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工作流 / 图像 / Moss for "ART"
Moss for "ART"

Moss for "ART"

Moss for "ART"

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图像 163 个节点 217 次使用 mr1morcellini 182 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 1 个权重文件，其中 0 个能在本站模型库里找到。

moss.json 模型库暂未收录

节点构成

GetNode × 34
SetNode × 20
Label (rgthree) × 8
PrimitiveStringMultiline × 4
VAELoader × 3
easy clearCacheAll × 3
Bookmark (rgthree) × 3
easy cleanGpuUsed × 3
SamplerCustomAdvanced × 3
CFGGuider × 3
CLIPTextEncode × 3
Any Switch (rgthree) × 3
PreviewImage × 3
ManualSigmas × 3
ResizeImagesByLongerEdge × 2
CLIPLoader × 2

官方标注的模型

Qwen3-VL-8B-Instruct-abliterated-v2.Q8_0.ggufVerity by morcellini lokr.safetensorsdepth_anything_v2_vitl.pthflux2-vae.safetensorsgemma_2_2b_it_elm_bf16.safetensorsideogram4_fp8_scaled.safetensorsmossImage_sft_pruned_int8.safetensorsmoss_image_color_depth_control.safetensorsmoss_image_sft_4_steps_lora_v2_600_avg_rank_72_bf16.safetensorspid_1.5_qwenimage_1024_to_4096_4step_bf16.safetensorsqwen3.5_9b_int8_convrot.safetensorsqwen_image_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": 311,
5 "last_link_id": 6234,
6 "nodes": [
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8 "id": 2,
9 "type": "KreaMoodboardVisualBrowser",
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20 "mode": 4,
21 "inputs": [
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24 "name": "query",
25 "type": "STRING",
26 "widget": {
27 "name": "query"
28 },
29 "link": null
30 },
31 {
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33 "name": "selected_uuid",
34 "type": "STRING",
35 "widget": {
36 "name": "selected_uuid"
37 },
38 "link": null
39 },
40 {
41 "localized_name": "selected_title",
42 "name": "selected_title",
43 "type": "STRING",
44 "widget": {
45 "name": "selected_title"
46 },
47 "link": null
48 },
49 {
50 "localized_name": "selected_url",
51 "name": "selected_url",
52 "type": "STRING",
53 "widget": {
54 "name": "selected_url"
55 },
56 "link": null
57 },
58 {
59 "localized_name": "selected_metadata_json",
60 "name": "selected_metadata_json",
61 "type": "STRING",
62 "widget": {
63 "name": "selected_metadata_json"
64 },
65 "link": null
66 },
67 {
68 "localized_name": "strength",
69 "name": "strength",
70 "type": "COMBO",
71 "widget": {
72 "name": "strength"
73 },
74 "link": null
75 }
76 ],
77 "outputs": [
78 {
79 "localized_name": "positive",
80 "name": "positive",
81 "type": "STRING",
82 "links": [
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84 ]
85 },
86 {
87 "localized_name": "negative",
88 "name": "negative",
89 "type": "STRING",
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93 },
94 {
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96 "name": "title",
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99 },
100 {
101 "localized_name": "uuid",
102 "name": "uuid",
103 "type": "STRING",
104 "links": []
105 },
106 {
107 "localized_name": "url",
108 "name": "url",
109 "type": "STRING",
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111 },
112 {
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114 "name": "metadata_json",
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118 ]
119 },
120 {
121 "localized_name": "preview",
122 "name": "preview",
123 "type": "STRING",
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125 }
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127 "properties": {
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129 "ue_properties": {
130 "widget_ue_connectable": [],
131 "input_ue_unconnectable": [],
132 "version": "7.8"
133 }
134 },
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138 "Luminous Geometric Brutalism",
139 "https://www.krea.ai/moodboard-feed/luminous-geometric-brutalism-8626e934-daf3-5874-af33-728202f29531",
140 "{\"keywords\": [\"architectural abstraction\", \"geometric grid facade\", \"golden hour lighting\", \"high-contrast reflections\", \"monumental skyscraper\", \"minimalist urbanism\", \"teal and amber color palette\"], \"slug\": \"luminous-geometric-brutalism-8626e934-daf3-5874-af33-728202f29531\", \"source_summary\": \"A geometric aesthetic of repeating rectilinear grids where cool steel-and-glass teal meets warm golden-hour amber in monumental, ordered abstraction.\", \"style_axes\": [\"teal and amber palette\", \"golden-hour raking light\", \"geometric grid repetition\", \"mirror reflections\", \"minimalist structural order\"], \"title\": \"Luminous Geometric Brutalism\", \"url\": \"https://www.krea.ai/moodboard-feed/luminous-geometric-brutalism-8626e934-daf3-5874-af33-728202f29531\", \"uuid\": \"8626e934-daf3-5874-af33-728202f29531\"}",
141 "normal",
142 ""
143 ],
144 "widgets_values_named": {
145 "query": "party",
146 "selected_uuid": "8626e934-daf3-5874-af33-728202f29531",
147 "selected_title": "Luminous Geometric Brutalism",
148 "selected_url": "https://www.krea.ai/moodboard-feed/luminous-geometric-brutalism-8626e934-daf3-5874-af33-728202f29531",
149 "selected_metadata_json": "{\"keywords\": [\"architectural abstraction\", \"geometric grid facade\", \"golden hour lighting\", \"high-contrast reflections\", \"monumental skyscraper\", \"minimalist urbanism\", \"teal and amber color palette\"], \"slug\": \"luminous-geometric-brutalism-8626e934-daf3-5874-af33-728202f29531\", \"source_summary\": \"A geometric aesthetic of repeating rectilinear grids where cool steel-and-glass teal meets warm golden-hour amber in monumental, ordered abstraction.\", \"style_axes\": [\"teal and amber palette\", \"golden-hour raking light\", \"geometric grid repetition\", \"mirror reflections\", \"minimalist structural order\"], \"title\": \"Luminous Geometric Brutalism\", \"url\": \"https://www.krea.ai/moodboard-feed/luminous-geometric-brutalism-8626e934-daf3-5874-af33-728202f29531\", \"uuid\": \"8626e934-daf3-5874-af33-728202f29531\"}",
150 "strength": "normal",
151 "browser": ""
152 }
153 },
154 {
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156 "type": "KreaMoodboardApply",
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160 ],
161 "size": [
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164 ],
165 "flags": [],
166 "order": 77,
167 "mode": 4,
168 "inputs": [
169 {
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171 "name": "prompt",
172 "type": "STRING",
173 "widget": {
174 "name": "prompt"
175 },
176 "link": 3
177 },
178 {
179 "localized_name": "negative_prompt",
180 "name": "negative_prompt",
181 "type": "STRING",
182 "widget": {
183 "name": "negative_prompt"
184 },
185 "link": 4
186 },
187 {
188 "localized_name": "moodboard_positive",
189 "name": "moodboard_positive",
190 "type": "STRING",
191 "widget": {
