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工作流 / 图像 / Qwen 2.1 Character Sheet Builder - No LLM
Qwen 2.1 Character Sheet Builder - No LLM

Qwen 2.1 Character Sheet Builder - No LLM

Qwen 2.1 Character Sheet Builder - No LLM

Version 3.0 - Basic/Advanced - Now With Even Less LLM!Version 3.0 is out! It's basically the same as the others with a few minor QoL adjustments and (most importantly) it's tailored for my new Angle Lora for Q21.Instruct…

图像 37 个节点 3,891 次使用 AirbagGuy 275 KB

下载工作流 JSON 查看官方来源

下载后把文件拖进 ComfyUI 窗口即可打开。

需要的权重

这个工作流引用了 2 个权重文件，其中 0 个能在本站模型库里找到。

Batched - Q21 Character Sheet Builder 3.2 - Advanced - Lora.json 模型库暂未收录
Unbatched - Q21 Character Sheet Builder 3.3 - Advanced - Lora.json 模型库暂未收录

节点构成

PreviewImage × 7
PrimitiveStringMultiline × 6
LoadImage × 4
Note × 2
easy promptLine × 2
1af4e9a8-bd04-4dba-aaae-ae4f3708b2ea × 1
ab6deaa0-2c52-44b5-b6a8-b17645e9adac × 1
17d3f5c1-a942-40c9-9fbb-0c435aa5e534 × 1
ImageScaleToTotalPixels × 1
56fced51-f034-4655-a066-01d7a829b441 × 1
38db5823-e237-405c-87aa-530100627f15 × 1
75743969-eb61-4851-9132-5359ba9ef5c2 × 1
be4ba64b-746c-4250-b5e9-034e232e9e9e × 1
PrimitiveString × 1
b864ec23-bb03-4a70-85ce-bc713e561fb5 × 1
8c99e872-61b0-4c34-b216-c160dce4a273 × 1

官方标注的模型

Airbag_V2.safetensorsQ21_Angle_512_V1.safetensorsQwen-Image-2.1-viggle-turbo-v0.3-6step-lora-r256.safetensorsminimax_h3_ref2va_int8_convrot.safetensorsminimax_h3_ref2va_pruned_int8_convrot.safetensorsminimax_h3_video_vae_fp16.safetensorsminimax_h3_video_vae_int8_convrot.safetensorsqwen3vl_32b_minimax_h3_int8_convrot.safetensorsqwen3vl_32b_minimax_h3_nvfp4_awq.safetensorsqwen3vl_8b_int8_convrot.safetensorsqwen_image_2.1_int8_convrot.safetensorstexture_fix_vae_for_qwen_image_2.1_bf16.safetensors

