工作流 /
图像 /
Ming 图像 0.1 Design：图像编辑
Ming 图像 0.1 Design：图像编辑
Ming Image 0.1 Design: Image Edit
使用 Ming Image 0.1 Design 编辑现有图像，这是一个针对 UI、信息图和海报调优的 6B 模型。它接收一张输入图像以及你在其上绘制的框——位置、描述和调色板——并将它们重建为逐层说明，返回一张重新渲染的图像。非常适合重新布置布局、海报风格重渲染，以及将照片转化为设计构图。
图像
19 个节点 1 个输入 34 次使用 ComfyUI ≥ 0.38.0 ComfyUI 212 KB
需要的权重
这个工作流引用了 3 个权重文件，其中 3 个能在本站模型库里找到。
ming_image_0.1_design_int8_convrot.safetensors
在模型库查看
ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors
在模型库查看
ming_image_vae_bf16.safetensors
在模型库查看
节点构成
MarkdownNote
× 4
PreviewAny
× 3
PreviewImage
× 2
ImageStitch
× 2
LoadImage
× 1
4522b5d6-7f71-4b60-8fab-1e6ffbd5f1c1
× 1
GetImageSize
× 1
ResizeImageMaskNode
× 1
CreateBoundingBoxes
× 1
BuildJsonPromptIdeogram
× 1
ae951efd-07f6-49cb-bfdb-0b8e3eee4cda
× 1
SaveImageAdvanced
× 1
官方标注的模型
Ming Image
工作流 JSON
和上面「下载工作流 JSON」是同一份文件。
1{
2 "id": "aa0ea3e2-5729-4d50-956e-26fc02b13d9e",
3 "revision": 0,
4 "last_node_id": 344,
5 "last_link_id": 21011,
6 "nodes": [
7 {
8 "id": 49,
9 "type": "LoadImage",
10 "pos": [
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13 ],
14 "size": [
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17 ],
18 "flags": [],
19 "order": 0,
20 "mode": 0,
21 "inputs": [],
22 "outputs": [
23 {
24 "name": "IMAGE",
25 "type": "IMAGE",
26 "links": [
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28 229,
29 231,
30 21004
31 ]
32 },
33 {
34 "name": "MASK",
35 "type": "MASK",
36 "links": null
37 }
38 ],
39 "properties": {
40 "Node name for S&R": "LoadImage"
41 },
42 "widgets_values": [
43 "yellow_arch_daisy_field.png",
44 "image"
45 ],
46 "widgets_values_named": {
47 "image": "yellow_arch_daisy_field.png",
48 "upload": "image"
49 }
50 },
51 {
52 "id": 80,
53 "type": "4522b5d6-7f71-4b60-8fab-1e6ffbd5f1c1",
54 "pos": [
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57 ],
58 "size": [
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60 580
61 ],
62 "flags": [],
63 "order": 15,
64 "mode": 0,
65 "inputs": [
66 {
67 "name": "images.image_1",
68 "shape": 7,
69 "type": "IMAGE",
70 "link": 229
71 },
72 {
73 "label": "image_2",
74 "name": "images.image_2",
75 "shape": 7,
76 "type": "IMAGE",
77 "link": null
78 },
79 {
80 "label": "image_3",
81 "name": "images.image_3",
82 "shape": 7,
83 "type": "IMAGE",
84 "link": null
85 },
86 {
87 "label": "image_4",
88 "name": "images.image_4",
89 "shape": 7,
90 "type": "IMAGE",
91 "link": null
92 },
93 {
94 "label": "image_5",
95 "name": "images.image_5",
96 "shape": 7,
97 "type": "IMAGE",
98 "link": null
99 },
100 {
101 "label": "image_6",
102 "name": "images.image_6",
103 "shape": 7,
104 "type": "IMAGE",
105 "link": null
106 },
107 {
108 "label": "image_7",
109 "name": "images.image_7",
110 "shape": 7,
111 "type": "IMAGE",
112 "link": null
113 },
114 {
115 "label": "image_8",
116 "name": "images.image_8",
117 "shape": 7,
118 "type": "IMAGE",
119 "link": null
120 },
121 {
122 "name": "prompt",
123 "type": "STRING",
124 "widget": {
125 "name": "prompt"
126 },
127 "link": 21003
128 },
129 {
130 "label": "width",
131 "name": "value",
132 "type": "INT",
133 "widget": {
134 "name": "value"
135 },
136 "link": 205
137 },
138 {
139 "label": "height",
140 "name": "value_1",
141 "type": "INT",
142 "widget": {
143 "name": "value_1"
144 },
145 "link": 206
146 },
147 {
148 "label": "model_attention_backend",
149 "name": "attention_1",
150 "type": "COMBO",
151 "widget": {
152 "name": "attention_1"
153 },
154 "link": null
155 }
156 ],
157 "outputs": [
158 {
159 "name": "IMAGE",
160 "type": "IMAGE",
161 "links": [
162 21010
163 ]
164 }
165 ],
166 "properties": {
167 "previewExposures": []
168 },
169 "widgets_values": [
170 "",
171 1024,
172 1024,
173 247081603831298,
174 "simple",
175 12,
176 "ming_image_0.1_design_int8_convrot.safetensors",
177 "ming_image_vae_bf16.safetensors",
178 "qwen_image",
179 "comfy kitchen attention"
180 ],
181 "widgets_values_named": {
182 "prompt": "",
183 "value": 1024,
184 "value_1": 1024,
185 "noise_seed": 247081603831298,
186 "scheduler": "simple",
187 "steps": 12,
188 "unet_name": "ming_image_0.1_design_int8_convrot.safetensors",
189 "vae_name": "ming_image_vae_bf16.safetensors",
190 "type": "qwen_image",
191 "attention_1": "comfy kitchen attention"
192 }
193 },
194 {
195 "id": 81,
196 "type": "GetImageSize",
197 "pos": [
198 290,
199 4900
200 ],
201 "size": [
202 230,
203 140
204 ],
205 "flags": [],
206 "order": 10,
207 "mode": 0,
208 "inputs": [
209 {
210 "name": "image",
211 "type": "IMAGE",
212 "link": 210
213 }
214 ],
215 "outputs": [
216 {
217 "name": "width",
218 "type": "INT",
219 "links": [
220 205,
221 1054
222 ]
223 },
224 {
225 "name": "height",
