
199 "bgcolor": "#1a5a7a"
200 },
201 {
202 "id": 15,
203 "type": "LoadImage",
204 "pos": [
205 1670,
206 -3110
207 ],
208 "size": [
209 350,
210 510
211 ],
212 "flags": [],
213 "order": 4,
214 "mode": 0,
215 "inputs": [],
216 "outputs": [
217 {
218 "name": "IMAGE",
219 "type": "IMAGE",
220 "links": [
221 52,
222 84
223 ]
224 },
225 {
226 "name": "MASK",
227 "type": "MASK",
228 "links": null
229 }
230 ],
231 "properties": {
232 "cnr_id": "comfy-core",
233 "ver": "0.44.0",
234 "Node name for S&R": "LoadImage",
235 "ue_properties": {
236 "widget_ue_connectable": [],
237 "input_ue_unconnectable": [],
238 "version": "7.8"
239 }
240 },
241 "widgets_values": [
242 "Lonecats Krea2 Pro_00040.png",
243 "image"
244 ],
245 "widgets_values_named": {
246 "image": "Lonecats Krea2 Pro_00040.png",
247 "upload": "image"
248 },
249 "color": "#233",
250 "bgcolor": "#355"
251 },
252 {
253 "id": 20,
254 "type": "LCSeed",
255 "pos": [
256 1200,
257 -2960
258 ],
259 "size": [
260 270,
261 130
262 ],
263 "flags": [],
264 "order": 5,
265 "mode": 0,
266 "inputs": [],
267 "outputs": [
268 {
269 "name": "seed",
270 "type": "INT",
271 "links": [
272 17
273 ]
274 }
275 ],
276 "properties": {
277 "cnr_id": "ComfyUI_LC123_nodes",
278 "ver": "e9d3f5fa5d7d6c0f603b2e19f8a8bf458ee3b8ec",
279 "Node name for S&R": "LCSeed",
280 "lc_seed_history": [
281 8675309,
282 8675310,
283 8675309
284 ],
285 "ue_properties": {
286 "widget_ue_connectable": [],
287 "input_ue_unconnectable": [],
288 "version": "7.8"
289 }
290 },
291 "widgets_values": [
292 8675309,
293 "",
294 "",
295 "— last used seeds —"
296 ],
297 "widgets_values_named": {
298 "base_seed": 8675309,
299 "🎲 Randomize Each Time": "",
300 "🎲 New Fixed Random": "",
301 "seed history": "— last used seeds —"
302 },
303 "color": "#822305",
304 "bgcolor": "#822305"
305 },
306 {
307 "id": 21,
308 "type": "SetNode",
309 "pos": [
310 1490,
311 -2960
312 ],
313 "size": [
314 210,
315 60
316 ],
317 "flags": {
318 "collapsed": true
319 },
320 "order": 27,
321 "mode": 0,
322 "inputs": [
323 {
324 "name": "INT",
325 "type": "INT",
326 "link": 17
327 }
328 ],
329 "outputs": [
330 {
331 "name": "INT",
332 "type": "INT",
333 "links": null
334 }
335 ],
336 "title": "Set_Seed",
337 "properties": {
338 "Node name for S&R": "SetNode",
339 "aux_id": "kijai/ComfyUI-KJNodes",
340 "previousName": "Seed",
341 "ue_properties": {
342 "widget_ue_connectable": [],
343 "version": "7.8",
344 "input_ue_unconnectable": []
345 }
346 },
347 "widgets_values": [
348 "Seed"
349 ],
350 "widgets_values_named": {
351 "Constant": "Seed"
352 },
353 "color": "#1b4669",
354 "bgcolor": "#29699c"
355 },
356 {
357 "id": 27,
358 "type": "SetNode",
359 "pos": [
360 2040,
361 -3110
362 ],
363 "size": [
364 210,
365 60
366 ],
367 "flags": {
368 "collapsed": true
369 },
370 "order": 26,
371 "mode": 0,
372 "inputs": [
373 {
374 "name": "IMAGE",
375 "type": "IMAGE",
376 "link": 52
377 }
378 ],
379 "outputs": [
380 {
381 "name": "IMAGE",
382 "type": "IMAGE",
383 "links": null
384 }
385 ],
386 "title": "Set_org image",
387 "properties": {
388 "Node name for S&R": "SetNode",
389 "aux_id": "kijai/ComfyUI-KJNodes",
390 "previousName": "org image",
391 "ue_properties": {
392 "widget_ue_connectable": [],
393 "version": "7.8",
394 "input_ue_unconnectable": []
395 }
396 },
397 "widgets_values": [
398 "org image"
399 ],
400 "widgets_values_named": {
401 "Constant": "org image"
402 },
403 "color": "#2a363b",
404 "bgcolor": "#3f5159"
405 },
406 {
407 "id": 28,
408 "type": "LCBatchImageComparer",
409 "pos": [
410 3080,
411 -3070
412 ],
413 "size": [
414 380,
415 540
416 ],
417 "flags": [],
418 "order": 36,
419 "mode": 0,
420 "inputs": [
421 {
422 "label": "Before",
423 "name": "image_a",
424 "shape": 7,
425 "type": "IMAGE",
426 "link": 34
427 },
428 {
429 "label": "After Relay",
430 "name": "image_b",
431 "shape": 7,
432 "type": "IMAGE",
433 "link": 71
434 }
435 ],
436 "outputs": [
437 {
438 "name": "images",
439 "type": "IMAGE",
440 "links": []
441 }
442 ],
443 "properties": {
444 "cnr_id": "ComfyUI_LC123_nodes",
445 "ver": "e9d3f5fa5d7d6c0f603b2e19f8a8bf458ee3b8ec",
446 "Node name for S&R": "LCBatchImageComparer",
447 "comparer_mode": "Slide",
448 "ue_properties": {
449 "widget_ue_connectable": [],
450 "input_ue_unconnectable": [],
451 "version": "7.8"
452 }
453 },
454 "color": "#324B4B",
455 "bgcolor": "#324B4B"
456 },
457 {
458 "id": 33,
459 "type": "GetNode",
460 "pos": [
461 2210,
462 -2970
463 ],
464 "size": [
465 210,
466 40
467 ],
468 "flags": {
469 "collapsed": true
470 },
471 "order": 6,
472 "mode": 0,
473 "inputs": [],
474 "outputs": [
475 {
476 "name": "INT",
477 "type": "INT",
478 "links": [
479 68
480 ]
481 }
482 ],
483 "title": "Get_Seed",
484 "properties": {
485 "Node name for S&R": "GetNode",
486 "aux_id": "kijai/ComfyUI-KJNodes",
487 "ue_properties": {
488 "widget_ue_connectable": [],
489 "version": "7.8",
490 "input_ue_unconnectable": []
491 }
492 },
493 "widgets_values": [
494 "Seed"
495 ],
496 "widgets_values_named": {
497 "Constant": "Seed"
498 },
499 "color": "#1b4669",
500 "bgcolor": "#29699c"
501 },
502 {
503 "id": 36,
504 "type": "SetNode",
505 "pos": [
506 2210,
507 -2890
508 ],
509 "size": [
510 210,
511 50
512 ],
513 "flags": {
514 "collapsed": true
515 },
516 "order": 35,
517 "mode": 0,
518 "inputs": [
519 {
520 "name": "IMAGE",
521 "type": "IMAGE",
522 "link": 70
523 }
524 ],
525 "outputs": [
526 {
527 "name": "IMAGE",
528 "type": "IMAGE",
529 "links": []
530 }
531 ],
532 "title": "Set_Relay Image",
533 "properties": {
534 "Node name for S&R": "SetNode",
535 "aux_id": "kijai/ComfyUI-KJNodes",
536 "previousName": "Relay Image",
537 "ue_properties": {
538 "widget_ue_connectable": [],
539 "version": "7.8",
540 "input_ue_unconnectable": []
541 }
542 },
543 "widgets_values": [
544 "Relay Image"
545 ],
546 "widgets_values_named": {
547 "Constant": "Relay Image"
548 },
549 "color": "#2a363b",
550 "bgcolor": "#3f5159"
551 },
552 {
553 "id": 38,
554 "type": "Fast Groups Bypasser (rgthree)",
555 "pos": [
556 2210,
557 -3070
558 ],
559 "size": [
560 220,
561 60
562 ],
563 "flags": [],
564 "order": 7,
565 "mode": 0,
566 "inputs": [],
567 "outputs": [
568 {
569 "label": "🤫",
570 "name": "OPT_CONNECTION",
571 "type": "*",
572 "links": []
573 }
574 ],
575 "title": "Relay Bypasser",