工作流 JSON

和上面「下载工作流 JSON」是同一份文件。

1{
2 "id": "db302fd7-0f86-4fb3-9031-59c125b6f532",
3 "revision": 0,
4 "last_node_id": 1468,
5 "last_link_id": 4878,
6 "nodes": [
7 {
8 "id": 10,
9 "type": "PrimitiveStringMultiline",
10 "pos": [
11 1730,
12 2190
13 ],
14 "size": [
15 430,
16 90
17 ],
18 "flags": [],
19 "order": 0,
20 "mode": 0,
21 "inputs": [
22 {
23 "localized_name": "value",
24 "name": "value",
25 "type": "STRING",
26 "widget": {
27 "name": "value"
28 },
29 "link": null
30 }
31 ],
32 "outputs": [
33 {
34 "localized_name": "STRING",
35 "name": "STRING",
36 "type": "STRING",
37 "links": [
38 2698
39 ]
40 }
41 ],
42 "title": "Style",
43 "properties": {
44 "Node name for S&R": "PrimitiveStringMultiline",
45 "cnr_id": "comfy-core",
46 "ver": "0.36.0"
47 },
48 "widgets_values": [
49 ""
50 ],
51 "widgets_values_named": {
52 "value": ""
53 },
54 "color": "#322",
55 "bgcolor": "#533"
56 },
57 {
58 "id": 14,
59 "type": "LoadImage",
60 "pos": [
61 840,
62 2890
63 ],
64 "size": [
65 290,
66 320
67 ],
68 "flags": [],
69 "order": 1,
70 "mode": 0,
71 "inputs": [
72 {
73 "localized_name": "image",
74 "name": "image",
75 "type": "COMBO",
76 "widget": {
77 "name": "image"
78 },
79 "link": null
80 },
81 {
82 "localized_name": "choose file to upload",
83 "name": "upload",
84 "type": "IMAGEUPLOAD",
85 "widget": {
86 "name": "upload"
87 },
88 "link": null
89 }
90 ],
91 "outputs": [
92 {
93 "localized_name": "IMAGE",
94 "name": "IMAGE",
95 "type": "IMAGE",
96 "links": [
97 1647
98 ]
99 },
100 {
101 "localized_name": "MASK",
102 "name": "MASK",
103 "type": "MASK",
104 "links": null
105 }
106 ],
107 "title": "Primary Image Load",
108 "properties": {
109 "Node name for S&R": "LoadImage",
110 "cnr_id": "comfy-core",
111 "ver": "0.36.0"
112 },
113 "widgets_values": [
114 "Josie_HS_Primary_00002_.png",
115 "image"
116 ],
117 "widgets_values_named": {
118 "image": "Josie_HS_Primary_00002_.png",
119 "upload": "image"
120 },
121 "color": "#233",
122 "bgcolor": "#355"
123 },
124 {
125 "id": 376,
126 "type": "1af4e9a8-bd04-4dba-aaae-ae4f3708b2ea",
127 "pos": [
128 1190,
129 2860
130 ],
131 "size": [
132 600,
133 470
134 ],
135 "flags": [],
136 "order": 2,
137 "mode": 0,
138 "inputs": [
139 {
140 "name": "unet_name",
141 "type": "COMBO",
142 "widget": {
143 "name": "unet_name"
144 },
145 "link": null
146 },
147 {
148 "name": "clip_name",
149 "type": "COMBO",
150 "widget": {
151 "name": "clip_name"
152 },
153 "link": null
154 },
155 {
156 "label": "Vae",
157 "name": "vae_name",
158 "type": "COMBO",
159 "widget": {
160 "name": "vae_name"
161 },
162 "link": null
163 },
164 {
165 "label": "Turbo Lora",
166 "name": "lora_name",
167 "type": "COMBO",
168 "widget": {
169 "name": "lora_name"
170 },
171 "link": null
172 },
173 {
174 "label": "Turbo Lora Strength",
175 "name": "strength_model",
176 "type": "FLOAT",
177 "widget": {
178 "name": "strength_model"
179 },
180 "link": null
181 },
182 {
183 "label": "Angle Lora",
184 "name": "lora_name_1",
185 "type": "COMBO",
186 "widget": {
187 "name": "lora_name_1"
188 },
189 "link": null
190 },
191 {
192 "label": "Angle Lora Strength",
193 "name": "strength_model_1",
194 "type": "FLOAT",
195 "widget": {
196 "name": "strength_model_1"
197 },
198 "link": null
199 },
200 {
201 "label": "Turbo On",
202 "name": "boolean",
203 "type": "BOOLEAN",
204 "widget": {
205 "name": "boolean"