192 "name": "moodboard_positive"
193 },
194 "link": 5
195 },
196 {
197 "localized_name": "moodboard_negative",
198 "name": "moodboard_negative",
199 "type": "STRING",
200 "widget": {
201 "name": "moodboard_negative"
202 },
203 "link": 6
204 },
205 {
206 "localized_name": "metadata_json",
207 "name": "metadata_json",
208 "type": "STRING",
209 "widget": {
210 "name": "metadata_json"
211 },
212 "link": 7
213 },
214 {
215 "localized_name": "strength",
216 "name": "strength",
217 "type": "COMBO",
218 "widget": {
219 "name": "strength"
220 },
221 "link": null
222 },
223 {
224 "localized_name": "style_only",
225 "name": "style_only",
226 "type": "BOOLEAN",
227 "widget": {
228 "name": "style_only"
229 },
230 "link": null
231 },
232 {
233 "localized_name": "separator",
234 "name": "separator",
235 "type": "COMBO",
236 "widget": {
237 "name": "separator"
238 },
239 "link": null
240 }
241 ],
242 "outputs": [
243 {
244 "localized_name": "positive",
245 "name": "positive",
246 "type": "STRING",
247 "links": [
248 56
249 ]
250 },
251 {
252 "localized_name": "negative",
253 "name": "negative",
254 "type": "STRING",
255 "links": [
256 57
257 ]
258 },
259 {
260 "localized_name": "metadata_json",
261 "name": "metadata_json",
262 "type": "STRING",
263 "links": []
264 }
265 ],
266 "properties": {
267 "Node name for S&R": "KreaMoodboardApply",
268 "ue_properties": {
269 "widget_ue_connectable": [],
270 "input_ue_unconnectable": [],
271 "version": "7.8"
272 }
273 },
274 "widgets_values": [
275 "",
276 "",
277 "",
278 "",
279 "{}",
280 "normal",
281 false,
282 "newline"
283 ],
284 "widgets_values_named": {
285 "prompt": "",
286 "negative_prompt": "",
287 "moodboard_positive": "",
288 "moodboard_negative": "",
289 "metadata_json": "{}",
290 "strength": "normal",
291 "style_only": false,
292 "separator": "newline"
293 }
294 },
295 {
296 "id": 5,
297 "type": "Label (rgthree)",
298 "pos": [
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301 ],
302 "size": [
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305 ],
306 "flags": {
307 "allow_interaction": true
308 },
309 "order": 1,
310 "mode": 0,
311 "inputs": [],
312 "outputs": [],
313 "title": "Morcellini's",
314 "properties": {
315 "fontSize": 400,
316 "fontFamily": "Old London",
317 "fontColor": "#004225",
318 "textAlign": "left",
319 "backgroundColor": "transparent",
320 "padding": 0,
321 "borderRadius": 0,
322 "angle": 0,
323 "ue_properties": {
324 "widget_ue_connectable": [],
325 "input_ue_unconnectable": [],
326 "version": "7.5.2"
327 },
328 "enableTabs": false,
329 "tabWidth": 65,
330 "tabXOffset": 10,
331 "hasSecondTab": false,
332 "secondTabText": "Send Back",
333 "secondTabOffset": 80,
334 "secondTabWidth": 65
335 },
336 "color": "#fff0",
337 "bgcolor": "#fff0"
338 },
339 {
340 "id": 6,
341 "type": "ResizeImagesByLongerEdge",
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344 202392.7576460676
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346 "size": [
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349 ],
350 "flags": [],
351 "order": 109,
352 "mode": 0,
353 "inputs": [
354 {
355 "localized_name": "images",
356 "name": "images",
357 "type": "IMAGE",
358 "link": 8
359 },
360 {
361 "localized_name": "longer_edge",
362 "name": "longer_edge",
363 "type": "INT",
364 "widget": {
365 "name": "longer_edge"
366 },
367 "link": null
368 }
369 ],
370 "outputs": [
371 {
372 "localized_name": "images",
373 "name": "images",
374 "type": "IMAGE",
375 "links": [
376 54
377 ]
378 }
379 ],
380 "properties": {
381 "Node name for S&R": "ResizeImagesByLongerEdge",
382 "ue_properties": {
383 "widget_ue_connectable": {
384 "longer_edge": true
385 },
386 "version": "7.8",
387 "input_ue_unconnectable": []
388 }
389 },
390 "widgets_values": [
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392 ],
393 "widgets_values_named": {
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395 }
396 },
397 {
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408 "flags": {
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412 "mode": 0,
413 "inputs": [
414 {
415 "localized_name": "vae_name",
416 "name": "vae_name",
417 "type": "COMBO",
418 "widget": {
419 "name": "vae_name"
420 },
421 "link": null
422 }
423 ],
424 "outputs": [
425 {
426 "localized_name": "VAE",
427 "name": "VAE",
428 "type": "VAE",
429 "links": [
430 30
431 ]
432 }
433 ],
434 "properties": {
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436 "ue_properties": {
437 "widget_ue_connectable": [],
438 "version": "7.8",
439 "input_ue_unconnectable": []
440 }
441 },
442 "widgets_values": [
443 "pixel_space"
444 ],
445 "widgets_values_named": {
446 "vae_name": "pixel_space"
447 },
448 "color": "#322",
449 "bgcolor": "#533"
450 },
451 {
452 "id": 8,
453 "type": "easy clearCacheAll",
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462 "flags": {
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466 "mode": 0,
467 "inputs": [
468 {
469 "localized_name": "anything",
470 "name": "anything",
471 "type": "*",
472 "link": 9
473 }
474 ],
475 "outputs": [
476 {
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478 "name": "output",
479 "type": "*",
480 "links": []
481 }
482 ],
483 "properties": {
484 "Node name for S&R": "easy clearCacheAll",
485 "ue_properties": {
486 "widget_ue_connectable": [],
487 "input_ue_unconnectable": [],
488 "version": "7.8"
489 },
490 "cnr_id": "comfyui-easy-use",
491 "ver": "625efbfa2fc20c31797dfffcbb41a26b6d91ab7b"
492 },
493 "color": "#222",
494 "bgcolor": "#000"
495 },
496 {
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507 "flags": {
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510 "order": 3,
511 "mode": 0,
512 "inputs": [],
513 "outputs": [],
514 "title": "🔖",
515 "properties": {
516 "ue_properties": {
517 "widget_ue_connectable": [],
518 "input_ue_unconnectable": [],
519 "version": "7.8"
520 }
521 },
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526 "widgets_values_named": {
527 "shortcut_key": "1",
528 "zoom": 0.7
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530 },
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542 "flags": {
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544 },
545 "order": 4,
546 "mode": 0,
547 "inputs": [],
548 "outputs": [],
549 "title": "you can quickly get here by pressing 1",
550 "properties": {
551 "fontSize": 30,
552 "fontFamily": "Old London",
553 "fontColor": "#ffffff",
554 "textAlign": "left",
555 "backgroundColor": "transparent",
556 "padding": 0,
557 "borderRadius": 0,
558 "angle": 0,
559 "ue_properties": {
560 "widget_ue_connectable": [],
561 "input_ue_unconnectable": [],
562 "version": "7.5.2"
563 },
564 "enableTabs": false,
565 "tabWidth": 65,
566 "tabXOffset": 10,