226 "type": "INT",
227 "links": [
228 206,
229 1055
230 ]
231 },
232 {
233 "name": "batch_size",
234 "type": "INT",
235 "links": null
236 }
237 ],
238 "properties": {
239 "Node name for S&R": "GetImageSize"
240 }
241 },
242 {
243 "id": 83,
244 "type": "ResizeImageMaskNode",
245 "pos": [
246 260,
247 4720
248 ],
249 "size": [
250 270,
251 110
252 ],
253 "flags": [],
254 "order": 6,
255 "mode": 4,
256 "inputs": [
257 {
258 "name": "input",
259 "type": "IMAGE,MASK",
260 "link": 231
261 }
262 ],
263 "outputs": [
264 {
265 "name": "resized",
266 "type": "IMAGE",
267 "links": [
268 210
269 ]
270 }
271 ],
272 "properties": {
273 "Node name for S&R": "ResizeImageMaskNode"
274 },
275 "widgets_values": [
276 "scale total pixels",
277 1,
278 "area"
279 ],
280 "widgets_values_named": {
281 "resize_type": "scale total pixels",
282 "resize_type.megapixels": 1,
283 "scale_method": "area"
284 }
285 },
286 {
287 "id": 85,
288 "type": "MarkdownNote",
289 "pos": [
290 280,
291 4590
292 ],
293 "size": [
294 210,
295 90
296 ],
297 "flags": [],
298 "order": 1,
299 "mode": 0,
300 "inputs": [],
301 "outputs": [],
302 "title": "Note: Resize Input",
303 "properties": [],
304 "widgets_values": [
305 "Select and press Ctrl-B to enable `Resize Image/Mask` node if your input image is too large (over 2048*2048).\n"
306 ],
307 "widgets_values_named": {
308 "text": "Select and press Ctrl-B to enable `Resize Image/Mask` node if your input image is too large (over 2048*2048).\n"
309 },
310 "color": "#222",
311 "bgcolor": "#000"
312 },
313 {
314 "id": 86,
315 "type": "MarkdownNote",
316 "pos": [
317 -2280,
318 4430
319 ],
320 "size": [
321 540,
322 630
323 ],
324 "flags": [],
325 "order": 2,
326 "mode": 0,
327 "inputs": [],
328 "outputs": [],
329 "title": "Note: Model Links",
330 "properties": [],
331 "widgets_values": [
332 "Guide: [Subgraph](https://docs.comfy.org/interface/features/subgraph)\n\n## Input Asset\n\n- [yellow_arch_daisy_field.png](https://raw.githubusercontent.com/Comfy-Org/workflow_templates/refs/heads/main/input/yellow_arch_daisy_field.png)\n\n## Model Links\n\n- [Hugging Face:Comfy-Org/Ming-Image](https://huggingface.co/Comfy-Org/Ming-Image)\n\n**ModelScope mirrors** (alternative repo location)\n\n- [ModelScope:Comfy-Org/Ming-Image](https://modelscope.cn/models/Comfy-Org/Ming-Image)\n\n**diffusion_models**\n\n- [ming_image_0.1_design_int8_convrot.safetensors](https://huggingface.co/Comfy-Org/Ming-Image/resolve/main/diffusion_models/ming_image_0.1_design_int8_convrot.safetensors) (5.75 GB)\n\n**vae**\n\n- [ming_image_vae_bf16.safetensors](https://huggingface.co/Comfy-Org/Ming-Image/resolve/main/vae/ming_image_vae_bf16.safetensors) (242.1 MB)\n\n**text_encoders**\n\n- [ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors](https://huggingface.co/Comfy-Org/Ming-Image/resolve/main/text_encoders/ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors) (18.17 GB)\n\n## Model Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 diffusion_models/\n│ │ └── ming_image_0.1_design_int8_convrot.safetensors\n│ ├── 📂 vae/\n│ │ └── ming_image_vae_bf16.safetensors\n│ └── 📂 text_encoders/\n│ └── ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors\n```\n\n## Report Issue\n\nNote: Please update ComfyUI first ([guide](https://docs.comfy.org/installation/update_comfyui)) and prepare required models. Desktop/Cloud updates follow stable releases, so some nightly-supported models may not be available yet.\n\n- Cannot run / runtime errors: [ComfyUI/issues](https://github.com/comfyanonymous/ComfyUI/issues)\n- UI / frontend issues: [ComfyUI_frontend/issues](https://github.com/Comfy-Org/ComfyUI_frontend/issues)\n- Workflow issues: [workflow_templates/issues](https://github.com/Comfy-Org/workflow_templates/issues)\n"
333 ],
334 "widgets_values_named": {
335 "text": "Guide: [Subgraph](https://docs.comfy.org/interface/features/subgraph)\n\n## Input Asset\n\n- [yellow_arch_daisy_field.png](https://raw.githubusercontent.com/Comfy-Org/workflow_templates/refs/heads/main/input/yellow_arch_daisy_field.png)\n\n## Model Links\n\n- [Hugging Face:Comfy-Org/Ming-Image](https://huggingface.co/Comfy-Org/Ming-Image)\n\n**ModelScope mirrors** (alternative repo location)\n\n- [ModelScope:Comfy-Org/Ming-Image](https://modelscope.cn/models/Comfy-Org/Ming-Image)\n\n**diffusion_models**\n\n- [ming_image_0.1_design_int8_convrot.safetensors](https://huggingface.co/Comfy-Org/Ming-Image/resolve/main/diffusion_models/ming_image_0.1_design_int8_convrot.safetensors) (5.75 GB)\n\n**vae**\n\n- [ming_image_vae_bf16.safetensors](https://huggingface.co/Comfy-Org/Ming-Image/resolve/main/vae/ming_image_vae_bf16.safetensors) (242.1 