206 },
207 "link": null
208 },
209 {
210 "label": "Manual Simgas On",
211 "name": "boolean_1",
212 "type": "BOOLEAN",
213 "widget": {
214 "name": "boolean_1"
215 },
216 "link": null
217 },
218 {
219 "label": "Manual Sigmas",
220 "name": "sigmas",
221 "type": "STRING",
222 "widget": {
223 "name": "sigmas"
224 },
225 "link": null
226 },
227 {
228 "label": "Scheduler",
229 "name": "scheduler",
230 "type": "COMBO",
231 "widget": {
232 "name": "scheduler"
233 },
234 "link": null
235 },
236 {
237 "label": "Steps",
238 "name": "steps",
239 "type": "INT",
240 "widget": {
241 "name": "steps"
242 },
243 "link": null
244 },
245 {
246 "label": "CFG",
247 "name": "value",
248 "type": "FLOAT",
249 "widget": {
250 "name": "value"
251 },
252 "link": null
253 },
254 {
255 "label": "Sampler",
256 "name": "sampler_name",
257 "type": "COMBO",
258 "widget": {
259 "name": "sampler_name"
260 },
261 "link": null
262 }
263 ],
264 "outputs": [
265 {
266 "localized_name": "MODEL",
267 "name": "MODEL",
268 "type": "MODEL",
269 "links": [
270 1152,
271 4213,
272 4226,
273 4241
274 ]
275 },
276 {
277 "localized_name": "CLIP",
278 "name": "CLIP",
279 "type": "CLIP",
280 "links": [
281 1148,
282 4210,
283 4223,
284 4238
285 ]
286 },
287 {
288 "label": "Vae",
289 "localized_name": "VAE",
290 "name": "VAE",
291 "type": "VAE",
292 "links": [
293 1150,
294 4211,
295 4224,
296 4239
297 ]
298 },
299 {
300 "label": "Sigmas",
301 "name": "*",
302 "type": "*",
303 "links": [
304 4870,
305 4871,
306 4872,
307 4873
308 ]
309 },
310 {
311 "name": "SAMPLER",
312 "type": "SAMPLER",
313 "links": [
314 1839,
315 4217,
316 4231,
317 4245
318 ]
319 },
320 {
321 "label": "CFG",
322 "name": "FLOAT",
323 "type": "FLOAT",
324 "links": [
325 1840,
326 4214,
327 4227,
328 4242
329 ]
330 }
331 ],
332 "properties": {
333 "cnr_id": "comfy-core",
334 "ver": "0.33.0",
335 "previewExposures": []
336 },
337 "widgets_values": [
338 "Qwen 2.1\\qwen_image_2.1_int8_convrot.safetensors",
339 "qwen3vl_8b_int8_convrot.safetensors",
340 "texture_fix_vae_for_qwen_image_2.1_bf16.safetensors",
341 "Holding Pattern\\Qwen-Image-2.1-viggle-turbo-v0.3-6step-lora-r256.safetensors",
342 0.8,
343 "Q21 Holding\\Q21_Angle_512_V1.safetensors",
344 1,
345 true,
346 false,
347 "1, 0",
348 "simple",
349 6,
350 1,
351 "euler"
352 ],
353 "widgets_values_named": {
354 "unet_name": "Qwen 2.1\\qwen_image_2.1_int8_convrot.safetensors",
355 "clip_name": "qwen3vl_8b_int8_convrot.safetensors",
356 "vae_name": "texture_fix_vae_for_qwen_image_2.1_bf16.safetensors",
357 "lora_name": "Holding Pattern\\Qwen-Image-2.1-viggle-turbo-v0.3-6step-lora-r256.safetensors",
358 "strength_model": 0.8,
359 "lora_name_1": "Q21 Holding\\Q21_Angle_512_V1.safetensors",
360 "strength_model_1": 1,
361 "boolean": true,
362 "boolean_1": false,
363 "sigmas": "1, 0",
364 "scheduler": "simple",
365 "steps": 6,
366 "value": 1,
367 "sampler_name": "euler"
368 },
369 "color": "#232",
370 "bgcolor": "#353"
371 },
372 {
373 "id": 404,
374 "type": "ab6deaa0-2c52-44b5-b6a8-b17645e9adac",
375 "pos": [
376 2600,
377 2360
378 ],
379 "size": [
380 290,
381 390
382 ],
383 "flags": [],
384 "order": 19,
385 "mode": 0,
386 "inputs": [
387 {
388 "label": "Primary Run Input Count",
389 "name": "index",
390 "type": "INT",
391 "widget": {
392 "name": "index"
393 },
394 "link": null
395 },
396 {
