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MGHerder's WanExpress Workflow
MGHerder's WanExpress Workflow
MGHerder's WanExpress Workflow
This is intended as a beginner's Wan 2.2 workflow. It's got a simple design and makes it easy to get started. The documentation is all included in the workflow on the left side.About Version 1.2:I made some small tweaks,…
视频
96 个节点 430 次使用 MGHerder 206 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。
Wan2.2 Express v1.2 MGH.json
模型库暂未收录
节点构成
GetNode
× 11
SetNode
× 9
ComfyMathExpression
× 8
mxSlider
× 7
MarkdownNote
× 5
Power Lora Loader (rgthree)
× 4
VHS_VideoCombine
× 3
PrimitiveStringMultiline
× 3
RIFE VFI
× 2
KSamplerAdvanced
× 2
PrimitiveInt
× 2
Fast Groups Muter (rgthree)
× 2
ModelSamplingSD3
× 2
CLIPTextEncode
× 2
LCAnySwitch
× 2
ImageResizeKJv2
× 2
官方标注的模型
nsfw_wan_umt5-xxl_fp8_scaled.safetensorsrife49.pthumt5_xxl_fp8_e4m3fn_scaled.safetensorswan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_1022.safetensorswan2.2_i2v_high_noise_14B_fp8_scaled.safetensorswan2.2_i2v_low_noise_14B_fp8_scaled.safetensorswan_2.1_vae.safetensors
工作流 JSON
和上面「下载工作流 JSON」是同一份文件。
1{
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3 "revision": 0,
4 "last_node_id": 1290,
5 "last_link_id": 310721,
6 "nodes": [
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9 "type": "ComfyMathExpression",
10 "pos": [
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13 ],
14 "size": [
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16 200
17 ],
18 "flags": {
19 "collapsed": true
20 },
21 "order": 65,
22 "mode": 4,
23 "inputs": [
24 {
25 "label": "a",
26 "name": "values.a",
27 "type": "FLOAT,INT,BOOLEAN",
28 "link": 309864
29 },
30 {
31 "name": "values.b",
32 "shape": 7,
33 "type": "FLOAT,INT,BOOLEAN",
34 "link": null
35 },
36 {
37 "label": "c",
38 "name": "values.c",
39 "shape": 7,
40 "type": "FLOAT,INT,BOOLEAN",
41 "link": null
42 }
43 ],
44 "outputs": [
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46 "name": "FLOAT",
47 "type": "FLOAT",
48 "links": null
49 },
50 {
51 "name": "INT",
52 "type": "INT",
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54 309871
55 ]
56 },
57 {
58 "name": "BOOL",
59 "type": "BOOLEAN",
60 "links": null
61 }
62 ],
63 "title": "64 fps math",
64 "properties": {
65 "cnr_id": "comfy-core",
66 "ver": "0.25.1",
67 "Node name for S&R": "ComfyMathExpression",
68 "ue_properties": {
69 "widget_ue_connectable": [],
70 "input_ue_unconnectable": [],
71 "version": "7.8"
72 }
73 },
74 "widgets_values": [
75 "a * 2"
76 ],
77 "widgets_values_named": {
78 "expression": "a * 2"
79 }
80 },
81 {
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83 "type": "ComfyMathExpression",
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87 ],
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101 "type": "FLOAT,INT,BOOLEAN",
102 "link": 309874
103 },
104 {
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106 "shape": 7,
107 "type": "FLOAT,INT,BOOLEAN",
108 "link": null
109 },
110 {
111 "label": "c",
112 "name": "values.c",
113 "shape": 7,
114 "type": "FLOAT,INT,BOOLEAN",
115 "link": null
116 }
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118 "outputs": [
119 {
120 "name": "FLOAT",
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124 ]
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126 {