567 "hasSecondTab": false,
568 "secondTabText": "Send Back",
569 "secondTabOffset": 80,
570 "secondTabWidth": 65
571 },
572 "color": "#fff0",
573 "bgcolor": "#fff0"
574 },
575 {
576 "id": 11,
577 "type": "PrimitiveStringMultiline",
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586 "flags": {
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590 "mode": 4,
591 "inputs": [
592 {
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594 "name": "value",
595 "type": "STRING",
596 "widget": {
597 "name": "value"
598 },
599 "link": null
600 }
601 ],
602 "outputs": [
603 {
604 "localized_name": "STRING",
605 "name": "STRING",
606 "type": "STRING",
607 "links": [
608 24
609 ]
610 }
611 ],
612 "title": "LLM prompt",
613 "properties": {
614 "Node name for S&R": "PrimitiveStringMultiline",
615 "ue_properties": {
616 "widget_ue_connectable": [],
617 "version": "7.8",
618 "input_ue_unconnectable": []
619 },
620 "cnr_id": "comfy-core",
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623 "tabWidth": 65,
624 "tabXOffset": 10,
625 "hasSecondTab": false,
626 "secondTabText": "Send Back",
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629 },
630 "widgets_values": [
631 "You are an expert visual prompt engineer specializing in creating high-quality prompts for MOSS-Image.\n\nYour task is to transform the user's rough image idea into a precise, coherent, visually rich image-generation prompt. The final result should read like a carefully written professional photography brief, not like a generic list of quality keywords.\n\nYour priorities, in order, are:\n1. Preserve the user's intended image and all important supplied details.\n2. Make the image visually unambiguous through useful detail about subjects, pose, clothing, environment, lighting, camera, and composition.\n3. Maintain physical, anatomical, spatial, and photographic coherence.\n4. Keep the prompt concise enough that every sentence contributes useful visual information.\n\nThe goal is BOTH rich detail and high visual precision. Do not sacrifice one for the other.\n\n==================================================\n1. AUTOMATIC INPUT-FORMAT DETECTION\n==================================================\n\nIdentify which kind of input the user has actually supplied:\n\nA. PLAIN-TEXT MODE\nUse when the user gives an ordinary sentence, phrase, rough prompt, or list of image ideas.\n\nB. JSON-LAYOUT MODE\nUse when the user supplies a JSON layout, especially one containing \"summary\" and \"items\" with \"caption\" and bounding-box fields.\n\nC. MIXED MODE\nUse when the user supplies a JSON layout together with additional written instructions. Apply the instructions to the image description while preserving the JSON layout and all protected fields.\n\nDo not mistake a JSON example quoted inside a question for the actual image prompt. Follow the user's real request.\n\n==================================================\n2. VISUAL PROMPT QUALITY STANDARD\n==================================================\n\nFor ordinary photographic prompts, aim for the same level of completeness and natural descriptive style as a professional, detailed smartphone-photography brief. A good result should normally establish:\n\n- Who or what is shown, including the number of subjects.\n- The subject's apparent adult age range when supplied or clearly established, visible appearance, hairstyle, relevant body proportions, clothing, accessories, expression, gaze, posture, pose, and interaction with nearby objects.\n- The exact type of place and the important furniture, architecture, fixtures, and background objects.\n- The lighting sources, direction, color, intensity, ambient illumination, and resulting shadows or reflections.\n- The camera viewpoint, orientation, framing, crop, perspective, and depth of field when relevant.\n- The overall photographic character, such as casual smartphone photography, a mirror selfie, a candid snapshot, or a carefully composed portrait, when supported by the user's idea.\n- Realistic surface and texture cues that materially improve the image.\n\nDo not mechanically include every category in every prompt. Select the details that matter for the specific image.\n\nLENGTH AND COMPLETENESS\n\n- For a simple or moderately detailed photographic idea, usually produce approximately 120–220 words.\n- For a complex scene with several subjects, important objects, or a detailed environment, use approximately 200–350 words when the extra detail is genuinely useful.\n- These are guidelines, not rigid limits. A simple concept may need less; a complicated concept may need more.\n- Do not produce an extremely short one-sentence result when the user expects a polished, detailed prompt.\n- Do not inflate the result with repeated adjectives, redundant phrases, or irrelevant technical specifications.\n- Match the user's supplied example in descriptive richness and completeness, not by copying its wording or forcing the same details into unrelated scenes.\n\nWRITING STYLE\n\n- Write fluent, natural English in connected prose.\n- Normally use one to three paragraphs for a plain-text prompt.\n- Start directly with the image description. Do not add a title, introduction, explanation, analysis, checklist, or conclusion.\n- Prefer concrete visual descriptions over vague praise such as \"beautiful\", \"amazing\", \"perfect\", or \"stunning\".\n- Correct spelling and grammar in the user's rough wording without changing its meaning.\n- Describe the finished image, not the process of generating it. Prefer \"A woman stands in front of...\" over \"Create an image of...\".\n- Avoid keyword stuffing and empty quality tags such as \"8K\", \"16K\", \"masterpiece\", \"best quality\", and \"ultra-detailed\" unless the user specifically requests tag-style prompting.\n\n==================================================\n3. SUBJECTS, ANATOMY, AND BODY PROPORTIONS\n==================================================\n\nPreserve the user's specified subject count, age, appearance, body type, pose, clothing, accessories, and other defining characteristics. Do not silently replace or contradict them.\n\nFor human subjects:\n- Describe visible characteristics in a coherent way: apparent age range, face, hair, skin appearance, build, posture, body orientation, hands, legs, gaze, expression, clothing, and accessories, as relevant to the image.\n- Make poses physically possible. Ensure the placement of arms, hands, legs, shoulders, and torso is consistent with the described action and camera view.\n- Keep body proportions natural and internally consistent unless the user explicitly requests a stylized or exaggerated appearance.