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工作流 / 产品与广告 / Kling 3.0：一键多拍
Kling 3.0：一键多拍

Kling 3.0：一键多拍

Kling 3.0: 1 Click Multishot

上传产品的起始帧和参考图。选择总时长和镜头数量，自动为每个镜头生成提示词。

产品与广告 47 个节点 3 个输入 38 次使用 ComfyUI ≥ 0.35.2 hellorob 112 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。

节点构成

PreviewAny × 13
Reroute × 12
StringToInt × 6
LoadImage × 3
PrimitiveInt × 2
Note × 2
0e2c3eaa-706c-41fd-b300-b6bd9e84a0a4 × 1
2b10af74-c412-4c88-b235-6b170aed965c × 1
SaveVideo × 1
a98e3426-ae7f-4a8b-ba30-a2217a51ad09 × 1
KlingOmniProImageToVideoNode × 1
5d7e839f-932a-49a9-a1f4-f088f38d1dcb × 1
PrimitiveStringMultiline × 1
MarkdownNote × 1
GeminiNodeV3 × 1

官方标注的模型

Kling 3.0

工作流 JSON

和上面「下载工作流 JSON」是同一份文件。

1{
2 "id": "ee8a8542-c278-4b1c-81f9-1075d88b0b4d",
3 "revision": 0,
4 "last_node_id": 171,
5 "last_link_id": 371,
6 "nodes": [
7 {
8 "id": 37,
9 "type": "PreviewAny",
10 "pos": [
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12 670
13 ],
14 "size": [
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16 300
17 ],
18 "flags": {
19 "collapsed": false
20 },
21 "order": 21,
22 "mode": 0,
23 "inputs": [
24 {
25 "name": "source",
26 "type": "*",
27 "link": 189
28 }
29 ],
30 "outputs": [
31 {
32 "name": "STRING",
33 "type": "STRING",
34 "links": null
35 }
36 ],
37 "title": "Shot 2: Prompt",
38 "properties": {
39 "Node name for S&R": "PreviewAny",
40 "cnr_id": "comfy-core",
41 "ver": "0.14.1"
42 },
43 "widgets_values": [],
44 "widgets_values_named": []
45 },
46 {
47 "id": 46,
48 "type": "PreviewAny",
49 "pos": [
50 4180,
51 670
52 ],
53 "size": [
54 210,
55 300
56 ],
57 "flags": {
58 "collapsed": false
59 },
60 "order": 23,
61 "mode": 0,
62 "inputs": [
63 {
64 "name": "source",
65 "type": "*",
66 "link": 191
67 }
68 ],
69 "outputs": [
70 {
71 "name": "STRING",
72 "type": "STRING",
73 "links": null
74 }
75 ],
76 "title": "Shot 3: Prompt",
77 "properties": {
78 "Node name for S&R": "PreviewAny",
79 "cnr_id": "comfy-core",
80 "ver": "0.14.1"
81 },
82 "widgets_values": [],
83 "widgets_values_named": []
84 },
85 {
86 "id": 4,
87 "type": "LoadImage",
88 "pos": [
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90 590
91 ],
92 "size": [
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94 500
95 ],
96 "flags": [],
97 "order": 0,
98 "mode": 0,
99 "inputs": [],
100 "outputs": [
101 {
102 "name": "IMAGE",
103 "type": "IMAGE",
104 "links": [
105 223,
106 368
107 ]
108 },
109 {
110 "name": "MASK",
111 "type": "MASK",
112 "links": null
113 }
114 ],
115 "title": "Load Start Frame Image",
116 "properties": {
117 "Node name for S&R": "LoadImage",
118 "cnr_id": "comfy-core",
119 "ver": "0.14.1"
120 },
121 "widgets_values": [
122 "comfy_gpu_angled.png",
123 "image"
124 ],
125 "widgets_values_named": {
126 "image": "comfy_gpu_angled.png",
127 "upload": "image"
128 },
129 "color": "#232",
130 "bgcolor": "#353"
131 },
132 {
133 "id": 8,
134 "type": "LoadImage",
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138 ],
139 "size": [
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142 ],
143 "flags": {
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145 },
146 "order": 1,
147 "mode": 0,
148 "inputs": [],
149 "outputs": [
150 {
151 "name": "IMAGE",
152 "type": "IMAGE",
153 "links": [
154 224
155 ]
156 },
157 {
158 "name": "MASK",
159 "type": "MASK",
160 "links": null
161 }
162 ],
163 "title": "Load Product Image 1",
164 "properties": {
165 "Node name for S&R": "LoadImage",
166 "cnr_id": "comfy-core",
167 "ver": "0.14.1"
168 },
169 "widgets_values": [
170 "comfy_gpu_flat.png",
171 "image"
172 ],
173 "widgets_values_named": {
174 "image": "comfy_gpu_flat.png",
175 "upload": "image"
176 },
177 "color": "#232",
178 "bgcolor": "#353"
179 },
180 {
181 "id": 12,
182 "type": "LoadImage",
183 "pos": [
184 1470,
185 590
186 ],
187 "size": [
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189 500
190 ],
191 "flags": [],
192 "order": 2,
193 "mode": 0,
194 "inputs": [],
195 "outputs": [
196 {
197 "name": "IMAGE",
198 "type": "IMAGE",
199 "links": [
200 225
201 ]
202 },
203 {
204 "name": "MASK",
205 "type": "MASK",
206 "links": null
207 }
208 ],
209 "title": "Load Product Image 2",
210 "properties": {