576 "properties": {
577 "matchColors": "",
578 "matchTitle": "#Relay Upscale Image",
579 "showNav": false,
580 "showAllGraphs": true,
581 "sort": "position",
582 "customSortAlphabet": "",
583 "toggleRestriction": "default",
584 "ue_properties": {
585 "widget_ue_connectable": [],
586 "input_ue_unconnectable": [],
587 "version": "7.8"
588 }
589 },
590 "color": "#c3782d",
591 "bgcolor": "#af6419"
592 },
593 {
594 "id": 39,
595 "type": "GetNode",
596 "pos": [
597 2210,
598 -2930
599 ],
600 "size": [
601 210,
602 50
603 ],
604 "flags": {
605 "collapsed": true
606 },
607 "order": 8,
608 "mode": 0,
609 "inputs": [],
610 "outputs": [
611 {
612 "name": "IMAGE",
613 "type": "IMAGE",
614 "links": [
615 34,
616 67,
617 99
618 ]
619 }
620 ],
621 "title": "Get_org image",
622 "properties": {
623 "Node name for S&R": "GetNode",
624 "aux_id": "kijai/ComfyUI-KJNodes",
625 "ue_properties": {
626 "widget_ue_connectable": [],
627 "version": "7.8",
628 "input_ue_unconnectable": []
629 }
630 },
631 "widgets_values": [
632 "org image"
633 ],
634 "widgets_values_named": {
635 "Constant": "org image"
636 },
637 "color": "#2a363b",
638 "bgcolor": "#3f5159"
639 },
640 {
641 "id": 40,
642 "type": "SetNode",
643 "pos": [
644 1960,
645 -2350
646 ],
647 "size": [
648 210,
649 60
650 ],
651 "flags": {
652 "collapsed": true
653 },
654 "order": 24,
655 "mode": 0,
656 "inputs": [
657 {
658 "name": "AUDIO",
659 "type": "AUDIO",
660 "link": 53
661 }
662 ],
663 "outputs": [
664 {
665 "name": "AUDIO",
666 "type": "AUDIO",
667 "links": null
668 }
669 ],
670 "title": "Set_Audio",
671 "properties": {
672 "Node name for S&R": "SetNode",
673 "aux_id": "kijai/ComfyUI-KJNodes",
674 "previousName": "Audio",
675 "ue_properties": {
676 "widget_ue_connectable": [],
677 "version": "7.8",
678 "input_ue_unconnectable": []
679 }
680 },
681 "widgets_values": [
682 "Audio"
683 ],
684 "widgets_values_named": {
685 "Constant": "Audio"
686 }
687 },
688 {
689 "id": 41,
690 "type": "SetNode",
691 "pos": [
692 1970,
693 -2310
694 ],
695 "size": [
696 210,
697 60
698 ],
699 "flags": {
700 "collapsed": true
701 },
702 "order": 25,
703 "mode": 0,
704 "inputs": [
705 {
706 "name": "FLOAT",
707 "type": "FLOAT",
708 "link": 54
709 }
710 ],
711 "outputs": [
712 {
713 "name": "FLOAT",
714 "type": "FLOAT",
715 "links": []
716 }
717 ],
718 "title": "Set_FPS",
719 "properties": {
720 "Node name for S&R": "SetNode",
721 "aux_id": "kijai/ComfyUI-KJNodes",
722 "previousName": "FPS",
723 "ue_properties": {
724 "widget_ue_connectable": [],
725 "version": "7.8",
726 "input_ue_unconnectable": []
727 }
728 },
729 "widgets_values": [
730 "FPS"
731 ],
732 "widgets_values_named": {
733 "Constant": "FPS"
734 },
735 "color": "#232",
736 "bgcolor": "#353"
737 },
738 {
739 "id": 42,
740 "type": "SetNode",
741 "pos": [
742 1970,
743 -2390
744 ],
745 "size": [
746 210,
747 60
748 ],
749 "flags": {
750 "collapsed": true
751 },
752 "order": 23,
753 "mode": 0,
754 "inputs": [
755 {
756 "name": "IMAGE",
757 "type": "IMAGE",
758 "link": 55
759 }
760 ],
761 "outputs": [
762 {
763 "name": "IMAGE",
764 "type": "IMAGE",
765 "links": null
766 }
767 ],
768 "title": "Set_Org Video",
769 "properties": {
770 "Node name for S&R": "SetNode",
771 "aux_id": "kijai/ComfyUI-KJNodes",
772 "previousName": "Org Video",
773 "ue_properties": {
774 "widget_ue_connectable": [],
775 "version": "7.8",
776 "input_ue_unconnectable": []
777 }
778 },
779 "widgets_values": [
780 "Org Video"
781 ],
782 "widgets_values_named": {
783 "Constant": "Org Video"
784 },
785 "color": "#2a363b",
786 "bgcolor": "#3f5159"
787 },
788 {
789 "id": 43,
790 "type": "GetNode",
791 "pos": [
792 2150,
793 -2170
794 ],
795 "size": [
796 210,
797 60
798 ],
799 "flags": {
800 "collapsed": true
801 },
802 "order": 9,
803 "mode": 0,
804 "inputs": [],
805 "outputs": [
806 {
807 "name": "AUDIO",
808 "type": "AUDIO",
809 "links": [
810 96
811 ]
812 }
813 ],
814 "title": "Get_Audio",
815 "properties": {
816 "Node name for S&R": "GetNode",
817 "aux_id": "kijai/ComfyUI-KJNodes",
818 "ue_properties": {
819 "widget_ue_connectable": [],
820 "version": "7.8",
821 "input_ue_unconnectable": []
822 }
823 },
824 "widgets_values": [
825 "Audio"
826 ],
827 "widgets_values_named": {
828 "Constant": "Audio"
829 },
830 "color": "#322",
831 "bgcolor": "#533"
832 },
833 {
834 "id": 44,
835 "type": "GetNode",
836 "pos": [
837 2150,
838 -2130
839 ],
840 "size": [
841 210,
842 60
843 ],
844 "flags": {
845 "collapsed": true
846 },
847 "order": 10,
848 "mode": 0,
849 "inputs": [],
850 "outputs": [
851 {
852 "name": "FLOAT",
853 "type": "FLOAT",
854 "links": [
855 97
856 ]
857 }
858 ],
859 "title": "Get_FPS",
860 "properties": {
861 "Node name for S&R": "GetNode",
862 "aux_id": "kijai/ComfyUI-KJNodes",
863 "ue_properties": {
864 "widget_ue_connectable": [],
865 "version": "7.8",
866 "input_ue_unconnectable": []
867 }
868 },
869 "widgets_values": [
870 "FPS"
871 ],
872 "widgets_values_named": {
873 "Constant": "FPS"
874 },
875 "color": "#232",
876 "bgcolor": "#353"
877 },
878 {
879 "id": 45,
880 "type": "GetNode",
881 "pos": [
882 3480,
883 -3100
884 ],
885 "size": [
886 210,
887 60
888 ],
889 "flags": {
890 "collapsed": true
891 },
892 "order": 11,
893 "mode": 0,
894 "inputs": [],
895 "outputs": [
896 {
897 "name": "IMAGE",
898 "type": "IMAGE",
899 "links": [
900 59
901 ]
902 }
903 ],
904 "title": "Get_Relay Image",
905 "properties": {
906 "Node name for S&R": "GetNode",
907 "aux_id": "kijai/ComfyUI-KJNodes",
908 "ue_properties": {
909 "widget_ue_connectable": [],
910 "version": "7.8",
911 "input_ue_unconnectable": []
912 }
913 },
914 "widgets_values": [
915 "Relay Image"
916 ],
917 "widgets_values_named": {
918 "Constant": "Relay Image"
919 },
920 "color": "#2a363b",
921 "bgcolor": "#3f5159"
922 },
923 {
924 "id": 46,
925 "type": "LCEasyFolder",
926 "pos": [
927 2730,
928 -2350
929 ],
930 "size": [
931 300,
932 160
933 ],
934 "flags": [],
935 "order": 12,
936 "mode": 0,
937 "inputs": [],
938 "outputs": [
939 {
940 "name": "filename_prefix",
941 "type": "STRING",
942 "links": [
943 98
944 ]
945 }
946 ],
947 "properties": {
948 "cnr_id": "ComfyUI_LC123_nodes",
949 "ver": "e9d3f5fa5d7d6c0f603b2e19f8a8bf458ee3b8ec",
950 "Node name for S&R": "LCEasyFolder",
951 "ue_properties": {
952 "widget_ue_connectable": [],