MB)\n\n**text_encoders**\n\n- [ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors](https://huggingface.co/Comfy-Org/Ming-Image/resolve/main/text_encoders/ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors) (18.17 GB)\n\n## Model Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 diffusion_models/\n│ │ └── ming_image_0.1_design_int8_convrot.safetensors\n│ ├── 📂 vae/\n│ │ └── ming_image_vae_bf16.safetensors\n│ └── 📂 text_encoders/\n│ └── ming_image_0.1_ling_mini_2.0_int8_convrot.safetensors\n```\n\n## Report Issue\n\nNote: Please update ComfyUI first ([guide](https://docs.comfy.org/installation/update_comfyui)) and prepare required models. Desktop/Cloud updates follow stable releases, so some nightly-supported models may not be available yet.\n\n- Cannot run / runtime errors: [ComfyUI/issues](https://github.com/comfyanonymous/ComfyUI/issues)\n- UI / frontend issues: [ComfyUI_frontend/issues](https://github.com/Comfy-Org/ComfyUI_frontend/issues)\n- Workflow issues: [workflow_templates/issues](https://github.com/Comfy-Org/workflow_templates/issues)\n"
336 },
337 "color": "#222",
338 "bgcolor": "#000"
339 },
340 {
341 "id": 87,
342 "type": "MarkdownNote",
343 "pos": [
344 -1710,
345 4430
346 ],
347 "size": [
348 560,
349 940
350 ],
351 "flags": [],
352 "order": 3,
353 "mode": 0,
354 "inputs": [],
355 "outputs": [],
356 "title": "Note: Ming Image 0.1 Design — Edit (subgraph + prompt)",
357 "properties": [],
358 "widgets_values": [
359 "# Ming Image 0.1 Design — Edit (ComfyUI)\n\nMing Image 0.1 **Design** (inclusionAI / Ant Ling): text-to-image and image editing with one checkpoint.\nFiles — see *Note: Model Links*.\n\nLinks — [Ming-Image repo](https://github.com/inclusionAI/Ming-Image) · [model card](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) · [Design skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/ling-ui-design) · [PPT skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/image-to-editable-ppt)\n\n## Three subgraphs — double-click any of them to open\n\n| subgraph | what it does |\n|---|---|\n| **Image Edit (Ming Image 0.1 Design)** | the model: reference image(s) + `prompt` → sample → decode |\n| **Ideogram JSON → Ming Caption** | rewrites the Ideogram JSON + the bounding boxes into Ming's Figma-style caption and feeds `prompt` |\n| **Aspect Ratio (gcd-reduced)** | nested inside the one above: `width` / `height` → `7:9, 896 × 1152 px` for `canvas_settings.aspect_ratio` |\n\n## Image Edit subgraph parameters\n\n| parameter | what it does |\n|---|---|\n| `image_1` … `image_8` | reference image(s) to edit; only `image_1` is required |\n| `prompt` | the instruction (see below) |\n| `width` / `height` | output size, wired from `Get Image Size` |\n| `noise_seed` / `scheduler` / `steps` | sampling; default `simple`, 12 steps |\n| `unet_name` / `vae_name` / `type` | weights, and text-encoder family (`qwen_image`) |\n| `model_attention_backend` | `comfy kitchen attention` (INT8, fastest) is only listed on GPUs that support it — NVIDIA cc 7.5+ or AMD/ROCm; otherwise pick `pytorch attention`: same result, slower |\n\nNo negative prompt, no CFG — positive conditioning only.\n\n## Prompt: \n\n**1 · Natural language**: you can disconnect the `Ideogram JSON to Ming Caption` output, then use your own prompt to edit the image\n\n**2 · Ming's own Figma-style caption**: Currently this workflow builds a prompt that matches Ming's Figma-style caption, you can use it for precise control (same shape as the official [rewriter system prompt](https://github.com/inclusionAI/Ming-Image/blob/main/assets/t2i_rewriter_system_prompt.txt) emits):\n\n```json\n{\n \"canvas_settings\": {\"aspect_ratio\": \"…\", \"ambient_lighting\": \"…\", \"image_style\": \"…\"},\n \"layers\": [{\"description\": \"…\", \"coordinates\": \"cx: 0.500, cy: 0.500, w: 1.000, h: 1.000\",\n \"hierarchy_and_relation\": \"…\", \"color_specs\": [\"#RRGGBB\"]}]\n}\n```\n\nTwo keys only; `layers` runs background → topmost; `coordinates` is one string (centre + size, normalised); rendered text is quoted and owned once. `aspect_ratio` and the pixel size are computed from the boxes' canvas, so adding boxes or swapping the input image needs no edits. Example: [t2i_four_seasons_cabin_prompt.json](https://github.com/inclusionAI/Ming-Image/blob/main/assets/t2i_four_seasons_cabin_prompt.json)\n"
360 ],
361 "widgets_values_named": {