397 "label": "Alpha Channel (True)",
398 "name": "boolean",
399 "type": "BOOLEAN",
400 "widget": {
401 "name": "boolean"
402 },
403 "link": null
404 },
405 {
406 "label": "Background Color (False Only)",
407 "name": "value",
408 "type": "STRING",
409 "widget": {
410 "name": "value"
411 },
412 "link": null
413 },
414 {
415 "label": "Style",
416 "name": "",
417 "type": "*",
418 "link": 2698
419 },
420 {
421 "label": "Subject Definition",
422 "name": "_1",
423 "type": "*",
424 "link": 3756
425 },
426 {
427 "label": "Head_Notes",
428 "name": "_2",
429 "type": "*",
430 "link": 2700
431 },
432 {
433 "label": "Expression",
434 "name": "_3",
435 "type": "*",
436 "link": 2701
437 },
438 {
439 "label": "Feed Gen Notes",
440 "name": "_5",
441 "type": "*",
442 "link": 3794
443 },
444 {
445 "label": "Behind_Notes",
446 "name": "_4",
447 "type": "*",
448 "link": 3793
449 },
450 {
451 "label": "Main Gen Notes",
452 "name": "_6",
453 "type": "*",
454 "link": 4860
455 }
456 ],
457 "outputs": [
458 {
459 "label": "Feed Run Primary",
460 "name": "STRING",
461 "type": "STRING",
462 "links": null
463 },
464 {
465 "label": "Feed Run Front",
466 "name": "STRING_1",
467 "type": "STRING",
468 "links": [
469 4430
470 ]
471 },
472 {
473 "label": "Feed Run Side",
474 "name": "STRING_2",
475 "type": "STRING",
476 "links": [
477 4431
478 ]
479 },
480 {
481 "label": "Feed Run Behind",
482 "name": "STRING_3",
483 "type": "STRING",
484 "links": [
485 4432
486 ]
487 },
488 {
489 "label": "Prompt Index",
490 "name": "STRING_4",
491 "type": "STRING",
492 "links": [
493 4434
494 ]
495 }
496 ],
497 "properties": {
498 "cnr_id": "comfy-core",
499 "ver": "0.36.0",
500 "previewExposures": []
501 },
502 "widgets_values": [
503 1,
504 false,
505 "plain white"
506 ],
507 "widgets_values_named": {
508 "index": 1,
509 "boolean": false,
510 "value": "plain white"
511 },
512 "color": "#322",
513 "bgcolor": "#533"
514 },
515 {
516 "id": 413,
517 "type": "17d3f5c1-a942-40c9-9fbb-0c435aa5e534",
518 "pos": [
519 2270,
520 3360
521 ],
522 "size": [
523 210,
524 410
525 ],
526 "flags": {
527 "collapsed": false
528 },
529 "order": 25,
530 "mode": 0,
531 "inputs": [
532 {
533 "localized_name": "clip",
534 "name": "clip",
535 "type": "CLIP",
536 "link": 1148
537 },
538 {
539 "localized_name": "vae",
540 "name": "vae",
541 "type": "VAE",
542 "link": 1150
543 },
544 {
545 "localized_name": "prompt",
546 "name": "prompt",
547 "type": "STRING",
548 "widget": {
549 "name": "prompt"
550 },
551 "link": 4435
552 },
553 {
554 "localized_name": "model",
555 "name": "model",
556 "type": "MODEL",
557 "link": 1152
558 },
559 {
560 "localized_name": "seed",
561 "name": "seed",
562 "type": "INT",
563 "widget": {
564 "name": "seed"
565 },
566 "link": null
567 },
568 {
569 "localized_name": "cfg",
570 "name": "cfg",
571 "type": "FLOAT",
572 "widget": {
573 "name": "cfg"
574 },
575 "link": 1840
576 },
577 {
578 "label": "Primary Image",
579 "name": "images.image_1_1",
580 "shape": 7,
581 "type": "IMAGE",
582 "link": 4439
583 },
584 {
585 "label": "Image 2",
586 "name": "images.image_2",
587 "shape": 7,
588 "type": "IMAGE",
589 "link": null
590 },
591 {
592 "label": "Image 3",
593 "name": "images.image_3",
594 "shape": 7,
595 "type": "IMAGE",
596 "link": null
597 },
598 {
599 "label": "Image 4",
600 "name": "images.image_4",
601 "shape": 7,
602 "type": "IMAGE",