953 "input_ue_unconnectable": [],
954 "version": "7.8"
955 }
956 },
957 "widgets_values": [
958 "Relay",
959 "Video Test",
960 "",
961 "none",
962 "auto"
963 ],
964 "widgets_values_named": {
965 "folder": "Relay",
966 "prefix": "Video Test",
967 "suffix": "",
968 "timestamp": "none",
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1827 "bgcolor": "#324B4B"
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1848 "texts": {
1849 "en": {
1850 "text": "## Save💾\n\n**LC Save Video**\n\n- **relay**: Takes the output and saves the video a few frames at a time, straight to the file. The whole upscaled video never sits in RAM.\n - 5 s clip at 4x: **0.3 GB** of RAM instead of **12 GB**.\n- Leave the Relay node's **image** output empty, or the full video gets built anyway.\n\n\n\n**When bypassing**\n- bypass the Relay group and LC Save Video saves the original video from **images** instead. Same save node, nothing to rewire.\n\n- **save_metadata** puts the workflow in the mp4, so CivitAI can find your resources.\n\n**Settings ⚙️**\n\n- **crf:** 19 looks like the source. Lower = better and bigger.\n- **pingpong** and **loop_count** are skipped for a relay video.\n\n"
1851 },
1852 "zh": {
1853 "text": "## 保存💾\n\n**LC 保存视频**\n\n- **relay**：逐帧将输出直接保存到文件，整个超分辨率视频不会占用内存。\n - 5秒4倍速视频：仅需 **0.3 GB** 内存，而非 **12 GB**。\n- 若 Relay 节点的 **image** 输出留空，则仍会完整生成整段视频。\n\n---\n\n**当绕过时**\n- 绕过 Relay 组后，LC 保存视频将从 **images** 直接保存原始视频。同一保存节点，无需重新布线。\n\n- **save_metadata** 将工作流嵌入 mp4 文件，以便 CivitAI 能识别你的资源。\n\n---\n\n**设置 ⚙️**\n\n- **crf**：19 看起来与源视频一致。数值越低，画质越好但文件越大。\n- 对于 relay 视频，**pingpong** 和 **loop_count** 设置将被跳过。",
1854 "src": "e76fffde",
1855 "srcTitle": ""
1856 },
1857 "zh-TW": {
1858 "text": "## 儲存💾\n\n**LC 儲存影片**\n\n- **relay**：將輸出逐幀儲存至檔案，不讓整個上採樣後的影片停留在記憶體中。\n - 5 秒片段以 4x 上採樣：僅需 **0.3 GB** 記憶體，而非 **12 GB**。\n- 若 Relay 節點的 **image** 輸出留空，則仍會完整建立影片。\n\n---\n\n**當繞過時**\n- 繞過 Relay 群組後，LC 儲存影片將直接從 **images** 儲存原始影片。同一儲存節點，無需重新接線。\n\n- **save_metadata** 會將工作流程嵌入 mp4 檔案，讓 CivitAI 可搜尋到你的資源。\n\n---\n\n**設定 ⚙️**\n\n- **crf**：19 看起來與原始影片相近。數值越低 = 質量越好、檔案越大。\n- 對於 relay 影片，**pingpong** 和 **loop_count** 設定將被跳過。",
1859 "src": "e76fffde",
1860 "srcTitle": ""
1861 },
1862 "ja": {
1863 "text": "## 保存💾\n\n**LC Save Video**\n\n- **relay**: 出力を受け取り、フレームごとにファイルに直接書き込み。アップスケールされた動画全体がRAMに一時的に残らない。\n - 4xで5秒のクリップ：**0.3 GB** のRAM（通常は**12 GB**）。\n- リレー・ノードの **image** 出力を空にしないと、動画全体が無条件に作成される。\n\n---\n\n**バイトパス時**\n- リレー・グループをバイパスすると、LC Save Video は **images** から元の動画を保存する。同じ保存ノードで、配線を変更する必要はない。\n\n- **save_metadata** はワークフロー情報をMP4に埋め込み、CivitAIがリソースを検索できるようにする。\n\n---\n\n**設定 ⚙️**\n\n- **crf:** 19は元の動画とほぼ同じ品質。低い値ほど品質は高くなりますが、ファイルサイズも大きくなる。\n- リレー・ビデオでは **pingpong** と **loop_count** は無視される。",
1864 "src": "e76fffde",
1865 "srcTitle": ""
1866 },
1867 "ko": {
1868 "text": "## 저장💾\n\n**LC Save Video**\n\n- **relay**: 출력을 받아 몇 프레임씩 직접 파일에 저장합니다. 확대된 전체 영상은 RAM에 한 번도 저장되지 않습니다.\n - 4x 확대 5초 영상: **0.3 GB**의 RAM 대신 **12 GB** 사용.\n- Relay 노드의 **image** 출력을 빈 값으로 두어야만 전체 영상이 생성되지 않습니다.\n\n---\n\n**바이패스 시**\n- Relay 그룹을 우회하면 LC Save Video는 **images**에서 원본 영상을 저장합니다. 동일한 저장 노드이며, 재연결할 필요 없음.\n\n- **save_metadata**: 작업 흐름을 mp4에 포함시켜 CivitAI가 자원을 찾을 수 있도록 합니다.\n\n---\n\n**설정 ⚙️**\n\n- **crf:** 19는 원본과 유사한 품질입니다. 낮을수록 품질은 좋고 파일 크기는 커집니다.\n- **pingpong** 및 **loop_count**는 릴레이 영상에서는 무시됩니다.",
1869 "src": "e76fffde",
1870 "srcTitle": ""
1871 }
1872 },
1873 "titles": []
1874 },
1875 "ue_properties": {
1876 "widget_ue_connectable": [],
1877 "version": "7.8",
1878 "input_ue_unconnectable": []
1879 }
1880 },
1881 "widgets_values": [
1882 "English ✏️",
1883 "## Save💾\n\n**LC Save Video**\n\n- **relay**: Takes the output and saves the video a few frames at a time, straight to the file. The whole upscaled video never sits in RAM.\n - 5 s clip at 4x: **0.3 GB** of RAM instead of **12 GB**.\n- Leave the Relay node's **image** output empty, or the full video gets built anyway.\n\n\n\n**When bypassing**\n- bypass the Relay group and LC Save Video saves the original video from **images** instead. Same save node, nothing to rewire.\n\n- **save_metadata** puts the workflow in the mp4, so CivitAI can find your resources.\n\n**Settings ⚙️**\n\n- **crf:** 19 looks like the source. Lower = better and bigger.\n- **pingpong** and **loop_count** are skipped for a relay video.\n\n"
1884 ],
1885 "widgets_values_named": {
1886 "language": "English ✏️",
1887 "text": "## Save💾\n\n**LC Save Video**\n\n- **relay**: Takes the output and saves the video a few frames at a time, straight to the file. The whole upscaled video never sits in RAM.\n - 5 s clip at 4x: **0.3 GB** of RAM instead of **12 GB**.\n- Leave the Relay node's **image** output empty, or the full video gets built anyway.\n\n\n\n**When bypassing**\n- bypass the Relay group and LC Save Video saves the original video from **images** instead. Same save node, nothing to rewire.\n\n- **save_metadata** puts the workflow in the mp4, so CivitAI can find your resources.\n\n**Settings ⚙️**\n\n- **crf:** 19 looks like the source. Lower = better and bigger.\n- **pingpong** and **loop_count** are skipped for a relay video.\n\n"
1888 },
1889 "color": "#2E3A46",
1890 "bgcolor": "#2E3A46"
1891 },
1892 {
1893 "id": 69,
1894 "type": "LCSaveVideo",
1895 "pos": [
1896 3040,
1897 -2350
1898 ],
1899 "size": [
1900 300,
1901 780
1902 ],
1903 "flags": [],
1904 "order": 31,
1905 "mode": 0,
1906 "inputs": [
1907 {
1908 "name": "images",
1909 "shape": 7,
1910 "type": "IMAGE",
1911 "link": 95
1912 },
1913 {
1914 "name": "audio",
1915 "shape": 7,
1916 "type": "AUDIO",
1917 "link": 96
1918 },
1919 {
1920 "name": "av_pipe",
1921 "shape": 7,
1922 "type": "LC_AV_PIPE",
1923 "link": null
1924 },
1925 {
1926 "name": "metadata",
1927 "shape": 7,
1928 "type": "LC_SAVE_META,LC_SAVE_VIDEO_META",
1929 "link": null
1930 },
1931 {
1932 "name": "relay",
1933 "shape": 7,
1934 "type": "LC_RELAY",
1935 "link": 94
1936 },
1937 {
1938 "name": "frame_rate",
1939 "type": "FLOAT",
1940 "widget": {
1941 "name": "frame_rate"
1942 },
1943 "link": 97
1944 },
1945 {
1946 "name": "filename_prefix",
1947 "type": "STRING",
1948 "widget": {
1949 "name": "filename_prefix"
1950 },
1951 "link": 98
1952 }
1953 ],
1954 "outputs": [
1955 {
1956 "name": "saved_as",
1957 "type": "STRING",
1958 "links": []
1959 }
1960 ],