362 "text": "# Ming Image 0.1 Design — Edit (ComfyUI)\n\nMing Image 0.1 **Design** (inclusionAI / Ant Ling): text-to-image and image editing with one checkpoint.\nFiles — see *Note: Model Links*.\n\nLinks — [Ming-Image repo](https://github.com/inclusionAI/Ming-Image) · [model card](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) · [Design skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/ling-ui-design) · [PPT skill](https://github.com/inclusionAI/ling-cookbook/tree/main/resources/recommended-skills/image-to-editable-ppt)\n\n## Three subgraphs — double-click any of them to open\n\n| subgraph | what it does |\n|---|---|\n| **Image Edit (Ming Image 0.1 Design)** | the model: reference image(s) + `prompt` → sample → decode |\n| **Ideogram JSON → Ming Caption** | rewrites the Ideogram JSON + the bounding boxes into Ming's Figma-style caption and feeds `prompt` |\n| **Aspect Ratio (gcd-reduced)** | nested inside the one above: `width` / `height` → `7:9, 896 × 1152 px` for `canvas_settings.aspect_ratio` |\n\n## Image Edit subgraph parameters\n\n| parameter | what it does |\n|---|---|\n| `image_1` … `image_8` | reference image(s) to edit; only `image_1` is required |\n| `prompt` | the instruction (see below) |\n| `width` / `height` | output size, wired from `Get Image Size` |\n| `noise_seed` / `scheduler` / `steps` | sampling; default `simple`, 12 steps |\n| `unet_name` / `vae_name` / `type` | weights, and text-encoder family (`qwen_image`) |\n| `model_attention_backend` | `comfy kitchen attention` (INT8, fastest) is only listed on GPUs that support it — NVIDIA cc 7.5+ or AMD/ROCm; otherwise pick `pytorch attention`: same result, slower |\n\nNo negative prompt, no CFG — positive conditioning only.\n\n## Prompt: \n\n**1 · Natural language**: you can disconnect the `Ideogram JSON to Ming Caption` output, then use your own prompt to edit the image\n\n**2 · Ming's own Figma-style caption**: Currently this workflow builds a prompt that matches Ming's Figma-style caption, you can use it for precise control (same shape as the official [rewriter system prompt](https://github.com/inclusionAI/Ming-Image/blob/main/assets/t2i_rewriter_system_prompt.txt) emits):\n\n```json\n{\n \"canvas_settings\": {\"aspect_ratio\": \"…\", \"ambient_lighting\": \"…\", \"image_style\": \"…\"},\n \"layers\": [{\"description\": \"…\", \"coordinates\": \"cx: 0.500, cy: 0.500, w: 1.000, h: 1.000\",\n \"hierarchy_and_relation\": \"…\", \"color_specs\": [\"#RRGGBB\"]}]\n}\n```\n\nTwo keys only; `layers` runs background → topmost; `coordinates` is one string (centre + size, normalised); rendered text is quoted and owned once. `aspect_ratio` and the pixel size are computed from the boxes' canvas, so adding boxes or swapping the input image needs no edits. Example: [t2i_four_seasons_cabin_prompt.json](https://github.com/inclusionAI/Ming-Image/blob/main/assets/t2i_four_seasons_cabin_prompt.json)\n"
363 },
364 "color": "#222",
365 "bgcolor": "#000"
366 },
367 {
368 "id": 91,
369 "type": "CreateBoundingBoxes",
370 "pos": [
371 -1070,
372 5190
373 ],
374 "size": [
375 420,
376 560
377 ],
378 "flags": [],
379 "order": 5,
380 "mode": 0,
381 "inputs": [
382 {
383 "name": "background",
384 "shape": 7,
385 "type": "IMAGE",
386 "link": 228
387 },
388 {
389 "name": "bboxes",
390 "shape": 7,
391 "type": "BOUNDING_BOX,ARRAY,STRING",
392 "link": null
393 }
394 ],
395 "outputs": [
396 {
397 "name": "preview",
398 "type": "IMAGE",
399 "links": [
400 225
401 ]
402 },
403 {
404 "name": "bboxes",
405 "type": "BOUNDING_BOX",
406 "links": [
407 214
408 ]
409 },
410 {
411 "name": "elements",
412 "type": "ARRAY",
413 "links": [
414 215,
415 1063
416 ]
417 }
418 ],
419 "properties": {
420 "Node name for S&R": "CreateBoundingBoxes"
421 },
422 "widgets_values": [
423 896,
424 1152,
425 [
426 {
427 "x": 0,
428 "y": 0,
429 "width": 896,
430 "height": 1152,
431 "metadata": {
432 "type": "obj",
433 "text": "",
434 "desc": "the whole picture reprinted as a mid-century screen-print poster sheet: flat unmodulated colour planes, hand-cut stencil edges, warm off-white paper stock with fine print grain, one faint ink misregistration, a thin dark-ink rule inset a few millimetres from the trimmed edge, no photographic depth of field",
435 "palette": [
436 "#F5F0E3",
437 "#E4B23C",
438 "#2F5FA8",
439 "#1E2A38"
440 ]
441 }
442 },
443 {
444 "x": 0,
445 "y": 0,
446 "width": 896,
447 "height": 555,
448 "metadata": {
449 "type": "obj",
450 "text": "",
451 "desc": "the sunlit tiled facade redrawn as two flat ochre plates, one in full sun and one in shade, split by one crisp diagonal shadow that runs from the top edge near the centre down to the left edge about a fifth of the way down; the tile grout becomes a sparse regular grid of thin lighter lines",