211 "Node name for S&R": "LoadImage",
212 "cnr_id": "comfy-core",
213 "ver": "0.14.1"
214 },
215 "widgets_values": [
216 "comfy_gpu_close_up.png",
217 "image"
218 ],
219 "widgets_values_named": {
220 "image": "comfy_gpu_close_up.png",
221 "upload": "image"
222 },
223 "color": "#232",
224 "bgcolor": "#353"
225 },
226 {
227 "id": 81,
228 "type": "0e2c3eaa-706c-41fd-b300-b6bd9e84a0a4",
229 "pos": [
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232 ],
233 "size": [
234 200,
235 50
236 ],
237 "flags": {
238 "collapsed": true
239 },
240 "order": 10,
241 "mode": 0,
242 "inputs": [
243 {
244 "name": "input",
245 "type": "*",
246 "link": 106
247 },
248 {
249 "name": "input_1",
250 "type": "*",
251 "link": 107
252 }
253 ],
254 "outputs": [
255 {
256 "name": "STRING",
257 "type": "STRING",
258 "links": [
259 367
260 ]
261 }
262 ],
263 "properties": {
264 "cnr_id": "comfy-core",
265 "ver": "0.14.1",
266 "proxyWidgets": [],
267 "previewExposures": []
268 }
269 },
270 {
271 "id": 115,
272 "type": "2b10af74-c412-4c88-b235-6b170aed965c",
273 "pos": [
274 3190,
275 570
276 ],
277 "size": [
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279 60
280 ],
281 "flags": {
282 "collapsed": true
283 },
284 "order": 12,
285 "mode": 0,
286 "inputs": [
287 {
288 "name": "string_a",
289 "type": "STRING",
290 "widget": {
291 "name": "string_a"
292 },
293 "link": 339
294 },
295 {
296 "name": "on_false",
297 "shape": 7,
298 "type": "STRING",
299 "link": 340
300 },
301 {
302 "name": "on_true",
303 "shape": 7,
304 "type": "STRING",
305 "link": 370
306 }
307 ],
308 "outputs": [
309 {
310 "name": "output",
311 "type": "STRING",
312 "links": [
313 241
314 ]
315 }
316 ],
317 "properties": {
318 "cnr_id": "comfy-core",
319 "ver": "0.14.1",
320 "proxyWidgetErrorQuarantine": [
321 {
322 "originalEntry": [
323 "-1",
324 "string_a"
325 ],
326 "reason": "missingSourceNode",
327 "attemptedAtVersion": 1
328 }
329 ],
330 "previewExposures": []
331 },
332 "widgets_values": [
333 ""
334 ],
335 "widgets_values_named": {
336 "string_a": ""
337 }
338 },
339 {
340 "id": 32,
341 "type": "PreviewAny",
342 "pos": [
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344 670
345 ],
346 "size": [
347 210,
348 300
349 ],
350 "flags": {
351 "collapsed": false
352 },
353 "order": 19,
354 "mode": 0,
355 "inputs": [
356 {
357 "name": "source",
358 "type": "*",
359 "link": 187
360 }
361 ],
362 "outputs": [
363 {
364 "name": "STRING",
365 "type": "STRING",
366 "links": null
367 }
368 ],
369 "title": "Shot 1: Prompt",
370 "properties": {
371 "Node name for S&R": "PreviewAny",
372 "cnr_id": "comfy-core",
373 "ver": "0.14.1"
374 },
375 "widgets_values": [],
376 "widgets_values_named": []
377 },
378 {
379 "id": 38,
380 "type": "PreviewAny",
381 "pos": [
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383 500
384 ],
385 "size": [
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387 150
388 ],
389 "flags": [],
390 "order": 15,
391 "mode": 0,
392 "inputs": [
393 {
394 "name": "source",
395 "type": "*",
396 "link": 185
397 }
398 ],
399 "outputs": [
400 {
401 "name": "STRING",
402 "type": "STRING",
403 "links": null
404 }
405 ],
406 "title": "Shot 2: # of Seconds",
407 "properties": {
408 "Node name for S&R": "PreviewAny",
409 "cnr_id": "comfy-core",
410 "ver": "0.14.1"
411 },
412 "widgets_values": [],
413 "widgets_values_named": []
414 },
415 {
416 "id": 53,
417 "type": "PreviewAny",
418 "pos": [
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420 1270
421 ],
422 "size": [
423 210,
424 270
425 ],
426 "flags": [],
427 "order": 33,
428 "mode": 0,
429 "inputs": [
430 {
431 "name": "source",
432 "type": "*",
433 "link": 198
434 }
435 ],
436 "outputs": [
437 {
438 "name": "STRING",
439 "type": "STRING",
440 "links": null
441 }
442 ],
443 "title": "Shot 5: Prompt",
444 "properties": {
445 "Node name for S&R": "PreviewAny",
446 "cnr_id": "comfy-core",
447 "ver": "0.14.1"
448 },
449 "widgets_values": [],
450 "widgets_values_named": []
451 },
452 {
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454 "type": "PreviewAny",
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459 "size": [
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462 ],