1961 "properties": {
1962 "cnr_id": "ComfyUI_LC_AV_nodes",
1963 "ver": "0.5.21",
1964 "Node name for S&R": "LCSaveVideo",
1965 "ue_properties": {
1966 "widget_ue_connectable": {
1967 "frame_rate": true,
1968 "loop_count": true,
1969 "filename_prefix": true,
1970 "format": true,
1971 "pix_fmt": true,
1972 "crf": true,
1973 "save_metadata": true,
1974 "civitai_still": true,
1975 "pingpong": true,
1976 "save_output": true,
1977 "prune_outputs": true
1978 },
1979 "version": "7.8",
1980 "input_ue_unconnectable": []
1981 }
1982 },
1983 "widgets_values": [
1984 24,
1985 0,
1986 "Relay",
1987 "video/h264-mp4",
1988 "yuv420p",
1989 19,
1990 false,
1991 false,
1992 false,
1993 true,
1994 true
1995 ],
1996 "widgets_values_named": {
1997 "frame_rate": 24,
1998 "loop_count": 0,
1999 "filename_prefix": "Relay",
2000 "format": "video/h264-mp4",
2001 "pix_fmt": "yuv420p",
2002 "crf": 19,
2003 "save_metadata": false,
2004 "civitai_still": false,
2005 "pingpong": false,
2006 "save_output": true,
2007 "prune_outputs": true
2008 },
2009 "color": "#1a5a7a",
2010 "bgcolor": "#1a5a7a"
2011 },
2012 {
2013 "id": 70,
2014 "type": "LCImageLabel",
2015 "pos": [
2016 1530,
2017 -3410
2018 ],
2019 "size": [
2020 221,
2021 221
2022 ],
2023 "flags": [],
2024 "order": 20,
2025 "mode": 0,
2026 "inputs": [],
2027 "outputs": [],
2028 "properties": {
2029 "imgW": 211,
2030 "imgH": 211,
2031 "angle": 0,
2032 "size": 211,
2033 "padding": 5,
2034 "borderWidth": 0,
2035 "borderColor": "#ffffff",
2036 "borderRadius": 0,
2037 "backgroundColor": "transparent",
2038 "bgTransparent": true,
2039 "syncImageRadius": true,
2040 "imageRadius": 0,
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2042 "_w": 221,
2043 "_h": 221,
2044 "ue_properties": {
2045 "widget_ue_connectable": [],
2046 "version": "7.8",
2047 "input_ue_unconnectable": []
2048 }
2049 },
2050 "widgets_values": [
2051 "{\"filename\":\"Logo no background_00001_.png\",\"subfolder\":\"\"}"
2052 ],
2053 "widgets_values_named": {
2054 "_image_path": "{\"filename\":\"Logo no background_00001_.png\",\"subfolder\":\"\"}"
2055 },
2056 "color": "#fff0",
2057 "bgcolor": "#fff0"
2058 },
2059 {
2060 "id": 71,
2061 "type": "LCPersonMask",
2062 "pos": [
2063 2450,
2064 -3030
2065 ],
2066 "size": [
2067 300,
2068 490
2069 ],
2070 "flags": [],
2071 "order": 28,
2072 "mode": 0,
2073 "inputs": [
2074 {
2075 "name": "image",
2076 "type": "IMAGE",
2077 "link": 99
2078 }
2079 ],
2080 "outputs": [
2081 {
2082 "name": "cutout",
2083 "type": "IMAGE",
2084 "links": null
2085 },
2086 {
2087 "name": "mask",
2088 "type": "MASK",
2089 "links": [
2090 100,
2091 101
2092 ]
2093 },
2094 {
2095 "name": "raw_mask",
2096 "type": "MASK",
2097 "links": null
2098 }
2099 ],
2100 "properties": {
2101 "cnr_id": "ComfyUI_LC_MaskMaker_nodes",
2102 "ver": "a8c3e1c6a19abfae17932561c88b32d41cf426a8",
2103 "Node name for S&R": "LCPersonMask",
2104 "ue_properties": {
2105 "widget_ue_connectable": [],
2106 "input_ue_unconnectable": [],
2107 "version": "7.8"
2108 }
2109 },
2110 "widgets_values": [
2111 "segformer",
2112 "models/segformer_b2_clothes (local)",
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2222 "en": {
2223 "text": "## LC Relay VSR Upscale 🧩\n\n**What is it?**\n- A one-step upscaler for video and pictures (2x or 4x). The model, VAE and sampler are built in. Fast, sharp, and it adds real skin and hair detail.\n- A fraction of the time VOSR2 or SeedVR2 takes, and higher quality.\n- **detect_faces** finds the faces on its own and holds back Relay's grain on them, so skin keeps its look.\n- 💡 If you used detailers or want all skin protected, wire a mask into **protect_mask**. It overrides detect_faces.\n\n- ✋ **First use downloads the models:** 5.2 GB per upscale size (10 GB for bf16) plus a 128 MB VAE, and 55 MB for the face finder. A toast tells you it started. Press Stop to cancel. Already have them? They are found wherever they are.\n\n---\n\n### Videos 🎥\n\n- **2x faces on** is the default. Your eye goes straight to the faces when things are moving, and everything else is background noise.\n - Use **faces off** only when time or file size is the problem.\n- **4x** runs on an 8 GB card, but on a moving video it looks barely different from 2x. Extra sharpness can actually make it look worse.\n- Save with **LC Save Video**'s **relay** input. It saves a few frames at a time, so the upscaled video never sits in RAM (5 s at 4x: 0.3 GB instead of 12 GB).\n - Leave the Relay node's **image** output empty, or the full video gets built anyway.\n\n| Video | 2x faces on | 2x faces off | 4x faces on | 4x faces off |\n|---|---|---|---|---|\n| 960 x 544, 5 s, 24 fps | 13 s (est.) | 12 s | 36 s (est.) | 34 s |\n| 896 x 1152, 6.6 s, 24 fps | 26 s (est.) | 22 - 24 s | 78 s (est.) | 72 - 75 s |\n| 832 x 1056, 15 s, 48 fps | 46 s | 40 s | 2.7 min | 2.6 min |\n| **8 GB laptop:** 960 x 544, 5 s, 24 fps | 28 s (est.) | 27 s | 2 min (est.) | 118 s |\n| **8 GB laptop:** 832 x 1056, 15 s, 48 fps | 4 min | 3.9 min | 18 min (est.) | 17 min (est.) |\n\n\\* RTX 5090 unless noted, laptop is an RTX 5060 8 GB. Models already downloaded. Faces on finds the faces every 3rd frame and costs about 4 - 15 % (measured on the 15 s clip). (est.) rows are scaled from the measured ones.\n- 💡 VRAM stays flat on 8 GB cards (7.4 GB at 2x), so long clips do not slow to a crawl. The 5090 used 13 GB at 2x and 24 GB at 4x only because it had it free.\n- ⚠️ **16 GB RAM:** the frames sit in RAM before Relay even starts. The 15 s, 48 fps clip peaked at 18.5 GB.\n - Keep clips short, or upscale **before** frame interpolation (half the frames, half the RAM). Recommended, not tested.\n\n---\n\n### Pictures 🖼️\n\n- People actually zoom in on a picture, so 4x is worth it here. Faces cost nothing on a picture (about 4 s either way at 4x, 6 s for a 1056 x 1320 picture).\n- **Quality order:**\n - 4x faces on\n - 2x faces on\n - 4x faces off\n - 2x faces off\n- **Realism and photos:** faces on.\n- **Lineart, anime, painted, etc.:** depends on the face.\n - Smooth or painted skin: faces on (the grain is just noise there).\n - Freckles, hatching or other drawn detail: faces off, or that detail goes soft.\n- Small faces in a crowd: on or off makes no visible difference.