603 "link": null
604 },
605 {
606 "name": "sampler",
607 "type": "SAMPLER",
608 "link": 1839
609 },
610 {
611 "name": "sigmas",
612 "type": "SIGMAS",
613 "link": 4873
614 },
615 {
616 "name": "resolution",
617 "type": "INT",
618 "widget": {
619 "name": "resolution"
620 },
621 "link": 2302
622 },
623 {
624 "name": "width",
625 "type": "INT",
626 "widget": {
627 "name": "width"
628 },
629 "link": 4827
630 },
631 {
632 "name": "height",
633 "type": "INT",
634 "widget": {
635 "name": "height"
636 },
637 "link": 4828
638 },
639 {
640 "label": "Base Seed",
641 "name": "values.b",
642 "shape": 7,
643 "type": "FLOAT,INT,BOOLEAN",
644 "link": 2149
645 }
646 ],
647 "outputs": [
648 {
649 "localized_name": "LATENT",
650 "name": "LATENT",
651 "type": "LATENT",
652 "links": null
653 },
654 {
655 "name": "IMAGE",
656 "type": "IMAGE",
657 "links": [
658 4505,
659 4507,
660 4514
661 ]
662 }
663 ],
664 "properties": {
665 "cnr_id": "comfy-core",
666 "ver": "0.36.0",
667 "previewExposures": [
668 {
669 "name": "$$canvas-image-preview",
670 "sourceNodeId": "378",
671 "sourcePreviewName": "$$canvas-image-preview"
672 }
673 ]
674 },
675 "widgets_values": [
676 "",
677 656,
678 1,
679 0,
680 1024,
681 1024
682 ],
683 "widgets_values_named": {
684 "prompt": "",
685 "seed": 656,
686 "cfg": 1,
687 "resolution": 0,
688 "width": 1024,
689 "height": 1024
690 }
691 },
692 {
693 "id": 448,
694 "type": "ImageScaleToTotalPixels",
695 "pos": [
696 840,
697 3260
698 ],
699 "size": [
700 280,
701 110
702 ],
703 "flags": [],
704 "order": 18,
705 "mode": 0,
706 "inputs": [
707 {
708 "localized_name": "image",
709 "name": "image",
710 "type": "IMAGE",
711 "link": 1647
712 },
713 {
714 "localized_name": "upscale_method",
715 "name": "upscale_method",
716 "type": "COMBO",
717 "widget": {
718 "name": "upscale_method"
719 },
720 "link": null
721 },
722 {
723 "localized_name": "megapixels",
724 "name": "megapixels",
725 "type": "FLOAT",
726 "widget": {
727 "name": "megapixels"
728 },
729 "link": null
730 },
731 {
732 "localized_name": "resolution_steps",
733 "name": "resolution_steps",
734 "type": "INT",
735 "widget": {
736 "name": "resolution_steps"
737 },
738 "link": null
739 }
740 ],
741 "outputs": [
742 {
743 "localized_name": "IMAGE",
744 "name": "IMAGE",
745 "type": "IMAGE",
746 "links": [
747 4228,
748 4489
749 ]
750 }
751 ],
752 "properties": {
753 "Node name for S&R": "ImageScaleToTotalPixels",
754 "cnr_id": "comfy-core",
755 "ver": "0.36.0"
756 },
757 "widgets_values": [
758 "nearest-exact",
759 1,
760 1
761 ],
762 "widgets_values_named": {
763 "upscale_method": "nearest-exact",
764 "megapixels": 1,
765 "resolution_steps": 1
766 },
767 "color": "#223",
768 "bgcolor": "#335"
769 },
770 {
771 "id": 485,
772 "type": "PrimitiveStringMultiline",
773 "pos": [
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775 2560
776 ],
777 "size": [
778 420,
779 200
780 ],
781 "flags": [],
782 "order": 3,
783 "mode": 0,
784 "inputs": [
785 {
786 "localized_name": "value",
787 "name": "value",
788 "type": "STRING",
789 "widget": {
790 "name": "value"
791 },
792 "link": null
793 }
794 ],
795 "outputs": [
796 {
797 "localized_name": "STRING",
798 "name": "STRING",
799 "type": "STRING",
800 "links": [
801 2700
802 ]
803 }
804 ],
805 "title": "Head Notes",
806 "properties": {
807 "Node name for S&R": "PrimitiveStringMultiline",