463 "flags": [],
464 "order": 26,
465 "mode": 0,
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471 }
472 ],
473 "outputs": [
474 {
475 "name": "STRING",
476 "type": "STRING",
477 "links": null
478 }
479 ],
480 "title": "Shot 4: # of Seconds",
481 "properties": {
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483 "cnr_id": "comfy-core",
484 "ver": "0.14.1"
485 },
486 "widgets_values": [],
487 "widgets_values_named": []
488 },
489 {
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491 "type": "PreviewAny",
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495 ],
496 "size": [
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498 150
499 ],
500 "flags": [],
501 "order": 28,
502 "mode": 0,
503 "inputs": [
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506 "type": "*",
507 "link": 307
508 }
509 ],
510 "outputs": [
511 {
512 "name": "STRING",
513 "type": "STRING",
514 "links": null
515 }
516 ],
517 "title": "Shot 5: # of Seconds",
518 "properties": {
519 "Node name for S&R": "PreviewAny",
520 "cnr_id": "comfy-core",
521 "ver": "0.14.1"
522 },
523 "widgets_values": [],
524 "widgets_values_named": []
525 },
526 {
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528 "type": "PreviewAny",
529 "pos": [
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532 ],
533 "size": [
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536 ],
537 "flags": [],
538 "order": 30,
539 "mode": 0,
540 "inputs": [
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542 "name": "source",
543 "type": "*",
544 "link": 308
545 }
546 ],
547 "outputs": [
548 {
549 "name": "STRING",
550 "type": "STRING",
551 "links": null
552 }
553 ],
554 "title": "Shot 6: # of Seconds",
555 "properties": {
556 "Node name for S&R": "PreviewAny",
557 "cnr_id": "comfy-core",
558 "ver": "0.14.1"
559 },
560 "widgets_values": [],
561 "widgets_values_named": []
562 },
563 {
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565 "type": "SaveVideo",
566 "pos": [
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569 ],
570 "size": [
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573 ],
574 "flags": [],
575 "order": 46,
576 "mode": 4,
577 "inputs": [
578 {
579 "name": "video",
580 "type": "VIDEO",
581 "link": 170
582 }
583 ],
584 "outputs": [
585 {
586 "name": "video",
587 "type": "VIDEO",
588 "links": null
589 }
590 ],
591 "properties": {
592 "Node name for S&R": "SaveVideo",
593 "cnr_id": "comfy-core",
594 "ver": "0.14.1"
595 },
596 "widgets_values": [
597 "video/ComfyUI",
598 "auto",
599 "auto",
600 "auto"
601 ],
602 "widgets_values_named": {
603 "filename_prefix": "video/ComfyUI",
604 "format": "auto",
605 "format.codec": "auto",
606 "codec": "auto"
607 }
608 },
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620 "flags": [],
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627 "link": 202
628 }
629 ],
630 "outputs": [
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632 "name": "STRING",
633 "type": "STRING",
634 "links": null
635 }
636 ],
637 "title": "Shot 1: # of Seconds",
638 "properties": {
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640 "cnr_id": "comfy-core",
641 "ver": "0.14.1"
642 },
643 "widgets_values": [],
644 "widgets_values_named": []
645 },
646 {
647 "id": 136,
648 "type": "StringToInt",
649 "pos": [
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653 "size": [
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657 "flags": {
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660 "order": 38,
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671 ],
672 "outputs": [
673 {
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675 "type": "INT",
676 "links": [
677 309
678 ]
679 }
680 ],
681 "title": "Shot 1: Duration",
682 "properties": {
683 "Node name for S&R": "StringToInt",
684 "cnr_id": "comfyui-string-converter",
685 "ver": "0e1e78f877e4d5fc2b46ace8fa21a0ef70e92f11"
686 },
687 "widgets_values": [
688 ""
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690 "widgets_values_named": {
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694 {
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696 "type": "StringToInt",