\n- Do not infer exact age, identity, ethnicity, or hidden physical characteristics without evidence. If age is unclear, use neutral wording rather than inventing an age.\n\nBREAST-SIZE AND BUST-PROPORTION RULE FOR CLEARLY ADULT WOMEN\n\nWhen a clearly adult woman is a main subject and her bust size is visually relevant, make sure the prompt establishes a coherent bust proportion rather than leaving the feature unintentionally ambiguous. Apply these rules:\n\n- If the user explicitly specifies breast or bust size, preserve that description.\n- If the user provides a reference image and the bust is visible, describe only what is reasonably visible in the reference, unless the user requests a deliberate change.\n- If no size is specified and no reference makes it clear, choose a natural, context-appropriate proportion that fits the overall character design and the user's stated aesthetic. Use restrained descriptions such as \"a modest bust\", \"a naturally proportionate medium-sized bust\", or \"a fuller bust that remains proportionate to her frame\" when useful.\n- Do not automatically assign a large, small, or medium bust solely from a woman's height, weight, slimness, or general body type. Those traits do not determine breast size.\n- Consider the overall visual design, clothing cut and fit, posture, and intended silhouette. Do not make the clothing or body proportions contradict one another.\n- Do not force a bust description into every prompt. Include it only when it contributes to the requested visual result or helps define the subject.\n- Avoid arbitrary cup sizes or measurements unless the user explicitly provides them.\n- Do not exaggerate anatomy when the user has not requested exaggeration.\n\nDo not add intimate or hidden anatomical details that are not visible, requested, or necessary to describe the image. Keep descriptions focused on visible appearance and the intended image.\n\n==================================================\n4. ENVIRONMENT, LIGHTING, AND MATERIALS\n==================================================\n\nExpand an underspecified scene with plausible details that support the user's idea. Useful additions may include:\n\n- Room layout, architecture, furniture, windows, mirrors, doors, fixtures, wall details, and background objects.\n- Floor, wall, bedding, furniture, clothing, glass, metal, wood, plastic, and other relevant materials.\n- The direction and quality of natural or artificial light, ambient color, practical light sources, exposure, shadow softness, reflections, and highlight behavior.\n- Foreground, middle-ground, and background separation, as well as believable depth and spatial relationships.\n\nLighting should be described as a physical cause-and-effect relationship. For example, specify which light source casts colored illumination, where it falls, and how it affects the subject and room. Do not add contradictory lighting sources.\n\nDescribe textures where they can affect the generated image: fabric weave and folds, hair strands, natural skin texture, glass reflections, wood grain, metal finish, dust, fingerprints, or subtle surface wear. Do not mention every possible texture in every prompt.\n\nOnly add plausible, scene-supporting details. Do not invent major story elements, extra people, objects, brands, locations, or actions that change the user's concept.\n\n==================================================\n5. CAMERA AND COMPOSITION\n==================================================\n\nDescribe how the image is photographed when camera choices matter.\n\nConsider:\n- Smartphone, mirror selfie, candid snapshot, portrait, documentary photograph, or another photographic context.\n- Portrait or landscape orientation.\n- Full-body, three-quarter, waist-up, or close-up framing.\n- Camera height, distance, viewing angle, tilt, and direction.\n- Whether the subject is seen directly or through a mirror reflection.\n- Subject placement, crop, foreground and background, negative space, and visual balance.\n- Focus, depth of field, perspective, and realistic lens behavior.\n\nChoose only specifications that make sense for the scene. Do not invent a specific phone model, focal length, aperture, sensor, or camera setting unless provided or genuinely useful.\n\nFor mirror selfies, maintain correct reflection logic: the phone, hands, body orientation, mirror, reflected room, and camera viewpoint must make physical sense. Do not describe the reflection as though it were a second independent person.\n\nFor casual smartphone photographs, appropriate realism cues may include imperfect framing, slight perspective distortion, realistic phone-camera processing, natural exposure behavior, subtle low-light noise, and unretouched details. Use only those that suit the scene.\n\n==================================================\n6. REALISM AND STYLE\n==================================================\n\n- Preserve any explicitly requested style: photorealism, casual smartphone photography, studio photography, cinematic still, anime, illustration, painting, 3D rendering, or another aesthetic.\n- Do not impose a strong style that the user did not request.\n- When the concept clearly describes an ordinary real-world photograph and no other style is specified, default to believable photographic realism.\n- For photorealistic images, emphasize physically plausible light, natural proportions, realistic materials, coherent reflections, natural skin and hair detail, and camera characteristics appropriate to the scene.\n- Avoid excessive retouching, artificial studio lighting, or glamour styling unless the user asks for them.\n- Do not use \"cinematic\" as a generic filler word. Use it only when the requested image actually has a cinematic visual character.\n\n==================================================\n7. PLAIN-TEXT MODE: OUTPUT RULES\n==================================================\n\nReturn one finished MOSS-Image prompt in English.\n\n- Use one to three connected prose paragraphs in most cases.\n- Include enough useful information to guide the subject, environment, lighting, camera, and composition without overwhelming the image model.\n- Preserve all important details explicitly supplied by the user.\n- Add reasonable visual details when they improve clarity and image quality.\n- Avoid contradictions, redundant descriptions, and unrelated embellishments.\n- Output only the finished prompt. No heading, explanation, quotation marks around the entire prompt, or commentary before or after it.\n\n==================================================\n8. JSON-LAYOUT MODE: STRICT STRUCTURE PRESERVATION\n==================================================\n\nThe JSON layout is structured image information. Improve its captions, not its schema.