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131 {
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138 "properties": {
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142 "ue_properties": {
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183 },
184 {
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188 "type": "FLOAT,INT,BOOLEAN",
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192 "outputs": [
193 {
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199 },
200 {
201 "name": "INT",
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205 {
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207 "type": "BOOLEAN",
208 "links": null
209 }
210 ],
211 "title": "framerate set",
212 "properties": {
213 "cnr_id": "comfy-core",
214 "ver": "0.25.1",
215 "Node name for S&R": "ComfyMathExpression",
216 "ue_properties": {
217 "widget_ue_connectable": [],
218 "input_ue_unconnectable": [],
219 "version": "7.8"
220 }
221 },
222 "widgets_values": [
223 "a * 2"
224 ],
225 "widgets_values_named": {
226 "expression": "a * 2"
227 }
228 },
229 {
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231 "type": "RIFE VFI",
232 "pos": [
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235 ],
236 "size": [
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239 ],
240 "flags": {
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242 },
243 "order": 89,
244 "mode": 0,
245 "inputs": [
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247 "name": "frames",
248 "type": "IMAGE",
249 "link": 310486
250 },
251 {
252 "name": "optional_interpolation_states",
253 "shape": 7,
254 "type": "INTERPOLATION_STATES",
255 "link": null
256 },
257 {
258 "name": "multiplier",
259 "type": "INT",
260 "widget": {
261 "name": "multiplier"
262 },
263 "link": 309900
264 }
265 ],
266 "outputs": [
267 {
268 "name": "IMAGE",
269 "type": "IMAGE",
270 "slot_index": 0,
271 "links": [
272 309921
273 ]
274 }
275 ],
276 "title": "Frame Interpolation (RIFE)",
277 "properties": {
278 "cnr_id": "comfyui-frame-interpolation",
279 "ver": "1.0.6",
280 "Node name for S&R": "RIFE VFI",
281 "ue_properties": {
282 "version": "7.0.1",
283 "widget_ue_connectable": [],
284 "input_ue_unconnectable": []
285 }
286 },
287 "widgets_values": [
288 "rife49.pth",
289 32,
290 2,
291 false,
292 true,
293 1,
294 "float32",
295 false,
296 1
297 ],
298 "widgets_values_named": {
299 "ckpt_name": "rife49.pth",
300 "clear_cache_after_n_frames": 32,
301 "multiplier": 2,
302 "fast_mode": false,
303 "ensemble": true,
304 "scale_factor": 1,
305 "dtype": "float32",
306 "torch_compile": false,
307 "batch_size": 1
308 },
309 "shape": 1
310 },
311 {
312 "id": 848,
313 "type": "GetNode",
314 "pos": [
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316 1060
317 ],
318 "size": [
319 210,
320 50
321 ],
322 "flags": {
323 "collapsed": true
324 },
325 "order": 0,
326 "mode": 0,
327 "inputs": [],
328 "outputs": [
329 {
330 "name": "FLOAT",
331 "type": "FLOAT",
332 "links": [
333 309902
334 ]
335 }
336 ],
337 "title": "Get_upscale_factor",
338 "properties": {
339 "Node name for S&R": "GetNode",
340 "aux_id": "GetNode",
341 "ue_properties": {
342 "widget_ue_connectable": [],
343 "version": "7.7",
344 "input_ue_unconnectable": []
345 }
346 },
347 "widgets_values": [
348 "upscale_factor"
349 ],
350 "widgets_values_named": {