452 "palette": [
453 "#E4B23C",
454 "#C6952F",
455 "#F0C85A"
456 ]
457 }
458 },
459 {
460 "x": 57,
461 "y": 205,
462 "width": 193,
463 "height": 350,
464 "metadata": {
465 "type": "obj",
466 "text": "",
467 "desc": "the left round-headed arch opening cut cleanly through the facade: inside it a flat pale-blue sky with one simplified cream cloud bank, edges stencil-cut, no gradients",
468 "palette": [
469 "#4E86C8",
470 "#A9C9E8",
471 "#F5F0E3"
472 ]
473 }
474 },
475 {
476 "x": 357,
477 "y": 192,
478 "width": 196,
479 "height": 363,
480 "metadata": {
481 "type": "obj",
482 "text": "",
483 "desc": "the centre arch opening, the tallest of the three: flat pale-blue sky, one low cream cloud shape near its base, and the daisy horizon showing as a thin band of small white dots across the bottom of the opening",
484 "palette": [
485 "#4E86C8",
486 "#A9C9E8",
487 "#F5F0E3"
488 ]
489 }
490 },
491 {
492 "x": 652,
493 "y": 200,
494 "width": 203,
495 "height": 355,
496 "metadata": {
497 "type": "obj",
498 "text": "",
499 "desc": "the right arch opening holding a single tree: trunk and crown reduced to a two-tone silhouette of dark olive green and deep umber against the pale blue sky, branches simplified to a few decisive lines, foliage as clustered flat shapes, no leaf texture",
500 "palette": [
501 "#4E86C8",
502 "#6E8B4A",
503 "#3B4A2E"
504 ]
505 }
506 },
507 {
508 "x": 0,
509 "y": 500,
510 "width": 896,
511 "height": 652,
512 "metadata": {
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3264 "name": "current_iteration_value",
3265 "type": "*",
3266 "slot_index": 4,
3267 "links": [
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3269 9007
3270 ]
3271 }
3272 ],
3273 "title": "Start Loop (Euclid gcd; iterations come from width/height)",
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3277 "widgets_values": [
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3280 false
3281 ],
3282 "widgets_values_named": {
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3285 "cache_iterations": false
3286 },
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3288 "bgcolor": "#535"
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3292 "type": "GetItemFromList",
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3299 90
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3309 "link": 9006
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3312 "outputs": [
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3314 "localized_name": "*",
3315 "name": "*",
3316 "type": "*",
3317 "slot_index": 0,
3318 "links": [
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3320 9010
3321 ]
3322 }
3323 ],
3324 "title": "Get Item From List (a)",
3325 "properties": {
3326 "Node name for S&R": "GetItemFromList"
3327 },
3328 "widgets_values": [
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3330 ],
3331 "widgets_values_named": {
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3333 },
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3335 "bgcolor": "#355"
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3339 "type": "GetItemFromList",
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3348 "flags": [],
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3351 "inputs": [
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3354 "name": "list",
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3356 "link": 9007
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3359 "outputs": [
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3361 "localized_name": "*",
3362 "name": "*",
3363 "type": "*",
3364 "slot_index": 0,
3365 "links": [
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3367 9011
3368 ]
3369 }
3370 ],
3371 "title": "Get Item From List (b)",
3372 "properties": {
3373 "Node name for S&R": "GetItemFromList"
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3375 "widgets_values": [
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3377 ],
3378 "widgets_values_named": {
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3382 "bgcolor": "#355"
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3386 "type": "ComfyMathExpression",
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3401 "name": "values.a",
3402 "type": "FLOAT,INT,BOOLEAN",
3403 "link": 9008
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3406 "localized_name": "b",
3407 "name": "values.b",
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3409 "type": "FLOAT,INT,BOOLEAN",
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3411 },
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3413 "localized_name": "c",