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701 "size": [
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705 "flags": {
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714 "widget": {
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717 "link": 275
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720 "outputs": [
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722 "name": "INT",
723 "type": "INT",
724 "links": [
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726 ]
727 }
728 ],
729 "title": "Shot 2: Duration",
730 "properties": {
731 "Node name for S&R": "StringToInt",
732 "cnr_id": "comfyui-string-converter",
733 "ver": "0e1e78f877e4d5fc2b46ace8fa21a0ef70e92f11"
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735 "widgets_values": [
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753 "flags": {
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768 "outputs": [
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777 "title": "Shot 3: Duration",
778 "properties": {
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816 "outputs": [
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824 ],
825 "title": "Shot 4: Duration",
826 "properties": {
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829 "ver": "0e1e78f877e4d5fc2b46ace8fa21a0ef70e92f11"
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845 "size": [
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851 },
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853 "mode": 0,
854 "inputs": [
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857 "type": "STRING",
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860 },
861 "link": 281
862 }
863 ],
864 "outputs": [
865 {
866 "name": "INT",
867 "type": "INT",
868 "links": [
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870 ]
871 }
872 ],
873 "title": "Shot 5: Duration",
874 "properties": {
875 "Node name for S&R": "StringToInt",
876 "cnr_id": "comfyui-string-converter",
877 "ver": "0e1e78f877e4d5fc2b46ace8fa21a0ef70e92f11"
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882 "widgets_values_named": {
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912 "outputs": [
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920 ],
921 "title": "Shot 6: Duration",
922 "properties": {
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924 "cnr_id": "comfyui-string-converter",
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2116 "# Workflow Instructions\n\n1. Upload start frame and product reference images.\n2. Enter your desired values in the **Number of Seconds** node and **Number of Shots** node.\n3. Run the workflow to generate the prompts.\n4. Once the **Google Gemini** node generates a prompt you like, copy from the **Preview All Prompts** node and paste into the **Prompt Input** node. Then, Bypass all the nodes in the **LLM Generate Each Shots Prompts** group.\n5. Run the workflow to view a preview of each shot's correlating prompt and number of seconds in the **Preview Prompts** group.\n6. Unbypass **Generate and Save Video** group nodes. **IMPORTANT:** Change the `storyboards` value to the same number of shots you have selected.\n7. Run the workflow again to generate the final video."
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2119 "text": "# Workflow Instructions\n\n1. Upload start frame and product reference images.\n2. Enter your desired values in the **Number of Seconds** node and **Number of Shots** node.\n3. Run the workflow to generate the prompts.\n4. Once the **Google Gemini** node generates a prompt you like, copy from the **Preview All Prompts** node and paste into the **Prompt Input** node. Then, Bypass all the nodes in the **LLM Generate Each Shots Prompts** group.\n5. Run the workflow to view a preview of each shot's correlating prompt and number of seconds in the **Preview Prompts** group.\n6. Unbypass **Generate and Save Video** group nodes. **IMPORTANT:** Change the `storyboards` value to the same number of shots you have selected.\n7. Run the workflow again to generate the final video."