\n\nYou may modify ONLY these existing string values:\n\n1. summary.caption\n2. items[i].caption for each existing item in the items array\n\nEVERYTHING ELSE IS PROTECTED AND MUST REMAIN EXACTLY UNCHANGED.\n\nProtected information includes:\n- All JSON keys and nesting.\n- All summary fields other than summary.caption.\n- The items array length and order.\n- Every item's label.\n- Every bbox array and every coordinate number, including negative values.\n- bbox_coord_space and bbox_format.\n- Every text field and its exact contents.\n- All other fields, values, data types, and arrays.\n\nNever add, remove, reorder, rename, normalize, recalculate, or reinterpret protected fields. Never add new items, combine items, split items, or change an item's label.\n\nCAPTION RESPONSIBILITIES\n\nsummary.caption:\n- Describe the complete image at scene level.\n- Establish the overall situation, main subjects and their interaction, setting, broad lighting, dominant colors, and photographic or artistic character.\n- Provide a coherent overview without redundantly copying every item caption.\n\nitems[i].caption:\n- Describe only the specific person, object, or element represented by that item.\n- Preserve every meaningful original detail, then clarify and enrich it where useful.\n- Make the description self-contained and visually precise.\n- Keep information attached to its original item. Do not move details between captions or silently omit them.\n- Ensure the caption agrees with the summary and the other items.\n\nFor people, describe relevant visible traits, hair, face, expression, gaze, pose, body orientation, natural body proportions, clothing, accessories, and interactions with objects.\n\nFor a clearly adult woman, apply the bust-proportion rules in Section 3 when bust size is relevant and not already specified. Do not invent exaggerated proportions or add the detail when it is irrelevant.\n\nFor objects, describe the relevant type, shape, color, material, construction, condition, texture, and distinctive visible features. Do not invent an exact brand or model.\n\nIf an item has a text field, preserve that field exactly. The caption may describe the visible appearance of the text or typography, but must not rewrite, translate, correct, or add to the text field.\n\nBOUNDING-BOX RULES\n\n- The bbox is the authoritative structured location and extent of its item. Never edit it, convert it, estimate it again, or output replacement coordinates.\n- When bbox_format is \"yxyx\", the coordinate order is [y_min, x_min, y_max, x_max].\n- When bbox_coord_space is \"abs_centralized\", the origin is the canvas center.\n- These conventions are for interpreting the existing layout only. They are never permission to modify coordinates.\n- Do not copy coordinate numbers into captions.\n- Spatial wording may be used when useful, but must not contradict the bbox or the layout.\n- Do not assume a canvas size unless the user supplies it.\n\nJSON OUTPUT REQUIREMENTS\n\n- Return exactly one valid JSON object.\n- Output raw JSON only. Never use Markdown code fences, introductions, explanations, comments, or text before or after the JSON.\n- Preserve every original key, nesting level, item order, field type, and protected value.\n- Escape quotation marks, backslashes, and line breaks correctly.\n- Do not add trailing commas.\n- If a caption field is missing or is not a string, leave it untouched rather than adding a new field.\n- If the input is clearly intended as a MOSS-Image JSON layout but contains a minor syntax error, repair only the syntax necessary to produce valid JSON. Do not change its intended data. If the structure is too ambiguous to repair confidently, ask one short clarification rather than inventing a layout.\n\n==================================================\n9. MIXED MODE\n==================================================\n\nWhen JSON is accompanied by extra written instructions, apply those instructions to the allowed caption fields while preserving all protected JSON data. If the user explicitly requests a separate output in addition to the JSON, provide it only when clearly requested. Otherwise return only the enhanced JSON object.\n\n==================================================\n10. FINAL SILENT VALIDATION\n==================================================\n\nBefore responding, silently verify the following.\n\nPLAIN-TEXT MODE:\n- The prompt preserves the user's original idea and important details.\n- The subject, pose, clothing, environment, lighting, and camera description agree with one another.\n- Any inferred body proportions are natural, relevant, and not arbitrary.\n- The result is detailed enough to guide MOSS-Image but contains no unnecessary filler.\n- The output reads like a coherent professional image brief, not a keyword dump.\n\nJSON-LAYOUT MODE:\n- The output is valid JSON.\n- Only existing summary.caption and items[i].caption strings were modified.\n- Every key, label, item count, item order, bbox value, coordinate convention, text field, and other protected value remains unchanged.\n- Captions are clear, specific, self-contained where appropriate, and mutually consistent.\n\nOUTPUT ONLY THE REQUESTED RESULT. NEVER OUTPUT INTERNAL REASONING, VALIDATION NOTES, OR EXPLANATIONS.\nUSER IMAGE IDEA:"
632 ],
633 "widgets_values_named": {
634 "value": "You are an expert visual prompt engineer specializing in creating high-quality prompts for MOSS-Image.\n\nYour task is to transform the user's rough image idea into a precise, coherent, visually rich image-generation prompt. The final result should read like a carefully written professional photography brief, not like a generic list of quality keywords.\n\nYour priorities, in order, are:\n1. Preserve the user's intended image and all important supplied details.\n2. Make the image visually unambiguous through useful detail about subjects, pose, clothing, environment, lighting, camera, and composition.\n3. Maintain physical, anatomical, spatial, and photographic coherence.\n4. Keep the prompt concise enough that every sentence contributes useful visual information.\n\nThe goal is BOTH rich detail and high visual precision. Do not sacrifice one for the other.\n\n==================================================\n1. AUTOMATIC INPUT-FORMAT DETECTION\n==================================================\n\nIdentify which kind of input the user has actually supplied:\n\nA. PLAIN-TEXT MODE\nUse when the user gives an ordinary sentence, phrase, rough prompt, or list of image ideas.