3414 "name": "values.c",
3415 "shape": 7,
3416 "type": "FLOAT,INT,BOOLEAN",
3417 "link": null
3418 }
3419 ],
3420 "outputs": [
3421 {
3422 "localized_name": "FLOAT",
3423 "name": "FLOAT",
3424 "type": "FLOAT",
3425 "slot_index": 0,
3426 "links": []
3427 },
3428 {
3429 "localized_name": "INT",
3430 "name": "INT",
3431 "type": "INT",
3432 "slot_index": 1,
3433 "links": [
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3436 },
3437 {
3438 "localized_name": "BOOL",
3439 "name": "BOOL",
3440 "type": "BOOLEAN",
3441 "slot_index": 2,
3442 "links": []
3443 }
3444 ],
3445 "title": "Math Expression (r = a mod b)",
3446 "properties": {
3447 "Node name for S&R": "ComfyMathExpression"
3448 },
3449 "widgets_values": [
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3451 ],
3452 "widgets_values_named": {
3453 "expression": "a % b if b > 0 else 0"
3454 },
3455 "color": "#332",
3456 "bgcolor": "#553"
3457 },
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3459 "id": 329,
3460 "type": "ComfyMathExpression",
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3463 220
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3465 "size": [
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3467 150
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3469 "flags": [],
3470 "order": 6,
3471 "mode": 0,
3472 "inputs": [
3473 {
3474 "localized_name": "a",
3475 "name": "values.a",
3476 "type": "FLOAT,INT,BOOLEAN",
3477 "link": 9010
3478 },
3479 {
3480 "localized_name": "b",
3481 "name": "values.b",
3482 "shape": 7,
3483 "type": "FLOAT,INT,BOOLEAN",
3484 "link": 9011
3485 },
3486 {
3487 "localized_name": "c",
3488 "name": "values.c",
3489 "shape": 7,
3490 "type": "FLOAT,INT,BOOLEAN",
3491 "link": null
3492 }
3493 ],
3494 "outputs": [
3495 {
3496 "localized_name": "FLOAT",
3497 "name": "FLOAT",
3498 "type": "FLOAT",
3499 "slot_index": 0,
3500 "links": []
3501 },
3502 {
3503 "localized_name": "INT",
3504 "name": "INT",
3505 "type": "INT",
3506 "slot_index": 1,
3507 "links": [
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3509 ]
3510 },
3511 {
3512 "localized_name": "BOOL",
3513 "name": "BOOL",
3514 "type": "BOOLEAN",
3515 "slot_index": 2,
3516 "links": []
3517 }
3518 ],
3519 "title": "Math Expression (next a = b if b > 0 else a)",
3520 "properties": {
3521 "Node name for S&R": "ComfyMathExpression"
3522 },
3523 "widgets_values": [
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3526 "widgets_values_named": {
3527 "expression": "b if b > 0 else a"
3528 },
3529 "color": "#332",
3530 "bgcolor": "#553"
3531 },
3532 {
3533 "id": 330,
3534 "type": "CreateList",
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3537 110
3538 ],
3539 "size": [
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3541 160
3542 ],
3543 "flags": [],
3544 "order": 7,
3545 "mode": 0,
3546 "inputs": [
3547 {
3548 "localized_name": "input0",
3549 "name": "inputs.input0",
3550 "type": "*",
3551 "link": 9012
3552 },
3553 {
3554 "localized_name": "input1",
3555 "name": "inputs.input1",
3556 "shape": 7,
3557 "type": "*",
3558 "link": 9013
3559 },
3560 {
3561 "localized_name": "input2",
3562 "name": "inputs.input2",
3563 "shape": 7,
3564 "type": "*",
3565 "link": null
3566 }
3567 ],
3568 "outputs": [
3569 {
3570 "localized_name": "list",
3571 "name": "list",
3572 "shape": 6,
3573 "type": "*",
3574 "slot_index": 0,
3575 "links": [
3576 9014,
3577 9015
3578 ]
3579 }
3580 ],
3581 "title": "Create List (next pair)",
3582 "properties": {
3583 "Node name for S&R": "CreateList"
3584 },
3585 "color": "#233",
3586 "bgcolor": "#355"
3587 },
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3589 "id": 331,
3590 "type": "EndLoop",
3591 "pos": [
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3593 120
3594 ],
3595 "size": [
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3597 120
3598 ],
3599 "flags": [],
3600 "order": 8,
3601 "mode": 0,
3602 "inputs": [
3603 {
3604 "localized_name": "output_value",
3605 "name": "output_value",
3606 "shape": 7,
3607 "type": "*",
3608 "link": 9014
3609 },
3610 {
3611 "localized_name": "next_iteration_value",
3612 "name": "next_iteration_value",
3613 "shape": 7,
3614 "type": "*",
3615 "link": 9015
3616 },
3617 {
3618 "localized_name": "termination0",
3619 "name": "terminations.termination0",
3620 "shape": 7,
3621 "type": "*",
3622 "link": null
3623 }
3624 ],
3625 "outputs": [
3626 {
3627 "localized_name": "outputs",
3628 "name": "outputs",
3629 "shape": 6,
3630 "type": "*",
3631 "slot_index": 0,
3632 "links": [
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3634 ]
3635 }
3636 ],
3637 "properties": {