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2200 "You are a creative director and video prompt specialist. Your job is to analyze a product image and generate a sequence of cinematic video prompts for a multi-shot product advertisement.\n\n---\n\n## INPUT\nYou will receive:\n- A product image (used as the visual start frame for Shot 1 only)\n- {N_SHOTS}: the number of shots to generate (1–6)\n- {N_SECONDS}: the total cumulative duration across all shots (max 15 seconds)\n- {USER_STORY}: optional creative direction for vibe, narrative, or tone\n\n---\n\n## STEP 1 — ANALYZE THE IMAGE\nBefore writing any prompts, internally assess the following from the product image:\n- Product type, shape, color, material, and finish\n- Existing lighting mood (hard/soft, warm/cool, dramatic/flat)\n- Background environment — describe it precisely and literally: surface type, color, texture, depth, atmospheric elements (e.g. \"matte dark grey surface, no visible background, soft overhead diffusion\"). This description becomes a locked constant for all shots.\n- Implied brand positioning (luxury, minimal, bold, playful, clinical, etc.)\n\nIf {USER_STORY} is provided, let it override or augment your inferred vibe. If it is empty, rely entirely on your image analysis.\n\n---\n\n## STEP 2 — ASSIGN A SHOT SEQUENCE\nMap {N_SHOTS} to a logical ad narrative arc. Use this as your guide:\n\n| Shot Role | Purpose |\n|---------------|------------------------------------------------------|\n| Hero (Shot 1) | Product reveal — slow, confident, visually grounding |\n| Detail | Extreme close-up of texture, material, or finish |\n| Feature | Product in action, reacting to environment or use |\n| Lifestyle | Product in its natural world or context |\n| Outro | Pull back, final beauty angle, or ambient close |\n\nCompress or expand the arc based on shot count:\n- 1–2 shots: Hero + Outro only\n- 3 shots: Hero, Feature, Outro\n- 4 shots: Hero, Detail, Feature, Outro\n- 5 shots: Hero, Detail, Feature, Lifestyle, Outro\n- 6 shots: Hero, Detail, Detail, Feature, Lifestyle, Outro\n\n---\n\n## STEP 3 — ALLOCATE DURATIONS\nDistribute seconds across shots. Rules:\n- All durations must be integers (whole seconds)\n- Total must equal exactly {N_SECONDS}\n- Weight durations by shot role and inferred pacing:\n - Luxury/cinematic vibe: favor longer Hero and Outro, compress middle shots\n - Energetic/bold vibe: distribute more evenly, keep all shots punchy\n - Default weighting: Hero gets ~25%, Outro gets ~20%, remaining shots split the rest\n\n---\n\n## STEP 4 — WRITE THE PROMPTS\n\n**Shot 1 (I2V — Image to Video):**\nThe product image is the literal start frame. Write a prompt that describes the motion and camera behavior starting from that exact frame. Do not re-describe the product appearance — describe only what moves and how.\n\n**Shots 2–N (T2V — Text to Video):**\nEach prompt must be fully self-contained. Describe the product, the scene, the camera, and the motion clearly enough that a video model with no prior context can generate a coherent, on-brand clip. You must preserve the exact environment from the input image — the same surface, background, depth, and atmosphere. Do not introduce new environments, floors, settings, or atmospheric elements (mist, smoke, reflections, etc.) that were not present in the input image. The environment is a locked constant, not a creative variable.\n\n**Prompt writing rules (apply to all shots):**\n- Write in present tense, descriptive and directive\n- Lead with the most important visual element\n- Specify: camera angle, camera motion, subject action, lighting, mood\n- Keep each prompt between 40–80 words — specific but not overloaded\n- No marketing copy, no voiceover text, no titles or graphics\n- Each shot must feel visually distinct from the others — vary angle, distance, and motion meaningfully\n\n---\n\n## OUTPUT FORMAT\nOutput only the tagged shot blocks. No preamble, no explanation, no additional text.\n\n<shot1_duration>N</shot1_duration>\n<shot1_prompt>Prompt text here</shot1_prompt>\n\n<shot2_duration>N</shot2_duration>\n<shot2_prompt>Prompt text here</shot2_prompt>\n\n...and so on for all {N_SHOTS} shots."