\n\n**💡 Tips & tricks**\n- Adjust how much the faces are protected with **mask_protect_strength**.\n- Adjust the sharpness with **detail**.\n\n---\n\n### Settings ⚙️\n\n- **source:**\n - **video:** the frames are matched to each other, so the video stays steady. Use it for anything that came from a video.\n - **image:** every picture is upscaled on its own, so nothing carries over between them. Use it for a batch of different pictures. One picture comes out the same either way.\n- **upscale:** 2x for video, 4x for pictures. Each one is its own model.\n - **2x bf16 / 4x bf16 (10 GB):** the original release. It's an option, but not noticeably different, and it uses about 5 GB more VRAM.\n - Quality: practically identical. The average difference is 0.12 - 0.21 levels out of 255, and the 100% crops can't be told apart.\n - 4x on a 5090: pictures 4 s either way (10 - 11 GB vs 15 - 17 GB VRAM), a 3 s clip 22 s vs 25 s (19 GB vs 24 GB).\n- **detail:** 1.0 is the full model output. Lower moves toward a plain upscale.\n- **detect_faces:** typically on. Off for drawn faces with freckles or hatching.\n- **mask_protect_strength:** how much of Relay's detail still lands on the face. 0.5 is the default. Raise it to keep more texture on stylized faces.\n\n---\n\n### Manual download 📂 (optional)\n\n- The node downloads these on first use. Grab them yourself if you'd rather not wait mid-run.\n\n- [Relay 2x model](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x model](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 model (optional, 10 GB)](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 model (optional, 10 GB)](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, optional, 10 GB)\n │ └── wan (4x bf16, optional, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```"
2224 },
2225 "zh": {
2226 "text": "## LC Relay VSR Upscale 🧩\n\n**这是什么？**\n- 一步完成的视频和图片放大器（2x 或 4x）。模型、VAE 和采样器都已内置。速度快、清晰，还能添加真实的皮肤和头发细节。\n- 耗时只有 VOSR2 或 SeedVR2 的一小部分，质量更高。\n- **detect_faces** 会自动找到人脸，并压住 Relay 在脸上的颗粒，让皮肤保持原样。\n- 💡 如果你用了检测修复（detailer），或想保护全部皮肤，把遮罩接到 **protect_mask**。它会覆盖 detect_faces。\n\n- ✋ **首次使用会下载模型：** 每种放大倍数 5.2 GB（bf16 为 10 GB），加一个 128 MB 的 VAE，人脸检测另需 55 MB。开始时会弹出提示。按 Stop 可取消。已经有了？无论放在哪里都能找到。\n\n---\n\n### 视频 🎥\n\n- **2x 开启人脸** 是默认设置。画面在动时，眼睛会直接盯住人脸，其余部分都成了背景噪声。\n - 只有在时间或文件大小成为问题时，才使用 **关闭人脸**。\n- **4x** 能在 8 GB 显卡上运行，但在运动的视频里和 2x 几乎看不出区别。多出来的锐度反而可能让画面更难看。\n- 用 **LC Save Video** 的 **relay** 输入保存。它一次只保存几帧，放大后的视频永远不会整个放进内存（5 秒 4x：0.3 GB，而不是 12 GB）。\n - Relay 节点的 **image** 输出保持空着，否则还是会生成完整视频。\n\n| 视频 | 2x 开启人脸 | 2x 关闭人脸 | 4x 开启人脸 | 4x 关闭人脸 |\n|---|---|---|---|---|\n| 960 x 544，5 秒，24 fps | 13 秒（估计） | 12 秒 | 36 秒（估计） | 34 秒 |\n| 896 x 1152，6.6 秒，24 fps | 26 秒（估计） | 22 - 24 秒 | 78 秒（估计） | 72 - 75 秒 |\n| 832 x 1056，15 秒，48 fps | 46 秒 | 40 秒 | 2.7 分钟 | 2.6 分钟 |\n| **8 GB 笔记本：** 960 x 544，5 秒，24 fps | 28 秒（估计） | 27 秒 | 2 分钟（估计） | 118 秒 |\n| **8 GB 笔记本：** 832 x 1056，15 秒，48 fps | 4 分钟 | 3.9 分钟 | 18 分钟（估计） | 17 分钟（估计） |\n\n\\* 除注明外均为 RTX 5090，笔记本为 RTX 5060 8 GB。模型已下载。开启人脸时每 3 帧检测一次人脸，多花约 4 - 15 %（在 15 秒片段上实测）。（估计）一栏由实测数据推算。\n- 💡 8 GB 显卡上显存占用保持平稳（2x 时 7.4 GB），所以长片段不会越跑越慢。5090 在 2x 用了 13 GB、4x 用了 24 GB，只是因为它有空闲显存。\n- ⚠️ **16 GB 内存：** 在 Relay 开始之前，所有帧就已经放在内存里了。15 秒、48 fps 的片段峰值为 18.5 GB。\n - 片段保持简短，或者在插帧 **之前** 放大（帧数减半，内存减半）。推荐做法，未测试。\n\n---\n\n### 图片 🖼️\n\n- 人们真的会放大查看图片，所以这里 4x 值得。图片上开启人脸几乎不花时间（4x 时开关都约 4 秒，1056 x 1320 的图片约 6 秒）。\n- **质量排序：**\n - 4x 开启人脸\n - 2x 开启人脸\n - 4x 关闭人脸\n - 2x 关闭人脸\n- **写实和照片：** 开启人脸。\n- **线稿、动漫、绘画等：** 取决于脸。\n - 光滑或绘画式的皮肤：开启人脸（那里的颗粒只是噪声）。\n - 雀斑、排线或其他画出来的细节：关闭人脸，否则这些细节会变软。\n- 人群里的小脸：开或关看不出区别。\n\n**💡 小技巧**\n- 用 **mask_protect_strength** 调整人脸受保护的程度。\n- 用 **detail** 调整锐度。\n\n---\n\n### 设置 ⚙️\n\n- **source：**\n - **video：** 帧与帧之间相互匹配，视频保持稳定。凡是来自视频的内容都用它。\n - **image：** 每张图片单独放大，彼此之间不会互相影响。用于一批不同的图片。单张图片两种方式结果相同。\n- **upscale：** 视频用 2x，图片用 4x。每种都是独立的模型。\n - **2x bf16 / 4x bf16（10 GB）：** 原始版本。可以选，但看不出明显区别，还要多用约 5 GB 显存。\n - 画质：几乎一样。平均差异只有 0.12 - 0.21（满分 255），100% 裁切图也看不出区别。\n - 5090 上 4x：图片都是 4 秒（显存 10 - 11 GB 对 15 - 17 GB），3 秒视频 22 秒对 25 秒（19 GB 对 24 GB）。\n- **detail：** 1.0 是完整的模型输出。调低会更接近普通放大。\n- **detect_faces：** 通常开启。对带雀斑或排线的手绘脸关闭。\n- **mask_protect_strength：** Relay 的细节还有多少会落到脸上。默认 0.5。调高可以在风格化的脸上保留更多纹理。\n\n---\n\n### 手动下载 📂（可选）\n\n- 节点会在首次使用时下载这些文件。不想在运行中途等待的话，可以自己先下载。\n\n- [Relay 2x 模型](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x 模型](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 模型（可选，10 GB）](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 模型（可选，10 GB）](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, 可选, 10 GB)\n │ └── wan (4x bf16, 可选, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```",
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2229 },
2230 "zh-TW": {
2231 "text": "## LC Relay VSR Upscale 🧩\n\n**這是什麼？**\n- 一步完成的影片和圖片放大器（2x 或 4x）。模型、VAE 和取樣器都已內建。速度快、清晰，還能加上真實的皮膚和頭髮細節。\n- 耗時只有 VOSR2 或 SeedVR2 的一小部分，品質更高。\n- **detect_faces** 會自動找到人臉，並壓住 Relay 在臉上的顆粒，讓皮膚保持原樣。\n- 💡 如果你用了細節修復（detailer），或想保護全部皮膚，把遮罩接到 **protect_mask**。它會覆蓋 detect_faces。\n\n- ✋ **第一次使用會下載模型：** 每種放大倍數 5.2 GB（bf16 為 10 GB），加一個 128 MB 的 VAE，人臉偵測另需 55 MB。開始時會跳出提示。按 Stop 可取消。已經有了？不論放在哪裡都找得到。\n\n---\n\n### 影片 🎥\n\n- **2x 開啟人臉** 是預設設定。畫面在動時，眼睛會直接盯著人臉，其餘部分都成了背景雜訊。\n - 只有在時間或檔案大小成為問題時，才使用 **關閉人臉**。\n- **4x** 能在 8 GB 顯示卡上執行，但在動態影片裡和 2x 幾乎看不出差別。多出來的銳利度反而可能讓畫面更難看。\n- 用 **LC Save Video** 的 **relay** 輸入儲存。它一次只存幾格，放大後的影片永遠不會整個放進記憶體（5 秒 4x：0.3 GB，而不是 12 GB）。\n - Relay 節點的 **image** 輸出保持空著，否則還是會產生完整影片。\n\n| 影片 | 2x 開啟人臉 | 2x 關閉人臉 | 4x 開啟人臉 | 4x 關閉人臉 |\n|---|---|---|---|---|\n| 960 x 544，5 秒，24 fps | 13 秒（估計） | 12 秒 | 36 秒（估計） | 34 秒 |\n| 896 x 1152，6.6 秒，24 fps | 26 秒（估計） | 22 - 24 秒 | 78 秒（估計） | 72 - 75 秒 |\n| 832 x 1056，15 秒，48 fps | 46 秒 | 40 秒 | 2.7 分鐘 | 2.6 分鐘 |\n| **8 GB 筆電：** 960 x 544，5 秒，24 fps | 28 秒（估計） | 27 秒 | 2 分鐘（估計） | 118 秒 |\n| **8 GB 筆電：** 832 x 1056，15 秒，48 fps | 4 分鐘 | 3.9 分鐘 | 18 分鐘（估計） | 17 分鐘（估計） |\n\n\\* 除註明外皆為 RTX 5090，筆電為 RTX 5060 8 GB。模型已下載。開啟人臉時每 3 格偵測一次人臉，多花約 4 - 15 %（在 15 秒片段上實測）。（估計）欄位由實測數據推算。\n- 💡 8 GB 顯示卡上顯存占用保持平穩（2x 時 7.4 GB），所以長片段不會越跑越慢。5090 在 2x 用了 13 GB、4x 用了 24 GB，只是因為它有空閒顯存。\n- ⚠️ **16 GB 記憶體：** 在 Relay 開始之前，所有畫格就已經放在記憶體裡了。15 秒、48 fps 的片段峰值為 18.5 GB。\n - 片段保持簡短，或者在補幀 **之前** 放大（畫格減半，記憶體減半）。建議做法，未測試。\n\n---\n\n### 圖片 🖼️\n\n- 大家真的會放大檢視圖片，所以這裡 4x 值得。圖片上開啟人臉幾乎不花時間（4x 時開關都約 4 秒，1056 x 1320 的圖片約 6 秒）。\n- **品質排序：**\n - 4x 開啟人臉\n - 2x 開啟人臉\n - 4x 關閉人臉\n - 2x 關閉人臉\n- **寫實和照片：** 開啟人臉。