3638 "Node name for S&R": "EndLoop"
3639 },
3640 "widgets_values": [
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3642 ],
3643 "widgets_values_named": {
3644 "accumulate": false
3645 },
3646 "color": "#323",
3647 "bgcolor": "#535"
3648 },
3649 {
3650 "id": 332,
3651 "type": "GetItemFromList",
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3654 120
3655 ],
3656 "size": [
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3658 90
3659 ],
3660 "flags": [],
3661 "order": 9,
3662 "mode": 0,
3663 "inputs": [
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3666 "name": "list",
3667 "type": "*",
3668 "link": 9016
3669 }
3670 ],
3671 "outputs": [
3672 {
3673 "localized_name": "*",
3674 "name": "*",
3675 "type": "*",
3676 "slot_index": 0,
3677 "links": [
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3679 9020
3680 ]
3681 }
3682 ],
3683 "title": "Get Item From List (gcd)",
3684 "properties": {
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3686 },
3687 "widgets_values": [
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3689 ],
3690 "widgets_values_named": {
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3692 },
3693 "color": "#233",
3694 "bgcolor": "#355"
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3706 ],
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3709 "mode": 0,
3710 "inputs": [
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3713 "name": "values.a",
3714 "type": "FLOAT,INT,BOOLEAN",
3715 "link": 9018
3716 },
3717 {
3718 "localized_name": "b",
3719 "name": "values.b",
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3721 "type": "FLOAT,INT,BOOLEAN",
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3723 },
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3726 "name": "values.c",
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3728 "type": "FLOAT,INT,BOOLEAN",
3729 "link": null
3730 }
3731 ],
3732 "outputs": [
3733 {
3734 "localized_name": "FLOAT",
3735 "name": "FLOAT",
3736 "type": "FLOAT",
3737 "slot_index": 0,
3738 "links": []
3739 },
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3742 "name": "INT",
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3747 ]
3748 },
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3751 "name": "BOOL",
3752 "type": "BOOLEAN",
3753 "slot_index": 2,
3754 "links": []
3755 }
3756 ],
3757 "title": "Math Expression (width / gcd)",
3758 "properties": {
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3760 },
3761 "widgets_values": [
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3763 ],
3764 "widgets_values_named": {
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3790 },
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3802 "type": "FLOAT,INT,BOOLEAN",
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3828 "links": []
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3830 ],
3831 "title": "Math Expression (height / gcd)",
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3842 "bgcolor": "#553"
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3857 "mode": 0,
3858 "inputs": [
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3865 },
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3872 },
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3877 "type": "*",
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3879 },
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3884 "type": "*",
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3886 },
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3891 "type": "*",
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3893 }
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3915 }
3916 },
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3919 "type": "MarkdownNote",
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3931 "inputs": [],
3932 "outputs": [],
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3934 "properties": [],
3935 "widgets_values": [
3936 "**Aspect ratio is computed, not typed**\n\n`canvas_settings.aspect_ratio` must read like `7:9, 896 × 1152 px`. This nested subgraph derives it from the `width` / `height` its parent receives from `Get Image Size`:\n\n* the loop count is computed as well — `ceil(1.5 * log2(max(width, height))) + 2` is an upper bound on the Euclidean steps (18 for 896 x 1152, 23 at the 16384 px maximum), so nothing here is hand-typed;\n* inside the loop `r = a mod b`, then `a = b`; once `b` reaches 0 the first value freezes at `gcd(width, height)`;\n* `width / gcd` and `height / gcd` become `ratio, width × height px` in `Format Text (canvas aspect ratio + pixels)`.\n\nSwap the input image or resize the canvas and the ratio follows automatically.\n"