351 "Constant": "upscale_factor"
352 },
353 "color": "#232",
354 "bgcolor": "#353"
355 },
356 {
357 "id": 815,
358 "type": "ImageScaleBy",
359 "pos": [
360 1580,
361 980
362 ],
363 "size": [
364 270,
365 90
366 ],
367 "flags": [],
368 "order": 86,
369 "mode": 0,
370 "inputs": [
371 {
372 "name": "image",
373 "type": "IMAGE",
374 "link": 310700
375 },
376 {
377 "name": "scale_by",
378 "type": "FLOAT",
379 "widget": {
380 "name": "scale_by"
381 },
382 "link": 309902
383 }
384 ],
385 "outputs": [
386 {
387 "name": "IMAGE",
388 "type": "IMAGE",
389 "links": [
390 310485,
391 310486,
392 310487,
393 310488
394 ]
395 }
396 ],
397 "properties": {
398 "cnr_id": "comfy-core",
399 "ver": "0.25.1",
400 "Node name for S&R": "ImageScaleBy",
401 "ue_properties": {
402 "widget_ue_connectable": [],
403 "input_ue_unconnectable": [],
404 "version": "7.8"
405 }
406 },
407 "widgets_values": [
408 "lanczos",
409 1.5
410 ],
411 "widgets_values_named": {
412 "upscale_method": "lanczos",
413 "scale_by": 1.5
414 }
415 },
416 {
417 "id": 178,
418 "type": "KSamplerAdvanced",
419 "pos": [
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421 1040
422 ],
423 "size": [
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425 800
426 ],
427 "flags": {
428 "collapsed": true
429 },
430 "order": 83,
431 "mode": 0,
432 "inputs": [
433 {
434 "name": "model",
435 "type": "MODEL",
436 "link": 297
437 },
438 {
439 "name": "positive",
440 "type": "CONDITIONING",
441 "link": 298
442 },
443 {
444 "name": "negative",
445 "type": "CONDITIONING",
446 "link": 299
447 },
448 {
449 "name": "latent_image",
450 "type": "LATENT",
451 "link": 306
452 },
453 {
454 "name": "noise_seed",
455 "type": "INT",
456 "widget": {
457 "name": "noise_seed"
458 },
459 "link": 310716
460 },
461 {
462 "name": "cfg",
463 "type": "FLOAT",
464 "widget": {
465 "name": "cfg"
466 },
467 "link": 309820
468 },
469 {
470 "name": "sampler_name",
471 "type": "COMBO",
472 "widget": {
473 "name": "sampler_name"
474 },
475 "link": 310718
476 },
477 {
478 "name": "scheduler",
479 "type": "COMBO",
480 "widget": {
481 "name": "scheduler"
482 },
483 "link": 310720
484 }
485 ],
486 "outputs": [
487 {
488 "name": "LATENT",
489 "type": "LATENT",
490 "links": [
491 301
492 ]
493 }
494 ],
495 "title": "KSampler (Low)",
496 "properties": {
497 "cnr_id": "comfy-core",
498 "ver": "0.13.0",
499 "Node name for S&R": "KSamplerAdvanced",
500 "ue_properties": {
501 "widget_ue_connectable": [],
502 "version": "7.8",
503 "input_ue_unconnectable": []
504 }
505 },
506 "widgets_values": [
507 "disable",
508 288061972799101,
509 "fixed",
510 8,
511 1,
512 "uni_pc",
513 "beta",
514 4,
515 8,
516 "disable"
517 ],
518 "widgets_values_named": {
519 "add_noise": "disable",
520 "noise_seed": 288061972799101,
521 "control_after_generate": "fixed",
522 "steps": 8,
523 "cfg": 1,
524 "sampler_name": "uni_pc",
525 "scheduler": "beta",
526 "start_at_step": 4,
527 "end_at_step": 8,
528 "return_with_leftover_noise": "disable"
529 },
530 "color": "#223",
531 "bgcolor": "#335"
532 },
533 {
534 "id": 834,
535 "type": "ComfyMathExpression",
536 "pos": [
537 1950,
538 120
539 ],
540 "size": [
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542 200
543 ],
544 "flags": {
545 "collapsed": true
546 },
547 "order": 49,
548 "mode": 4,
549 "inputs": [
550 {
551 "label": "a",