\n- **線稿、動漫、繪畫等：** 取決於臉。\n - 光滑或繪畫式的皮膚：開啟人臉（那裡的顆粒只是雜訊）。\n - 雀斑、排線或其他畫出來的細節：關閉人臉，否則這些細節會變軟。\n- 人群裡的小臉：開或關看不出差別。\n\n**💡 小技巧**\n- 用 **mask_protect_strength** 調整人臉受保護的程度。\n- 用 **detail** 調整銳利度。\n\n---\n\n### 設定 ⚙️\n\n- **source：**\n - **video：** 畫格之間互相匹配，影片保持穩定。凡是來自影片的內容都用它。\n - **image：** 每張圖片單獨放大，彼此之間不會互相影響。用於一批不同的圖片。單張圖片兩種方式結果相同。\n- **upscale：** 影片用 2x，圖片用 4x。每種都是獨立的模型。\n - **2x bf16 / 4x bf16（10 GB）：** 原始版本。可以選，但看不出明顯差別，還要多用約 5 GB 顯存。\n - 畫質：幾乎一樣。平均差異只有 0.12 - 0.21（滿分 255），100% 裁切圖也看不出差別。\n - 5090 上 4x：圖片都是 4 秒（顯存 10 - 11 GB 對 15 - 17 GB），3 秒影片 22 秒對 25 秒（19 GB 對 24 GB）。\n- **detail：** 1.0 是完整的模型輸出。調低會更接近一般放大。\n- **detect_faces：** 通常開啟。對帶雀斑或排線的手繪臉關閉。\n- **mask_protect_strength：** Relay 的細節還有多少會落到臉上。預設 0.5。調高可以在風格化的臉上保留更多紋理。\n\n---\n\n### 手動下載 📂（可選）\n\n- 節點會在第一次使用時下載這些檔案。不想在執行中途等待的話，可以自己先下載。\n\n- [Relay 2x 模型](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x 模型](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 模型（選用，10 GB）](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 模型（選用，10 GB）](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, 選用, 10 GB)\n │ └── wan (4x bf16, 選用, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```",
2232 "src": "36dc9909",
2233 "srcTitle": "Relay Notes"
2234 },
2235 "ja": {
2236 "text": "## LC Relay VSR Upscale 🧩\n\n**これは何？**\n- 動画と画像を1ステップで拡大するアップスケーラー（2x または 4x）。モデル、VAE、サンプラーはすべて内蔵。速くてシャープ、本物の肌と髪のディテールを加えます。\n- VOSR2 や SeedVR2 のほんの一部の時間で、しかも高品質。\n- **detect_faces** が顔を自動で見つけ、顔の上の Relay の粒状感を抑えるので、肌の見た目がそのまま残ります。\n- 💡 ディテーラーを使った場合や肌全体を守りたい場合は、マスクを **protect_mask** につなぎます。detect_faces より優先されます。\n\n- ✋ **初回使用時にモデルをダウンロードします：** 拡大サイズごとに 5.2 GB（bf16 は 10 GB）と 128 MB の VAE、顔検出用に 55 MB。開始するとトーストが出ます。Stop でキャンセル。もう持っている？ どこに置いてあっても見つかります。\n\n---\n\n### 動画 🎥\n\n- **2x 顔オン** がデフォルト。動いている映像では目はまっすぐ顔に向かい、それ以外は背景のノイズになります。\n - **顔オフ** は時間やファイルサイズが問題になるときだけ使います。\n- **4x** は 8 GB のカードでも動きますが、動いている映像では 2x とほとんど違いが分かりません。余分なシャープさでかえって悪く見えることもあります。\n- **LC Save Video** の **relay** 入力で保存します。数フレームずつ保存するので、拡大した動画が丸ごとメモリに乗ることはありません（5 秒の 4x：12 GB ではなく 0.3 GB）。\n - Relay ノードの **image** 出力は空けておきます。つなぐと結局フル動画が作られます。\n\n| 動画 | 2x 顔オン | 2x 顔オフ | 4x 顔オン | 4x 顔オフ |\n|---|---|---|---|---|\n| 960 x 544、5 秒、24 fps | 13 秒（推定） | 12 秒 | 36 秒（推定） | 34 秒 |\n| 896 x 1152、6.6 秒、24 fps | 26 秒（推定） | 22 - 24 秒 | 78 秒（推定） | 72 - 75 秒 |\n| 832 x 1056、15 秒、48 fps | 46 秒 | 40 秒 | 2.7 分 | 2.6 分 |\n| **8 GB ノートPC：** 960 x 544、5 秒、24 fps | 28 秒（推定） | 27 秒 | 2 分（推定） | 118 秒 |\n| **8 GB ノートPC：** 832 x 1056、15 秒、48 fps | 4 分 | 3.9 分 | 18 分（推定） | 17 分（推定） |\n\n\\* 注記がなければ RTX 5090、ノートPC は RTX 5060 8 GB。モデルはダウンロード済み。顔オンは 3 フレームごとに顔を検出し、約 4 - 15 % 余分にかかります（15 秒のクリップで実測）。（推定）は実測値から換算しています。\n- 💡 8 GB のカードでは VRAM が一定のまま（2x で 7.4 GB）なので、長いクリップでも極端に遅くなりません。5090 が 2x で 13 GB、4x で 24 GB 使ったのは、空きがあったからにすぎません。\n- ⚠️ **16 GB RAM：** Relay が始まる前に、フレームはすでにメモリに乗っています。15 秒、48 fps のクリップはピーク 18.5 GB。\n - クリップは短くするか、フレーム補間の **前に** 拡大します（フレームが半分、RAM も半分）。推奨、未テスト。\n\n---\n\n### 画像 🖼️\n\n- 画像は実際にズームして見られるので、ここでは 4x の価値があります。画像なら顔オンの時間はほぼゼロ（4x でオンでもオフでも約 4 秒、1056 x 1320 の画像で 6 秒）。\n- **品質の順番：**\n - 4x 顔オン\n - 2x 顔オン\n - 4x 顔オフ\n - 2x 顔オフ\n- **リアル系と写真：** 顔オン。\n- **線画、アニメ、ペイント系など：** 顔しだい。\n - なめらかな肌やペイントの肌：顔オン（そこでは粒状感はただのノイズ）。\n - そばかす、ハッチング、その他描き込まれたディテール：顔オフ。そうしないとそのディテールがぼやけます。\n- 群衆の中の小さな顔：オンでもオフでも見た目の違いはありません。\n\n**💡 コツ**\n- 顔の守り具合は **mask_protect_strength** で調整します。\n- シャープさは **detail** で調整します。\n\n---\n\n### 設定 ⚙️\n\n- **source：**\n - **video：** フレーム同士を合わせるので、動画が安定します。動画から来たものすべてに使います。\n - **image：** 画像ごとに個別に拡大するので、画像間で何も引き継がれません。別々の画像のバッチに使います。画像1枚ならどちらでも同じ結果です。\n- **upscale：** 動画は 2x、画像は 4x。それぞれ別のモデルです。\n - **2x bf16 / 4x bf16（10 GB）：** オリジナル版。選べますが、目に見える違いはなく、VRAM を約 5 GB 多く使います。\n - 画質：ほぼ同じ。平均差は 255 段階中 0.12 - 0.21 で、100% の切り抜きでも見分けがつきません。\n - 5090 で 4x：画像はどちらも 4 秒（VRAM 10 - 11 GB 対 15 - 17 GB）、3 秒のクリップは 22 秒対 25 秒（19 GB 対 24 GB）。\n- **detail：** 1.0 がモデルのフル出力。下げるほど普通の拡大に近づきます。\n- **detect_faces：** 通常はオン。そばかすやハッチングのある描かれた顔ではオフ。\n- **mask_protect_strength：** Relay のディテールが顔にどれだけ乗るか。デフォルトは 0.5。上げると様式化された顔のテクスチャがより残ります。\n\n---\n\n### 手動ダウンロード 📂（任意）\n\n- ノードは初回使用時にこれらをダウンロードします。実行中に待ちたくなければ、自分で先に入手してください。\n\n- [Relay 2x モデル](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x モデル](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 モデル（任意、10 GB）](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 モデル（任意、10 GB）](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, 任意, 10 GB)\n │ └── wan (4x bf16, 任意, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```",
2237 "src": "36dc9909",
2238 "srcTitle": "Relay Notes"
2239 },
2240 "ko": {
2241 "text": "## LC Relay VSR Upscale 🧩\n\n**이게 뭔가요?**\n- 영상과 사진을 한 번에 키우는 업스케일러(2x 또는 4x). 모델, VAE, 샘플러가 모두 내장되어 있습니다. 빠르고 선명하며, 실제 피부와 머리카락 디테일을 더합니다.\n- VOSR2나 SeedVR2에 걸리는 시간의 일부만 들고, 품질은 더 높습니다.\n- **detect_faces**가 얼굴을 스스로 찾아 얼굴 위의 Relay 그레인을 눌러 주므로, 피부가 원래 모습을 유지합니다.\n- 💡 디테일러를 썼거나 피부 전체를 보호하고 싶다면 마스크를 **protect_mask**에 연결하세요. detect_faces보다 우선합니다.\n\n- ✋ **첫 사용 시 모델을 다운로드합니다:** 업스케일 크기마다 5.2 GB(bf16은 10 GB)와 128 MB VAE, 얼굴 감지용 55 MB. 시작하면 토스트 알림이 뜹니다. Stop을 누르면 취소됩니다. 이미 있나요? 어디에 두었든 찾아냅니다.\n\n---\n\n### 영상 🎥\n\n- **2x 얼굴 켜기**가 기본입니다. 화면이 움직일 때 눈은 곧장 얼굴로 가고, 나머지는 배경 노이즈가 됩니다.\n - **얼굴 끄기**는 시간이나 파일 크기가 문제일 때만 쓰세요.\n- **4x**는 8 GB 카드에서도 돌아가지만, 움직이는 영상에서는 2x와 거의 차이가 없습니다. 오히려 과한 선명함 때문에 더 나빠 보일 수 있습니다.\n- **LC Save Video**의 **relay** 입력으로 저장하세요. 몇 프레임씩 저장하므로, 업스케일된 영상이 통째로 RAM에 올라가지 않습니다(5초 4x: 12 GB 대신 0.3 GB).\n - Relay 노드의 **image** 출력은 비워 두세요. 연결하면 결국 전체 영상이 만들어집니다.\n\n| 영상 | 2x 얼굴 켜기 | 2x 얼굴 끄기 | 4x 얼굴 켜기 | 4x 얼굴 끄기 |\n|---|---|---|---|---|\n| 960 x 544, 5초, 24 fps | 13초 (추정) | 12초 | 36초 (추정) | 34초 |\n| 896 x 1152, 6.6초, 24 fps | 26초 (추정) | 22 - 24초 | 78초 (추정) | 72 - 75초 |\n| 832 x 1056, 15초, 48 fps | 46초 | 40초 | 2.7분 | 2.6분 |\n| **8 GB 노트북:** 960 x 544, 5초, 24 fps | 28초 (추정) | 27초 | 2분 (추정) | 118초 |\n| **8 GB 노트북:** 832 x 1056, 15초, 48 fps | 4분 | 3.9분 | 18분 (추정) | 17분 (추정) |\n\n\\* 따로 적지 않으면 RTX 5090, 노트북은 RTX 5060 8 GB. 모델은 다운로드된 상태. 얼굴 켜기는 3프레임마다 얼굴을 찾으며 약 4 - 15 % 더 걸립니다(15초 클립에서 측정). (추정)은 측정값을 바탕으로 계산했습니다.\n- 💡 8 GB 카드에서는 VRAM이 일정하게 유지되므로(2x에서 7.4 GB), 긴 클립도 크게 느려지지 않습니다. 5090이 2x에서 13 GB, 4x에서 24 GB를 쓴 것은 여유가 있었기 때문일 뿐입니다.\n- ⚠️ **16 GB RAM:** Relay가 시작되기 전에 프레임이 이미 RAM에 올라가 있습니다. 15초, 48 fps 클립은 최고 18.5 GB였습니다.\n - 클립을 짧게 하거나, 프레임 보간 **전에** 업스케일하세요(프레임 절반, RAM 절반). 권장, 테스트 안 됨.