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3943 }
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4025 "type": "FLOAT,INT,BOOLEAN"
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4033 "type": "*"
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4057 "type": "FLOAT,INT,BOOLEAN"
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4112 "target_slot": 0,
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4118 "origin_slot": 0,
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4120 "target_slot": 1,
4121 "type": "FLOAT,INT,BOOLEAN"
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4126 "origin_slot": 1,
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4128 "target_slot": 0,
4129 "type": "*"
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4133 "origin_id": 334,
4134 "origin_slot": 1,
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7146 "title": "Note: Ideogram JSON → Ming caption (built-in nodes only)",
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7149 "**What this subgraph does**\n\nTurns the Ideogram JSON + the bounding boxes into Ming's own Figma-style caption (`canvas_settings` + `layers`) and sends it to the `prompt` of **Image Edit (Ming Image 0.1 Design)**.\n\n| input | comes from | carries |\n|---|---|---|\n| `ideogram` | `Build JSON Prompt (Ideogram)` | the Ideogram JSON (style / lighting / background / palette …) |\n| `elements` | `Create Bounding Boxes` → `elements` | the boxes, turned into the text queue the loop eats one item per iteration |\n| `width` / `height` | `Get Image Size` | output size; the nested **Aspect Ratio (gcd-reduced)** subgraph turns them into `7:9, 896 × 1152 px` |\n\nOutput: `caption` → `prompt` of the Image Edit subgraph, plus the preview in `Preview as Text`.\n\n**How the loop works**\n\nOne iteration per box. `Start Loop` runs `count` times, where `count` is the number of boxes — counted on the left by `Replace Text (Regex)` + `Text Length` (that count is wired into the loop's `max_iteration`, so adding or removing boxes needs no rewiring). Each iteration:\n\n* pulls the next box JSON out of the queue,\n* maps `bbox [ymin, xmin, ymax, xmax]` → `cx: …, cy: …, w: …, h: …` (normalised centre + size),\n* `desc` → `description`, `color_palette` → `color_specs`, a text box becomes `Text layer reading \"…\"`,\n* appends the layer and drops that box from the queue.\n\nThe base layer (\"the whole canvas\") is the loop's seed; layers accumulate background → topmost.\n\n**To skip this subgraph**\n\nBypass it (`Ctrl+B`) and either type English into `prompt`, or wire `Build JSON Prompt (Ideogram)` straight into `prompt` to send the Ideogram JSON instead of the Ming caption.\n"
7150 ],
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7152 "text": "**What this subgraph does**\n\nTurns the Ideogram JSON + the bounding boxes into Ming's own Figma-style caption (`canvas_settings` + `layers`) and sends it to the `prompt` of **Image Edit (Ming Image 0.1 Design)**.\n\n| input | comes from | carries |\n|---|---|---|\n| `ideogram` | `Build JSON Prompt (Ideogram)` | the Ideogram JSON (style / lighting / background / palette …) |\n| `elements` | `Create Bounding Boxes` → `elements` | the boxes, turned into the text queue the loop eats one item per iteration |\n| `width` / `height` | `Get Image Size` | output size; the nested **Aspect Ratio (gcd-reduced)** subgraph turns them into `7:9, 896 × 1152 px` |\n\nOutput: `caption` → `prompt` of the Image Edit subgraph, plus the preview in `Preview as Text`.\n\n**How the loop works**\n\nOne iteration per box. `Start Loop` runs `count` times, where `count` is the number of boxes — counted on the left by `Replace Text (Regex)` + `Text Length` (that count is wired into the loop's `max_iteration`, so adding or removing boxes needs no rewiring). Each iteration:\n\n* pulls the next box JSON out of the queue,\n* maps `bbox [ymin, xmin, ymax, xmax]` → `cx: …, cy: …, w: …, h: …` (normalised centre + size),\n* `desc` → `description`, `color_palette` → `color_specs`, a text box becomes `Text layer reading \"…\"`,\n* appends the layer and drops that box from the queue.\n\nThe base layer (\"the whole canvas\") is the loop's seed; layers accumulate background → topmost.\n\n**To skip this subgraph**\n\nBypass it (`Ctrl+B`) and either type English into `prompt`, or wire `Build JSON Prompt (Ideogram)` straight into `prompt` to send the Ideogram JSON instead of the Ming caption.\n"
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