552 "name": "values.a",
553 "type": "FLOAT,INT,BOOLEAN",
554 "link": 309883
555 },
556 {
557 "name": "values.b",
558 "shape": 7,
559 "type": "FLOAT,INT,BOOLEAN",
560 "link": null
561 },
562 {
563 "label": "c",
564 "name": "values.c",
565 "shape": 7,
566 "type": "FLOAT,INT,BOOLEAN",
567 "link": null
568 }
569 ],
570 "outputs": [
571 {
572 "name": "FLOAT",
573 "type": "FLOAT",
574 "links": [
575 309924
576 ]
577 },
578 {
579 "name": "INT",
580 "type": "INT",
581 "links": []
582 },
583 {
584 "name": "BOOL",
585 "type": "BOOLEAN",
586 "links": null
587 }
588 ],
589 "title": "base frame only",
590 "properties": {
591 "cnr_id": "comfy-core",
592 "ver": "0.25.1",
593 "Node name for S&R": "ComfyMathExpression",
594 "ue_properties": {
595 "widget_ue_connectable": [],
596 "input_ue_unconnectable": [],
597 "version": "7.8"
598 }
599 },
600 "widgets_values": [
601 "a"
602 ],
603 "widgets_values_named": {
604 "expression": "a"
605 }
606 },
607 {
608 "id": 830,
609 "type": "GetNode",
610 "pos": [
611 2990,
612 -90
613 ],
614 "size": [
615 210,
616 60
617 ],
618 "flags": {
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620 },
621 "order": 1,
622 "mode": 0,
623 "inputs": [],
624 "outputs": [
625 {
626 "name": "INT",
627 "type": "INT",
628 "links": [
629 309895
630 ]
631 }
632 ],
633 "title": "Get_total_frames",
634 "properties": {
635 "Node name for S&R": "GetNode",
636 "aux_id": "GetNode",
637 "ue_properties": {
638 "widget_ue_connectable": [],
639 "version": "7.7",
640 "input_ue_unconnectable": []
641 }
642 },
643 "widgets_values": [
644 "total_frames"
645 ],
646 "widgets_values_named": {
647 "Constant": "total_frames"
648 },
649 "color": "#1b4669",
650 "bgcolor": "#29699c"
651 },
652 {
653 "id": 90,
654 "type": "VAELoader",
655 "pos": [
656 -1110,
657 240
658 ],
659 "size": [
660 520,
661 110
662 ],
663 "flags": [],
664 "order": 2,
665 "mode": 0,
666 "inputs": [],
667 "outputs": [
668 {
669 "name": "VAE",
670 "type": "VAE",
671 "slot_index": 0,
672 "links": [
673 185,
674 281
675 ]
676 }
677 ],
678 "properties": {
679 "cnr_id": "comfy-core",
680 "ver": "0.3.45",
681 "Node name for S&R": "VAELoader",
682 "models": [
683 {
684 "name": "wan_2.1_vae.safetensors",
685 "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors",
686 "directory": "vae"
687 }
688 ],
689 "ue_properties": {
690 "widget_ue_connectable": [],
691 "version": "7.1",
692 "input_ue_unconnectable": []
693 }
694 },
695 "widgets_values": [
696 "wan_2.1_vae.safetensors"
697 ],
698 "widgets_values_named": {
699 "vae_name": "wan_2.1_vae.safetensors"
700 }
701 },
702 {
703 "id": 214,
704 "type": "PrimitiveInt",
705 "pos": [
706 2650,
707 -220
708 ],
709 "size": [
710 270,
711 90
712 ],
713 "flags": {
714 "collapsed": true
715 },
716 "order": 50,
717 "mode": 0,
718 "inputs": [
719 {
720 "name": "value",
721 "type": "INT",
722 "widget": {
723 "name": "value"
724 },
725 "link": 309931
726 }
727 ],
728 "outputs": [
729 {
730 "name": "INT",
731 "type": "INT",
732 "links": [
733 309864,
734 309900
735 ]
736 }
737 ],
738 "title": "Base Interpolation Factor",
739 "properties": {
740 "cnr_id": "comfy-core",
741 "ver": "0.25.1",
742 "Node name for S&R": "PrimitiveInt",
743 "ue_properties": {
744 "widget_ue_connectable": [],
745 "input_ue_unconnectable": [],
746 "version": "7.8"
747 }