\n\n---\n\n### 사진 🖼️\n\n- 사람들은 사진을 실제로 확대해서 보므로, 여기서는 4x가 그만한 값을 합니다. 사진에서는 얼굴 켜기에 드는 시간이 거의 없습니다(4x에서 켜든 끄든 약 4초, 1056 x 1320 사진은 6초).\n- **품질 순서:**\n - 4x 얼굴 켜기\n - 2x 얼굴 켜기\n - 4x 얼굴 끄기\n - 2x 얼굴 끄기\n- **실사와 사진:** 얼굴 켜기.\n- **라인아트, 애니메이션, 페인팅 등:** 얼굴에 따라 다릅니다.\n - 매끈하거나 칠한 피부: 얼굴 켜기(거기서는 그레인이 그냥 노이즈입니다).\n - 주근깨, 해칭 등 그려 넣은 디테일: 얼굴 끄기. 안 그러면 그 디테일이 뭉개집니다.\n- 군중 속 작은 얼굴: 켜든 끄든 눈에 띄는 차이가 없습니다.\n\n**💡 팁**\n- 얼굴을 얼마나 보호할지는 **mask_protect_strength**로 조절합니다.\n- 선명도는 **detail**로 조절합니다.\n\n---\n\n### 설정 ⚙️\n\n- **source:**\n - **video:** 프레임끼리 서로 맞추므로 영상이 안정적입니다. 영상에서 나온 것에는 모두 이것을 쓰세요.\n - **image:** 사진마다 따로 업스케일하므로 사진 사이에 아무것도 이어지지 않습니다. 서로 다른 사진 묶음에 쓰세요. 사진 한 장은 어느 쪽이든 결과가 같습니다.\n- **upscale:** 영상은 2x, 사진은 4x. 각각 별도의 모델입니다.\n - **2x bf16 / 4x bf16 (10 GB):** 원본 릴리스. 선택할 수 있지만 눈에 띄는 차이가 없고 VRAM을 약 5 GB 더 씁니다.\n - 화질: 사실상 같습니다. 평균 차이는 255단계 중 0.12 - 0.21이고, 100% 크롭으로도 구분이 안 됩니다.\n - 5090에서 4x: 사진은 둘 다 4초(VRAM 10 - 11 GB 대 15 - 17 GB), 3초 클립은 22초 대 25초(19 GB 대 24 GB).\n- **detail:** 1.0이 모델의 전체 출력입니다. 낮출수록 일반 업스케일에 가까워집니다.\n- **detect_faces:** 보통 켭니다. 주근깨나 해칭이 있는 그린 얼굴에서는 끕니다.\n- **mask_protect_strength:** Relay의 디테일이 얼굴에 얼마나 남는지. 기본값 0.5. 높이면 스타일화된 얼굴에 텍스처가 더 남습니다.\n\n---\n\n### 수동 다운로드 📂 (선택)\n\n- 노드가 첫 사용 시 이 파일들을 다운로드합니다. 실행 도중 기다리기 싫다면 직접 받아 두세요.\n\n- [Relay 2x 모델](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x 모델](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 모델 (선택, 10 GB)](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 모델 (선택, 10 GB)](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, 선택, 10 GB)\n │ └── wan (4x bf16, 선택, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```",
2242 "src": "36dc9909",
2243 "srcTitle": "Relay Notes"
2244 }
2245 },
2246 "titles": {
2247 "en": "Relay Notes",
2248 "zh": "中继笔记",
2249 "zh-TW": "中繼筆記",
2250 "ja": "Relay メモ",
2251 "ko": "Relay 노트"
2252 }
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2261 "English ✏️",
2262 "## LC Relay VSR Upscale 🧩\n\n**What is it?**\n- A one-step upscaler for video and pictures (2x or 4x). The model, VAE and sampler are built in. Fast, sharp, and it adds real skin and hair detail.\n- A fraction of the time VOSR2 or SeedVR2 takes, and higher quality.\n- **detect_faces** finds the faces on its own and holds back Relay's grain on them, so skin keeps its look.\n- 💡 If you used detailers or want all skin protected, wire a mask into **protect_mask**. It overrides detect_faces.\n\n- ✋ **First use downloads the models:** 5.2 GB per upscale size (10 GB for bf16) plus a 128 MB VAE, and 55 MB for the face finder. A toast tells you it started. Press Stop to cancel. Already have them? They are found wherever they are.\n\n---\n\n### Videos 🎥\n\n- **2x faces on** is the default. Your eye goes straight to the faces when things are moving, and everything else is background noise.\n - Use **faces off** only when time or file size is the problem.\n- **4x** runs on an 8 GB card, but on a moving video it looks barely different from 2x. Extra sharpness can actually make it look worse.\n- Save with **LC Save Video**'s **relay** input. It saves a few frames at a time, so the upscaled video never sits in RAM (5 s at 4x: 0.3 GB instead of 12 GB).\n - Leave the Relay node's **image** output empty, or the full video gets built anyway.\n\n| Video | 2x faces on | 2x faces off | 4x faces on | 4x faces off |\n|---|---|---|---|---|\n| 960 x 544, 5 s, 24 fps | 13 s (est.) | 12 s | 36 s (est.) | 34 s |\n| 896 x 1152, 6.6 s, 24 fps | 26 s (est.) | 22 - 24 s | 78 s (est.) | 72 - 75 s |\n| 832 x 1056, 15 s, 48 fps | 46 s | 40 s | 2.7 min | 2.6 min |\n| **8 GB laptop:** 960 x 544, 5 s, 24 fps | 28 s (est.) | 27 s | 2 min (est.) | 118 s |\n| **8 GB laptop:** 832 x 1056, 15 s, 48 fps | 4 min | 3.9 min | 18 min (est.) | 17 min (est.) |\n\n\\* RTX 5090 unless noted, laptop is an RTX 5060 8 GB. Models already downloaded. Faces on finds the faces every 3rd frame and costs about 4 - 15 % (measured on the 15 s clip). (est.) rows are scaled from the measured ones.\n- 💡 VRAM stays flat on 8 GB cards (7.4 GB at 2x), so long clips do not slow to a crawl. The 5090 used 13 GB at 2x and 24 GB at 4x only because it had it free.\n- ⚠️ **16 GB RAM:** the frames sit in RAM before Relay even starts. The 15 s, 48 fps clip peaked at 18.5 GB.\n - Keep clips short, or upscale **before** frame interpolation (half the frames, half the RAM). Recommended, not tested.\n\n---\n\n### Pictures 🖼️\n\n- People actually zoom in on a picture, so 4x is worth it here. Faces cost nothing on a picture (about 4 s either way at 4x, 6 s for a 1056 x 1320 picture).\n- **Quality order:**\n - 4x faces on\n - 2x faces on\n - 4x faces off\n - 2x faces off\n- **Realism and photos:** faces on.\n- **Lineart, anime, painted, etc.:** depends on the face.\n - Smooth or painted skin: faces on (the grain is just noise there).\n - Freckles, hatching or other drawn detail: faces off, or that detail goes soft.\n- Small faces in a crowd: on or off makes no visible difference.\n\n**💡 Tips & tricks**\n- Adjust how much the faces are protected with **mask_protect_strength**.\n- Adjust the sharpness with **detail**.\n\n---\n\n### Settings ⚙️\n\n- **source:**\n - **video:** the frames are matched to each other, so the video stays steady. Use it for anything that came from a video.\n - **image:** every picture is upscaled on its own, so nothing carries over between them. Use it for a batch of different pictures. One picture comes out the same either way.\n- **upscale:** 2x for video, 4x for pictures. Each one is its own model.\n - **2x bf16 / 4x bf16 (10 GB):** the original release. It's an option, but not noticeably different, and it uses about 5 GB more VRAM.\n - Quality: practically identical. The average difference is 0.12 - 0.21 levels out of 255, and the 100% crops can't be told apart.\n - 4x on a 5090: pictures 4 s either way (10 - 11 GB vs 15 - 17 GB VRAM), a 3 s clip 22 s vs 25 s (19 GB vs 24 GB).\n- **detail:** 1.0 is the full model output. Lower moves toward a plain upscale.\n- **detect_faces:** typically on. Off for drawn faces with freckles or hatching.\n- **mask_protect_strength:** how much of Relay's detail still lands on the face. 0.5 is the default. Raise it to keep more texture on stylized faces.\n\n---\n\n### Manual download 📂 (optional)\n\n- The node downloads these on first use. Grab them yourself if you'd rather not wait mid-run.\n\n- [Relay 2x model](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x model](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 model (optional, 10 GB)](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 model (optional, 10 GB)](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, optional, 10 GB)\n │ └── wan (4x bf16, optional, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```"