808 "cnr_id": "comfy-core",
809 "ver": "0.36.0"
810 },
811 "widgets_values": [
812 ""
813 ],
814 "widgets_values_named": {
815 "value": ""
816 },
817 "color": "#322",
818 "bgcolor": "#533"
819 },
820 {
821 "id": 511,
822 "type": "56fced51-f034-4655-a066-01d7a829b441",
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825 2860
826 ],
827 "size": [
828 440,
829 240
830 ],
831 "flags": [],
832 "order": 36,
833 "mode": 0,
834 "inputs": [
835 {
836 "label": "File Name",
837 "name": "value",
838 "type": "STRING",
839 "widget": {
840 "name": "value"
841 },
842 "link": null
843 },
844 {
845 "label": "File Path",
846 "name": "output_folder",
847 "type": "STRING",
848 "widget": {
849 "name": "output_folder"
850 },
851 "link": null
852 },
853 {
854 "name": "caption",
855 "shape": 7,
856 "type": "STRING",
857 "link": null
858 },
859 {
860 "label": "Full",
861 "name": "images",
862 "type": "IMAGE",
863 "link": 3709
864 },
865 {
866 "label": "Standard",
867 "name": "images_1",
868 "type": "IMAGE",
869 "link": 4814
870 },
871 {
872 "label": "All Heads",
873 "name": "images_2",
874 "type": "IMAGE",
875 "link": 3712
876 },
877 {
878 "label": "All Bodies",
879 "name": "images_3",
880 "type": "IMAGE",
881 "link": 3711
882 },
883 {
884 "label": "File Names",
885 "name": "text_b",
886 "shape": 7,
887 "type": "STRING",
888 "link": 4816
889 },
890 {
891 "label": "Image list",
892 "name": "images_4",
893 "type": "IMAGE",
894 "link": 4815
895 }
896 ],
897 "outputs": [],
898 "properties": {
899 "cnr_id": "comfy-core",
900 "ver": "0.36.0",
901 "previewExposures": []
902 },
903 "widgets_values": [
904 "",
905 ""
906 ],
907 "widgets_values_named": {
908 "value": "",
909 "output_folder": ""
910 }
911 },
912 {
913 "id": 526,
914 "type": "Note",
915 "pos": [
916 1160,
917 2080
918 ],
919 "size": [
920 560,
921 660
922 ],
923 "flags": [],
924 "order": 4,
925 "mode": 0,
926 "inputs": [],
927 "outputs": [],
928 "title": "Instructions",
929 "properties": [],
930 "widgets_values": [
931 "Excuse the mess. this is the Advanced version of my Q21 Character Sheet Builder. It requires my AutoTrim node linked below: \n\nhttps://github.com/AirbagGuy/ComfyUI-AirbagGuyAutoTrim\n\nIt also uses my new Angle Lora linked below. This version is designed around that lora, as the prompts that previously used for this work flow were stripped down and replaced with my lora triggers. Now, the lora triggers are intuitive so it will work without, just not as well and not as consistent. \n\nhttps://civitai.red/models/3038852/q21-angle-lora-character-creation\n\nThis one works a little different than the previous versions. Because my lora performs well with a stitched three image input, the idea here is that work flow will generate a low resolution base sheet that will then feed to a single ksampler that will go through an index of the prompts. \n\nThe advantages? Reduces clutter, makes the work flow way less laggy, and significantly reduces quality degradation. Now that you're not chaining samplers seven times things just look nicer. Way faster too since you have precise control over the size of the images going into the Main ksampler.