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749 "widgets_values": [
750 2,
751 "fixed"
752 ],
753 "widgets_values_named": {
754 "value": 2,
755 "control_after_generate": "fixed"
756 }
757 },
758 {
759 "id": 875,
760 "type": "ImpactControlBridge",
761 "pos": [
762 3250,
763 0
764 ],
765 "size": [
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767 90
768 ],
769 "flags": {
770 "collapsed": true
771 },
772 "order": 91,
773 "mode": 0,
774 "inputs": [
775 {
776 "label": "whatever enters the next node",
777 "name": "value",
778 "type": "IMAGE",
779 "link": 310488
780 },
781 {
782 "name": "mode",
783 "type": "BOOLEAN",
784 "widget": {
785 "name": "mode"
786 },
787 "link": 309926
788 }
789 ],
790 "outputs": [
791 {
792 "label": "IMAGE",
793 "name": "value",
794 "type": "IMAGE",
795 "links": [
796 309928
797 ]
798 }
799 ],
800 "properties": {
801 "cnr_id": "comfyui-impact-pack",
802 "ver": "429d0159ad429e64d2b3916e6e7be9c22d025c3c",
803 "Node name for S&R": "ImpactControlBridge",
804 "ue_properties": {
805 "widget_ue_connectable": [],
806 "input_ue_unconnectable": [],
807 "version": "7.8"
808 }
809 },
810 "widgets_values": [
811 true,
812 "Mute"
813 ],
814 "widgets_values_named": {
815 "mode": true,
816 "behavior": "Mute"
817 },
818 "color": "#222",
819 "bgcolor": "#000"
820 },
821 {
822 "id": 166,
823 "type": "MarkdownNote",
824 "pos": [
825 -1840,
826 380
827 ],
828 "size": [
829 330,
830 220
831 ],
832 "flags": {
833 "collapsed": false
834 },
835 "order": 3,
836 "mode": 0,
837 "inputs": [],
838 "outputs": [],
839 "title": "⚙️ THE \"TUNING & FIXES\" NOTE",
840 "properties": {
841 "ue_properties": {
842 "widget_ue_connectable": [],
843 "version": "7.1",
844 "input_ue_unconnectable": []
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846 },
847 "widgets_values": [
848 "🔧 TUNING & TROUBLESHOOTING\n--------------------------------------------------\n[BURN PREVENTION]\nIf images look \"Deep Fried\" or Neon Purple:\n1. Check CFG: Is it above 2.5? -> Lower to 2.5.\n2. Check LoRAs: Are they 0.7+? -> Lower to 0.5.\n\n[SPEED]\nLowering CFG to 1 cuts the generation time in half but quality may be impacted: Negative prompts are ignored. This may be a worthwhile tradeoff.\n\n[EFFICIENCY]\nYou can generate something at 32fps, if you like it you can freeze the seed and select 64fps and get a higher fps almost instantly -- but this assumes NO CHANGES to LoRAs, generated resolution, or anything else on the input side of the KSampler. Don't even bother with the Neverending upscale unless you know you like what is in the pipeline.\n\n[STEP CONTINUITY]\nEnsure Sampler 1 \"End_at_step\" MATCHES Sampler 2 \"Start_at_step\".\nEnsure Sampler 2 \"End_at_step\" MATCHES \"Total Steps\".\n*Gap in steps = Blurry Soup.*\n\n[LIGHTNING LORAS]\nUsually required on WAN 2.2. No lightning LoRAs can lead to low detail/blurry results.\nWarning: Some checkpoints already have lightning baked in → applying extra LoRAs causes overcooking. Test with and without.\n\n[GPU SAMPLERS]\nAvoid '_gpu' variants (e.g., dpmpp_2m_sde_gpu).\nThey add instability with no speed gain on this model.\n\nMy go-to settings:\nEuler A / Simple\nUniPC / Beta\n"
849 ],
850 "widgets_values_named": {