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2265 "language": "English ✏️",
2266 "text": "## LC Relay VSR Upscale 🧩\n\n**What is it?**\n- A one-step upscaler for video and pictures (2x or 4x). The model, VAE and sampler are built in. Fast, sharp, and it adds real skin and hair detail.\n- A fraction of the time VOSR2 or SeedVR2 takes, and higher quality.\n- **detect_faces** finds the faces on its own and holds back Relay's grain on them, so skin keeps its look.\n- 💡 If you used detailers or want all skin protected, wire a mask into **protect_mask**. It overrides detect_faces.\n\n- ✋ **First use downloads the models:** 5.2 GB per upscale size (10 GB for bf16) plus a 128 MB VAE, and 55 MB for the face finder. A toast tells you it started. Press Stop to cancel. Already have them? They are found wherever they are.\n\n---\n\n### Videos 🎥\n\n- **2x faces on** is the default. Your eye goes straight to the faces when things are moving, and everything else is background noise.\n - Use **faces off** only when time or file size is the problem.\n- **4x** runs on an 8 GB card, but on a moving video it looks barely different from 2x. Extra sharpness can actually make it look worse.\n- Save with **LC Save Video**'s **relay** input. It saves a few frames at a time, so the upscaled video never sits in RAM (5 s at 4x: 0.3 GB instead of 12 GB).\n - Leave the Relay node's **image** output empty, or the full video gets built anyway.\n\n| Video | 2x faces on | 2x faces off | 4x faces on | 4x faces off |\n|---|---|---|---|---|\n| 960 x 544, 5 s, 24 fps | 13 s (est.) | 12 s | 36 s (est.) | 34 s |\n| 896 x 1152, 6.6 s, 24 fps | 26 s (est.) | 22 - 24 s | 78 s (est.) | 72 - 75 s |\n| 832 x 1056, 15 s, 48 fps | 46 s | 40 s | 2.7 min | 2.6 min |\n| **8 GB laptop:** 960 x 544, 5 s, 24 fps | 28 s (est.) | 27 s | 2 min (est.) | 118 s |\n| **8 GB laptop:** 832 x 1056, 15 s, 48 fps | 4 min | 3.9 min | 18 min (est.) | 17 min (est.) |\n\n\\* RTX 5090 unless noted, laptop is an RTX 5060 8 GB. Models already downloaded. Faces on finds the faces every 3rd frame and costs about 4 - 15 % (measured on the 15 s clip). (est.) rows are scaled from the measured ones.\n- 💡 VRAM stays flat on 8 GB cards (7.4 GB at 2x), so long clips do not slow to a crawl. The 5090 used 13 GB at 2x and 24 GB at 4x only because it had it free.\n- ⚠️ **16 GB RAM:** the frames sit in RAM before Relay even starts. The 15 s, 48 fps clip peaked at 18.5 GB.\n - Keep clips short, or upscale **before** frame interpolation (half the frames, half the RAM). Recommended, not tested.\n\n---\n\n### Pictures 🖼️\n\n- People actually zoom in on a picture, so 4x is worth it here. Faces cost nothing on a picture (about 4 s either way at 4x, 6 s for a 1056 x 1320 picture).\n- **Quality order:**\n - 4x faces on\n - 2x faces on\n - 4x faces off\n - 2x faces off\n- **Realism and photos:** faces on.\n- **Lineart, anime, painted, etc.:** depends on the face.\n - Smooth or painted skin: faces on (the grain is just noise there).\n - Freckles, hatching or other drawn detail: faces off, or that detail goes soft.\n- Small faces in a crowd: on or off makes no visible difference.\n\n**💡 Tips & tricks**\n- Adjust how much the faces are protected with **mask_protect_strength**.\n- Adjust the sharpness with **detail**.\n\n---\n\n### Settings ⚙️\n\n- **source:**\n - **video:** the frames are matched to each other, so the video stays steady. Use it for anything that came from a video.\n - **image:** every picture is upscaled on its own, so nothing carries over between them. Use it for a batch of different pictures. One picture comes out the same either way.\n- **upscale:** 2x for video, 4x for pictures. Each one is its own model.\n - **2x bf16 / 4x bf16 (10 GB):** the original release. It's an option, but not noticeably different, and it uses about 5 GB more VRAM.\n - Quality: practically identical. The average difference is 0.12 - 0.21 levels out of 255, and the 100% crops can't be told apart.\n - 4x on a 5090: pictures 4 s either way (10 - 11 GB vs 15 - 17 GB VRAM), a 3 s clip 22 s vs 25 s (19 GB vs 24 GB).\n- **detail:** 1.0 is the full model output. Lower moves toward a plain upscale.\n- **detect_faces:** typically on. Off for drawn faces with freckles or hatching.\n- **mask_protect_strength:** how much of Relay's detail still lands on the face. 0.5 is the default. Raise it to keep more texture on stylized faces.\n\n---\n\n### Manual download 📂 (optional)\n\n- The node downloads these on first use. Grab them yourself if you'd rather not wait mid-run.\n\n- [Relay 2x model](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors?download=true)\n\n- [Relay 2x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x2_fp32.safetensors?download=true)\n\n- [Relay 4x model](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/diffusion_models/relayvsr_x4_int8_convrot.safetensors?download=true)\n\n- [Relay 4x VAE](https://huggingface.co/Comfy-Org/RelayVSR/resolve/main/vae/relayvsr_flash_vae_x4_fp32.safetensors?download=true)\n\n- [Relay 4x bf16 model (optional, 10 GB)](https://huggingface.co/kopper/RelayVSR/resolve/main/wan.safetensors?download=true)\n\n- [Relay 2x bf16 model (optional, 10 GB)](https://huggingface.co/lightx2v/RelayVSR_X2/resolve/main/wan_x2.safetensors?download=true)\n\n```\nComfyUI/\n└── models/\n ├── diffusion_models/\n │ ├── relayvsr_x2_lightx2v_int8_convrot (5.2 GB)\n │ ├── relayvsr_x4_int8_convrot (5.2 GB)\n │ ├── wan_x2 (2x bf16, optional, 10 GB)\n │ └── wan (4x bf16, optional, 10 GB)\n ├── vae/\n │ ├── relayvsr_flash_vae_x2_fp32\n │ └── relayvsr_flash_vae_x4_fp32\n └── facexlib/\n ├── detection_mobilenet0.25_Final.pth\n └── parsing_bisenet.pth\n```"
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