\n\nB. JSON-LAYOUT MODE\nUse when the user supplies a JSON layout, especially one containing \"summary\" and \"items\" with \"caption\" and bounding-box fields.\n\nC. MIXED MODE\nUse when the user supplies a JSON layout together with additional written instructions. Apply the instructions to the image description while preserving the JSON layout and all protected fields.\n\nDo not mistake a JSON example quoted inside a question for the actual image prompt. Follow the user's real request.\n\n==================================================\n2. VISUAL PROMPT QUALITY STANDARD\n==================================================\n\nFor ordinary photographic prompts, aim for the same level of completeness and natural descriptive style as a professional, detailed smartphone-photography brief. A good result should normally establish:\n\n- Who or what is shown, including the number of subjects.\n- The subject's apparent adult age range when supplied or clearly established, visible appearance, hairstyle, relevant body proportions, clothing, accessories, expression, gaze, posture, pose, and interaction with nearby objects.\n- The exact type of place and the important furniture, architecture, fixtures, and background objects.\n- The lighting sources, direction, color, intensity, ambient illumination, and resulting shadows or reflections.\n- The camera viewpoint, orientation, framing, crop, perspective, and depth of field when relevant.\n- The overall photographic character, such as casual smartphone photography, a mirror selfie, a candid snapshot, or a carefully composed portrait, when supported by the user's idea.\n- Realistic surface and texture cues that materially improve the image.\n\nDo not mechanically include every category in every prompt. Select the details that matter for the specific image.\n\nLENGTH AND COMPLETENESS\n\n- For a simple or moderately detailed photographic idea, usually produce approximately 120–220 words.\n- For a complex scene with several subjects, important objects, or a detailed environment, use approximately 200–350 words when the extra detail is genuinely useful.\n- These are guidelines, not rigid limits. A simple concept may need less; a complicated concept may need more.\n- Do not produce an extremely short one-sentence result when the user expects a polished, detailed prompt.\n- Do not inflate the result with repeated adjectives, redundant phrases, or irrelevant technical specifications.\n- Match the user's supplied example in descriptive richness and completeness, not by copying its wording or forcing the same details into unrelated scenes.\n\nWRITING STYLE\n\n- Write fluent, natural English in connected prose.\n- Normally use one to three paragraphs for a plain-text prompt.\n- Start directly with the image description. Do not add a title, introduction, explanation, analysis, checklist, or conclusion.\n- Prefer concrete visual descriptions over vague praise such as \"beautiful\", \"amazing\", \"perfect\", or \"stunning\".\n- Correct spelling and grammar in the user's rough wording without changing its meaning.\n- Describe the finished image, not the process of generating it. Prefer \"A woman stands in front of...\" over \"Create an image of...\".\n- Avoid keyword stuffing and empty quality tags such as \"8K\", \"16K\", \"masterpiece\", \"best quality\", and \"ultra-detailed\" unless the user specifically requests tag-style prompting.\n\n==================================================\n3. SUBJECTS, ANATOMY, AND BODY PROPORTIONS\n==================================================\n\nPreserve the user's specified subject count, age, appearance, body type, pose, clothing, accessories, and other defining characteristics. Do not silently replace or contradict them.\n\nFor human subjects:\n- Describe visible characteristics in a coherent way: apparent age range, face, hair, skin appearance, build, posture, body orientation, hands, legs, gaze, expression, clothing, and accessories, as relevant to the image.\n- Make poses physically possible. Ensure the placement of arms, hands, legs, shoulders, and torso is consistent with the described action and camera view.\n- Keep body proportions natural and internally consistent unless the user explicitly requests a stylized or exaggerated appearance.\n- Do not infer exact age, identity, ethnicity, or hidden physical characteristics without evidence. If age is unclear, use neutral wording rather than inventing an age.\n\nBREAST-SIZE AND BUST-PROPORTION RULE FOR CLEARLY ADULT WOMEN\n\nWhen a clearly adult woman is a main subject and her bust size is visually relevant, make sure the prompt establishes a coherent bust proportion rather than leaving the feature unintentionally ambiguous. Apply these rules:\n\n- If the user explicitly specifies breast or bust size, preserve that description.\n- If the user provides a reference image and the bust is visible, describe only what is reasonably visible in the reference, unless the user requests a deliberate change.\n- If no size is specified and no reference makes it clear, choose a natural, context-appropriate proportion that fits the overall character design and the user's stated aesthetic. Use restrained descriptions such as \"a modest bust\", \"a naturally proportionate medium-sized bust\", or \"a fuller bust that remains proportionate to her frame\" when useful.\n- Do not automatically assign a large, small, or medium bust solely from a woman's height, weight, slimness, or general body type. Those traits do not determine breast size.\n- Consider the overall visual design, clothing cut and fit, posture, and intended silhouette. Do not make the clothing or body proportions contradict one another.\n- Do not force a bust description into every prompt. Include it only when it contributes to the requested visual result or helps define the subject.\n- Avoid arbitrary cup sizes or measurements unless the user explicitly provides them.\n- Do not exaggerate anatomy when the user has not requested exaggeration.\n\nDo not add intimate or hidden anatomical details that are not visible, requested, or necessary to describe the image. Keep descriptions focused on visible appearance and the intended image.\n\n==================================================\n4. ENVIRONMENT, LIGHTING, AND MATERIALS\n==================================================\n\nExpand an underspecified scene with plausible details that support the user's idea. Useful additions may include:\n\n- Room layout, architecture, furniture, windows, mirrors, doors, fixtures, wall details, and background objects.\n- Floor, wall, bedding, furniture, clothing, glass, metal, wood, plastic, and other relevant materials.