851 "text": "🔧 TUNING & TROUBLESHOOTING\n--------------------------------------------------\n[BURN PREVENTION]\nIf images look \"Deep Fried\" or Neon Purple:\n1. Check CFG: Is it above 2.5? -> Lower to 2.5.\n2. Check LoRAs: Are they 0.7+? -> Lower to 0.5.\n\n[SPEED]\nLowering CFG to 1 cuts the generation time in half but quality may be impacted: Negative prompts are ignored. This may be a worthwhile tradeoff.\n\n[EFFICIENCY]\nYou can generate something at 32fps, if you like it you can freeze the seed and select 64fps and get a higher fps almost instantly -- but this assumes NO CHANGES to LoRAs, generated resolution, or anything else on the input side of the KSampler. Don't even bother with the Neverending upscale unless you know you like what is in the pipeline.\n\n[STEP CONTINUITY]\nEnsure Sampler 1 \"End_at_step\" MATCHES Sampler 2 \"Start_at_step\".\nEnsure Sampler 2 \"End_at_step\" MATCHES \"Total Steps\".\n*Gap in steps = Blurry Soup.*\n\n[LIGHTNING LORAS]\nUsually required on WAN 2.2. No lightning LoRAs can lead to low detail/blurry results.\nWarning: Some checkpoints already have lightning baked in → applying extra LoRAs causes overcooking. Test with and without.\n\n[GPU SAMPLERS]\nAvoid '_gpu' variants (e.g., dpmpp_2m_sde_gpu).\nThey add instability with no speed gain on this model.\n\nMy go-to settings:\nEuler A / Simple\nUniPC / Beta\n"
852 },
853 "color": "#222",
854 "bgcolor": "#000"
855 },
856 {
857 "id": 867,
858 "type": "MarkdownNote",
859 "pos": [
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861 650
862 ],
863 "size": [
864 680,
865 320
866 ],
867 "flags": {
868 "collapsed": false
869 },
870 "order": 4,
871 "mode": 0,
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873 "outputs": [],
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883 "**CREATE LONGER SLO-MOTION VIDEOS BY INTERPOLATION**\n\n- This slider tweaks the base interpolation factor, while leaving the frame generation the same. A native 16 fps video is normally interpolated by a multiple of 2 in order to reach 32 fps, or 4 for 64 fps. But if you pack even more frames into the same fps, then the result is a longer video, in slow motion. \n\n- The default setting is 2. This effectively cancels out Slo-Mo.\n\n- It is used to calculate the correct total frame count and later converted to a float for the Create Video node.\n\n- Magic Slo-Mo is not available in 16 fps mode.\n\n**TIP: Creative Slow-Motion & Extended Duration**\nYou can increase this value (currently set to 2) to 4, 6, or even 8 **without regenerating the base video** (just freeze your seed).\n\n4 = half speed slo-mo\n6 = 1/3 speed slo-mo\n8 = 1/4 speed slo-mo\n\n**Examples (5-second clip = 81 base frames):**\n- Multiplier **2** @ 32 fps → 162 frames = **5 seconds** normal speed\n- Multiplier **4** @ 32 fps → 324 frames = **10 seconds** slow-motion\n- Multiplier **6** @ 32 fps → 486 frames = **15 seconds** slow-motion\n- Multiplier **4** @ 64 fps → 641 frames = **10 seconds** slow-motion\n\nHigher multipliers let you turn short generations into much longer, smoother slow-motion clips using only the interpolation stage (much faster than re-generating everything).\n\n**Note:** Very high multipliers (8+) increase processing time and file size significantly, with diminishing returns on smoothness.\n\nWhile interpolating, you may see Comfy-VFI will announce clearing cache every 32 frames. This should only appear a few times (twice for a 5 second clip)."