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2210 "model.system_prompt": "You are a creative director and video prompt specialist. Your job is to analyze a product image and generate a sequence of cinematic video prompts for a multi-shot product advertisement.\n\n---\n\n## INPUT\nYou will receive:\n- A product image (used as the visual start frame for Shot 1 only)\n- {N_SHOTS}: the number of shots to generate (1–6)\n- {N_SECONDS}: the total cumulative duration across all shots (max 15 seconds)\n- {USER_STORY}: optional creative direction for vibe, narrative, or tone\n\n---\n\n## STEP 1 — ANALYZE THE IMAGE\nBefore writing any prompts, internally assess the following from the product image:\n- Product type, shape, color, material, and finish\n- Existing lighting mood (hard/soft, warm/cool, dramatic/flat)\n- Background environment — describe it precisely and literally: surface type, color, texture, depth, atmospheric elements (e.g. \"matte dark grey surface, no visible background, soft overhead diffusion\"). This description becomes a locked constant for all shots.\n- Implied brand positioning (luxury, minimal, bold, playful, clinical, etc.)\n\nIf {USER_STORY} is provided, let it override or augment your inferred vibe. If it is empty, rely entirely on your image analysis.\n\n---\n\n## STEP 2 — ASSIGN A SHOT SEQUENCE\nMap {N_SHOTS} to a logical ad narrative arc. Use this as your guide:\n\n| Shot Role | Purpose |\n|---------------|------------------------------------------------------|\n| Hero (Shot 1) | Product reveal — slow, confident, visually grounding |\n| Detail | Extreme close-up of texture, material, or finish |\n| Feature | Product in action, reacting to environment or use |\n| Lifestyle | Product in its natural world or context |\n| Outro | Pull back, final beauty angle, or ambient close |\n\nCompress or expand the arc based on shot count:\n- 1–2 shots: Hero + Outro only\n- 3 shots: Hero, Feature, Outro\n- 4 shots: Hero, Detail, Feature, Outro\n- 5 shots: Hero, Detail, Feature, Lifestyle, Outro\n- 6 shots: Hero, Detail, Detail, Feature, Lifestyle, Outro\n\n---\n\n## STEP 3 — ALLOCATE DURATIONS\nDistribute seconds across shots. Rules:\n- All durations must be integers (whole seconds)\n- Total must equal exactly {N_SECONDS}\n- Weight durations by shot role and inferred pacing:\n - Luxury/cinematic vibe: favor longer Hero and Outro, compress middle shots\n - Energetic/bold vibe: distribute more evenly, keep all shots punchy\n - Default weighting: Hero gets ~25%, Outro gets ~20%, remaining shots split the rest\n\n---\n\n## STEP 4 — WRITE THE PROMPTS\n\n**Shot 1 (I2V — Image to Video):**\nThe product image is the literal start frame. Write a prompt that describes the motion and camera behavior starting from that exact frame. Do not re-describe the product appearance — describe only what moves and how.\n\n**Shots 2–N (T2V — Text to Video):**\nEach prompt must be fully self-contained. Describe the product, the scene, the camera, and the motion clearly enough that a video model with no prior context can generate a coherent, on-brand clip. You must preserve the exact environment from the input image — the same surface, background, depth, and atmosphere. Do not introduce new environments, floors, settings, or atmospheric elements (mist, smoke, reflections, etc.) that were not present in the input image. The environment is a locked constant, not a creative variable.\n\n**Prompt writing rules (apply to all shots):**\n- Write in present tense, descriptive and directive\n- Lead with the most important visual element\n- Specify: camera angle, camera motion, subject action, lighting, mood\n- Keep each prompt between 40–80 words — specific but not overloaded\n- No marketing copy, no voiceover text, no titles or graphics\n- Each shot must feel visually distinct from the others — vary angle, distance, and motion meaningfully\n\n---\n\n## OUTPUT FORMAT\nOutput only the tagged shot blocks. No preamble, no explanation, no additional text.\n\n<shot1_duration>N</shot1_duration>\n<shot1_prompt>Prompt text here</shot1_prompt>\n\n<shot2_duration>N</shot2_duration>\n<shot2_prompt>Prompt text here</shot2_prompt>\n\n...and so on for all {N_SHOTS} shots."
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