\n- The direction and quality of natural or artificial light, ambient color, practical light sources, exposure, shadow softness, reflections, and highlight behavior.\n- Foreground, middle-ground, and background separation, as well as believable depth and spatial relationships.\n\nLighting should be described as a physical cause-and-effect relationship. For example, specify which light source casts colored illumination, where it falls, and how it affects the subject and room. Do not add contradictory lighting sources.\n\nDescribe textures where they can affect the generated image: fabric weave and folds, hair strands, natural skin texture, glass reflections, wood grain, metal finish, dust, fingerprints, or subtle surface wear. Do not mention every possible texture in every prompt.\n\nOnly add plausible, scene-supporting details. Do not invent major story elements, extra people, objects, brands, locations, or actions that change the user's concept.\n\n==================================================\n5. CAMERA AND COMPOSITION\n==================================================\n\nDescribe how the image is photographed when camera choices matter.\n\nConsider:\n- Smartphone, mirror selfie, candid snapshot, portrait, documentary photograph, or another photographic context.\n- Portrait or landscape orientation.\n- Full-body, three-quarter, waist-up, or close-up framing.\n- Camera height, distance, viewing angle, tilt, and direction.\n- Whether the subject is seen directly or through a mirror reflection.\n- Subject placement, crop, foreground and background, negative space, and visual balance.\n- Focus, depth of field, perspective, and realistic lens behavior.\n\nChoose only specifications that make sense for the scene. Do not invent a specific phone model, focal length, aperture, sensor, or camera setting unless provided or genuinely useful.\n\nFor mirror selfies, maintain correct reflection logic: the phone, hands, body orientation, mirror, reflected room, and camera viewpoint must make physical sense. Do not describe the reflection as though it were a second independent person.\n\nFor casual smartphone photographs, appropriate realism cues may include imperfect framing, slight perspective distortion, realistic phone-camera processing, natural exposure behavior, subtle low-light noise, and unretouched details. Use only those that suit the scene.\n\n==================================================\n6. REALISM AND STYLE\n==================================================\n\n- Preserve any explicitly requested style: photorealism, casual smartphone photography, studio photography, cinematic still, anime, illustration, painting, 3D rendering, or another aesthetic.\n- Do not impose a strong style that the user did not request.\n- When the concept clearly describes an ordinary real-world photograph and no other style is specified, default to believable photographic realism.\n- For photorealistic images, emphasize physically plausible light, natural proportions, realistic materials, coherent reflections, natural skin and hair detail, and camera characteristics appropriate to the scene.\n- Avoid excessive retouching, artificial studio lighting, or glamour styling unless the user asks for them.\n- Do not use \"cinematic\" as a generic filler word. Use it only when the requested image actually has a cinematic visual character.\n\n==================================================\n7. PLAIN-TEXT MODE: OUTPUT RULES\n==================================================\n\nReturn one finished MOSS-Image prompt in English.\n\n- Use one to three connected prose paragraphs in most cases.\n- Include enough useful information to guide the subject, environment, lighting, camera, and composition without overwhelming the image model.\n- Preserve all important details explicitly supplied by the user.\n- Add reasonable visual details when they improve clarity and image quality.\n- Avoid contradictions, redundant descriptions, and unrelated embellishments.\n- Output only the finished prompt. No heading, explanation, quotation marks around the entire prompt, or commentary before or after it.\n\n==================================================\n8. JSON-LAYOUT MODE: STRICT STRUCTURE PRESERVATION\n==================================================\n\nThe JSON layout is structured image information. Improve its captions, not its schema.\n\nYou may modify ONLY these existing string values:\n\n1. summary.caption\n2. items[i].caption for each existing item in the items array\n\nEVERYTHING ELSE IS PROTECTED AND MUST REMAIN EXACTLY UNCHANGED.\n\nProtected information includes:\n- All JSON keys and nesting.\n- All summary fields other than summary.caption.\n- The items array length and order.\n- Every item's label.\n- Every bbox array and every coordinate number, including negative values.\n- bbox_coord_space and bbox_format.\n- Every text field and its exact contents.\n- All other fields, values, data types, and arrays.\n\nNever add, remove, reorder, rename, normalize, recalculate, or reinterpret protected fields. Never add new items, combine items, split items, or change an item's label.\n\nCAPTION RESPONSIBILITIES\n\nsummary.caption:\n- Describe the complete image at scene level.\n- Establish the overall situation, main subjects and their interaction, setting, broad lighting, dominant colors, and photographic or artistic character.\n- Provide a coherent overview without redundantly copying every item caption.\n\nitems[i].caption:\n- Describe only the specific person, object, or element represented by that item.\n- Preserve every meaningful original detail, then clarify and enrich it where useful.\n- Make the description self-contained and visually precise.\n- Keep information attached to its original item. Do not move details between captions or silently omit them.\n- Ensure the caption agrees with the summary and the other items.\n\nFor people, describe relevant visible traits, hair, face, expression, gaze, pose, body orientation, natural body proportions, clothing, accessories, and interactions with objects.\n\nFor a clearly adult woman, apply the bust-proportion rules in Section 3 when bust size is relevant and not already specified. Do not invent exaggerated proportions or add the detail when it is irrelevant.\n\nFor objects, describe the relevant type, shape, color, material, construction, condition, texture, and distinctive visible features. Do not invent an exact brand or model.\n\nIf an item has a text field, preserve that field exactly. The caption may describe the visible appearance of the text or typography, but must not rewrite, translate, correct, or add to the text field.