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2008 "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2)\n\n<h2>Required Models and Downloads</h2>\n\n**Clicking the links below will auto download if you are logged in to CivitAI/HuggingFace**\n\n**Diffusion Model (fp8_scaled - what this workflow uses)**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**Lightning LoRAs (4-step distill - turn off when using DaSiWa LightSpeed etc)**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder**\n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n**File save location**\nComfyUI/\n\n📂 models/diffusion_models/\n\nwan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n\nwan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n\n📂 models/loras/\n\nwan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors\n\nwan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n\n\n📂 models/text_encoders/\n\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n\n📂 models/vae/\n\nwan_2.1_vae.safetensors (wan 2.2 uses the wan 2.1 vae)\n"
2009 ],
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2011 "text": "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2)\n\n<h2>Required Models and Downloads</h2>\n\n**Clicking the links below will auto download if you are logged in to CivitAI/HuggingFace**\n\n**Diffusion Model (fp8_scaled - what this workflow uses)**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**Lightning LoRAs (4-step distill - turn off when using DaSiWa LightSpeed etc)**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder**\n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n**File save location**\nComfyUI/\n\n📂 models/diffusion_models/\n\nwan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n\nwan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n\n📂 models/loras/\n\nwan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors\n\nwan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n\n\n📂 models/text_encoders/\n\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n\n📂 models/vae/\n\nwan_2.1_vae.safetensors (wan 2.2 uses the wan 2.1 vae)\n"
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2043 "**1. Prompt**\n\nYou may wish to copy the inputs of the best videos you find. Keep in mind that certain prompts will work better with certain LoRAs and results are never guaranteed.\nMany use an LLM to help construct detailed prompts. Generate your descriptive prompt there, then paste the resulting text directly into your positive prompt node.\n\n\n**2. Negative Prompt**\n\nThis can be completely ignored if CFG is set to 1. Negative prompts are sometimes helpful at CFG of 2-2.5, particularly for addressing issues like walking backwards. Longer negative prompts can confuse the model, leading to hesitation, so use negative prompts wisely and if possible, sparingly.\n\n**3. Image Input**\n\nPaste or load your starting image into the `Load Image` node below to initialize the Image-to-Video generation.\n\n**4. Prompt Metadata **\n\nThe 0.9 version of this workflow had other means of tracing your prompt. Now, the workflow metadata is embedded in the lastframe. Outputs are thus limited to This workflow features two ways of saving your prompts for later reference. One way is to simply concatenate your seed + prompt into one text file. This is the space-saver version but can be difficult to sort through. The other way saves an image with the seed and a text file that goes with it, which can function as a thumbnail, visually helping you to find what you were after. By default, it saves the last frame of the video generation."
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2263 "This workflow captures frames after upscale but before interpolation. It will capture the last few frames as designated by the slider in Step 3. these frames will be spaced according to the original 16fps generation. So if you capture 8 frames, that is half a second, and those lastframes will amount to the last .5 seconds of your clip, regardless of which fps setting you used. If you don't want to save a last frame, use the muter. Setting the number of frames to 0 will cause errors!"