\n\nThe caveats? You can't really micro manage seeds anymore, you can't vary the resolution, and if one angle fails you'll have to regenerate everything else. The good news is the lora is reliable, so I think this is worth it. Use the Feed preview to make adjustments before you go onto the main run. \n\nDirections: \n\nStep 1 - Reference Images\n\nThis takes 1 to 4 images. Upload an image to the Primary Image node, this one will be doing the heavy lifting. If you use more you'll have to manually connect them to the routing subgraph \"Supplemental Reference Connection\". One headshot will work; you'll just need to describe everything else.\n\nStep 2 - Prompt \n\nFirst thing you'll want to do is adjust \"Primary Run Input Count\" on the Prompt Templates subgraph to correspond with your number of reference images. \n\nHow this works: there are a series of pre-loaded prompts, with bracketed keywords that correspond with the text nodes to the right. You fill out those text boxes, and a find/replace string node will replace the keywords and fill out the prompt. \n\n- Subject Definition - Allows for more nuance with regards to your subject. Usually Man/Woman will suffice. Haven't tried it with anthro or non-humanoid characters.\n-Style - Straight forward, dictate your desired style.\n-Expression - Same deal, dictate your expression.\n-Feed Gen Only - Only applies to the Feed generation. Used to dictate things like outfits and feature changes.\n-Main Gen Only - Only applies to Main Gen. Used however you like. Can be used to add additional images to Main Gen\n-Head Behind Notes - Used to dictate specific details or to tell the model to remove or ignore things. This is fairly critical with the lora turned on, as the lora likes to copy the image as shown.\n\nThe prompt template will also let you choose if you want to use alpha channel or a background. Users choice. I used to be a big proponent of the alpha channel, but the lora also will give you a consistent \"plain white background\" if you stick with that. You could also put the character where ever you want if you really wanted. \n\nStep 3 - Parameters\n\nThree sections here, the model loader, and both size and seed parameters subgraphs. IF you've gotten this far I assume you know what everything on the Model Load node does. I did add a turbo toggle. This does work fairly well with turbo, I'd recommend viggle, you may need to adjust the manual sigmas though. \n\nThis version they are broken down into Feed and Main. I'd recommend keeping your feed outputs a relatively low resolution. Note that right now I have \"Output Resolution\" on the Feed Parameters subgraph set to 0.5.\n\nStep 4 - Stitch \n\nFairly straight forward. Everything uses my autotrim mode after everything is generated. The basic settings are on the subgraph. If you generate with a background you'll want to switch it off alpha channel. If you generate with alpha channel but want to add a background, turn \"Flatten Images\" on.\n\nStep 5 - Save \n\nUses KJ's save nodes. Save categories are built in so all you need to do is add the save path and add what you want the file names to start with. \n\nStep 6 - Run\n\nCheck the Feed Preview node to make sure everything is correct.\n"
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7394 ]
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7466 ]
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7538 ]
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7938 ]
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