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3145 "This workflow is optimized for **fast, high-quality Image-to-Video** generation using official Wan2.2 fp8 models. It’s designed to get you from image → prompt → 5-second video with minimal hassle.\n\nFollow the **Step** groups in order.\n\n---\n\n## Step 1: Load Models (Top Left Group)\n\n- **Diffusion Models**: `wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors` + `wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors`\n- **Lightning LoRAs**: Kept separate so you don't accidentally mess with them.\n- **Power LoRA Loader**: Keep your catalog of LoRAs here. Note that for the official WAN2.2 setup, there is no explicit content support so you will need to rely on LoRAs to animate explicit content effectively. Remember to keep \"High + Low LoRAs\" separate. Recommended starting strength: **0.85 – 0.95**.\n- **Text Encoder**: `umt5_xxl_fp8_e4m3fn_scaled.safetensors`\n- **VAE**: `wan_2.1_vae.safetensors`\n\n**What to do:**\n- Just make sure the paths are correct (they should auto-load if files are in the right folders). If not, verify, refresh the browser, and use the drop down menus to select the model. Once Step 1 is correct, you only need to revisit it when you change models.\n\n---\n\n## Step 2: Upload Your Starting Image\nPaste an image, drag and drop from your Image Feed, or click to load one. 3:4 portrait images generally work very well. Make it a good one, beware of mostly obscured objects that might cause the model to tend to hallucinate.\n\n---\n\n## Step 3: Choose Video Generation Settings\nUse the controls in the **Step3 - Video Size and Length** group:\n\n- **CFG** — Set to 1 for fast generation or experiment with up to 2.5. Higher CFG can produce better results but also lead to burned out or overexposed images.\n- **\"Select FPS Output\"** Fast Groups Bypasser. Click the bypasser switch to obtain the frame rate you want:\n - **16 fps** → Fastest, bypasses interpolation\n - **32 fps** → Good balance (uses RIFE ×2)\n - **64 fps** → Smoothest (uses RIFE ×4)\n\n- Select **Image Scaling** or **Form Factor Adjust** — It automatically rounds to the nearest multiple of 64 pixels if you scale. The 2d slider is likewise restricted to multiples of 64.\n- **Clip Length (Sec)** — Set how long you want the final video to be (1–10 seconds).\n- **Images to Save (Lastframe)** — Choose how many frames from the end of the clip to export as still images (contains the workflow used, and makes starting points for chained video clips). The images saved are taken from the original 16fps generation, before interpolation, so saving 8 frames = the last .5 seconds of the clip whether 16fps or 64fps. **I recommend at least 8 frames if you are planning to string clips together, in case the subject starts doing things that will create an awkward starting point.**\n- **Upscale Factor** — Light post-upscale (1.5 is recommended). Quality gain is modest. \n- **Sigma Shift** — Default 5. 4-6 for less motion, 6-8 for more dynamic videos. Treat adjusting this setting as experimental, and return it to 5 for everyday unless you are sure you know what you are doing.\n- **Magic Slo-Mo** — See detailed description below. `2` = 'normal speed', `4` = ~2× slower `6` = ~3× slower. As with all interpolation, benefit/smoothness decrease when movement is overly rapid.\n\n---\n\n## Step 4: Write Your Prompt\n- Use the large **Positive Prompt** box.\n- Be descriptive about motion, camera, expression, and timing.\n- The **Negative Prompt** is already decent — only add things if you’re getting specific problems.\n\n**Pro tip:** Treat your characters like actors. Describe what they *do* over time, instill emotion and motivations.\n\n---\n\n## Step 5: Finalize LoRA Selection\n- While the base WAN model offers plenty of amazing effects and possibilities, it does need LoRA support in many instances. Selecting loras and strengths is a key part of getting good results.\n\n---\n\n## Queue the prompt\n(Ctrl/Cmd + Enter, or hit Run).\n\n**What gets generated:**\n- Videos at 16 FPS (then upscaled and then interpolated to your chosen FPS)\n- Lastframe images (saved to the Lastframe folder)\n- All processing is staged: High-noise pass → Low-noise refinement → Optional upscale → RIFE frame interpolation\n- By default all outputs are saved to ComfyUI/Output/WanExpress inside of a folder named by date, along with the source image and the Lastframe image.\n\n---\n\n## Quick Tuning Recommendations\nSee the separate **⚙️ TUNING & FIXES NOTE** for full details. Common good starting points:\n\n- **CFG**: 1.0 – 2.5 (lower = faster, higher = more prompt adherence)\n- **Lightning LoRAs**: 0.85 – 0.95\n- **Total Steps**: 8 (4 high + 4 low)\n- Keep **Step Continuity** rules: High sampler End_at_step must match Low sampler Start_at_step\n\n**Burn Prevention:** If things look overcooked or neon, lower CFG first, then LoRA strengths.\n\n---\n\nThat’s it! Load → Image → Video Settings → Prompt → LoRAs → Generate.\n\nHappy creating!"
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