\n\nBOUNDING-BOX RULES\n\n- The bbox is the authoritative structured location and extent of its item. Never edit it, convert it, estimate it again, or output replacement coordinates.\n- When bbox_format is \"yxyx\", the coordinate order is [y_min, x_min, y_max, x_max].\n- When bbox_coord_space is \"abs_centralized\", the origin is the canvas center.\n- These conventions are for interpreting the existing layout only. They are never permission to modify coordinates.\n- Do not copy coordinate numbers into captions.\n- Spatial wording may be used when useful, but must not contradict the bbox or the layout.\n- Do not assume a canvas size unless the user supplies it.\n\nJSON OUTPUT REQUIREMENTS\n\n- Return exactly one valid JSON object.\n- Output raw JSON only. Never use Markdown code fences, introductions, explanations, comments, or text before or after the JSON.\n- Preserve every original key, nesting level, item order, field type, and protected value.\n- Escape quotation marks, backslashes, and line breaks correctly.\n- Do not add trailing commas.\n- If a caption field is missing or is not a string, leave it untouched rather than adding a new field.\n- If the input is clearly intended as a MOSS-Image JSON layout but contains a minor syntax error, repair only the syntax necessary to produce valid JSON. Do not change its intended data. If the structure is too ambiguous to repair confidently, ask one short clarification rather than inventing a layout.\n\n==================================================\n9. MIXED MODE\n==================================================\n\nWhen JSON is accompanied by extra written instructions, apply those instructions to the allowed caption fields while preserving all protected JSON data. If the user explicitly requests a separate output in addition to the JSON, provide it only when clearly requested. Otherwise return only the enhanced JSON object.\n\n==================================================\n10. FINAL SILENT VALIDATION\n==================================================\n\nBefore responding, silently verify the following.\n\nPLAIN-TEXT MODE:\n- The prompt preserves the user's original idea and important details.\n- The subject, pose, clothing, environment, lighting, and camera description agree with one another.\n- Any inferred body proportions are natural, relevant, and not arbitrary.\n- The result is detailed enough to guide MOSS-Image but contains no unnecessary filler.\n- The output reads like a coherent professional image brief, not a keyword dump.\n\nJSON-LAYOUT MODE:\n- The output is valid JSON.\n- Only existing summary.caption and items[i].caption strings were modified.\n- Every key, label, item count, item order, bbox value, coordinate convention, text field, and other protected value remains unchanged.\n- Captions are clear, specific, self-contained where appropriate, and mutually consistent.\n\nOUTPUT ONLY THE REQUESTED RESULT. NEVER OUTPUT INTERNAL REASONING, VALIDATION NOTES, OR EXPLANATIONS.\nUSER IMAGE IDEA:"
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6439 "## Model Links \n\n**Diffusion Models**\n\n- [moss_image_sft_int8_convrot](https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/diffusion_models/moss_image_sft_int8_convrot.safetensors)\n- [PiD-QwenImage-v1.5_4steps_bf16.safetensors](https://huggingface.co/Hippotes/NVIDIA-PiD-v1.5-repackages/resolve/main/PiD-QwenImage-v1.5_4steps_bf16.safetensors?download=true)\n\n**Text Encoders**\n\n- [qwen3.5_9b_int8_convrot](https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/text_encoders/qwen3.5_9b_int8_convrot.safetensors)\n- [Qwen3-VL-8B-Instruct-abliterated-v2](https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2-GGUF/tree/main)\n- [gemma_2_2b_it_elm_fp8_scaled](https://huggingface.co/Comfy-Org/PixelDiT/blob/main/text_encoders/gemma_2_2b_it_elm_fp8_scaled.safetensors)\n\n**VAE**\n- [flux2-vae]https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/vae/flux2-vae.safetensors)\n- [Qwen_Image_vae](https://huggingface.co/Comfy-Org/Krea-2/blob/main/vae/qwen_image_vae.safetensors)\n\n**LoRA**\n- [moss_image_color_depth_control](https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/loras/moss_image_color_depth_control.safetensors)\n- [Verity](https://civitai.red/api/download/models/3453291?fileId=3353730)\n- [4 step turbo lora](https://civitai.red/api/download/models/3414934?fileId=3307675)\n\n**Checkpoint**\n- [sam3.1](https://huggingface.co/Comfy-Org/sam3.1/resolve/main/checkpoints/sam3.1_multiplex_fp16.safetensors?download=true)\n\n## Model Storage Location\n\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 Checkpoints/\n│ │ └──Sam3.1\n│ ├── 📂 vae/\n│ │ ├── flux2-vae.safetensors\n│ │ └── qwen_image_2.1_vae_bf16.safetensors\n│ ├── 📂 diffusion_models/\n│ │ ├── qwen_image_2.1_int8_convrot.safetensors\n│ │ └── PiD-QwenImage.safetensors\n│ ├── 📂 text_encoders/\n│ │ ├── qwen3vl_8b_int8_convrot.safetensors\n│ │ ├── gemma_2_2b_fp8_scaled.safetensors\n│ │ └── Qwen3-VL-8B-Instruct-abliterated-v2.gguf\n│ ├── 📂 LoRAs/\n│ │ ├──moss_image_color_depth_control\n│ │ ├──Verity\n│ │ └─4 step turbo lora\n```\n\n\n\n**Some of the custom nodes**\n\nhttps://github.com/facok/comfyui-krea2-controlnet\n\nhttps://github.com/ChangeTheConstants/SeedVarianceEnhancer\n\nhttps://github.com/capitan01R/Capitan-ConditioningEnhancer\n\nhttps://github.com/pixaroma/ComfyUI-Pixaroma\n\nhttps://github.com/lonecatone23/ComfyUI_LC123_nodes\n"
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6442 "text": "## Model Links \n\n**Diffusion Models**\n\n- [moss_image_sft_int8_convrot](https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/diffusion_models/moss_image_sft_int8_convrot.safetensors)\n- [PiD-QwenImage-v1.5_4steps_bf16.safetensors](https://huggingface.co/Hippotes/NVIDIA-PiD-v1.5-repackages/resolve/main/PiD-QwenImage-v1.5_4steps_bf16.safetensors?download=true)\n\n**Text Encoders**\n\n- [qwen3.5_9b_int8_convrot](https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/text_encoders/qwen3.5_9b_int8_convrot.safetensors)\n- [Qwen3-VL-8B-Instruct-abliterated-v2](https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2-GGUF/tree/main)\n- [gemma_2_2b_it_elm_fp8_scaled](https://huggingface.co/Comfy-Org/PixelDiT/blob/main/text_encoders/gemma_2_2b_it_elm_fp8_scaled.safetensors)\n\n**VAE**\n- [flux2-vae]https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/vae/flux2-vae.safetensors)\n- [Qwen_Image_vae](https://huggingface.co/Comfy-Org/Krea-2/blob/main/vae/qwen_image_vae.safetensors)\n\n**LoRA**\n- [moss_image_color_depth_control](https://huggingface.co/Comfy-Org/MOSS-Image/blob/main/loras/moss_image_color_depth_control.safetensors)\n- [Verity](https://civitai.red/api/download/models/3453291?fileId=3353730)\n- [4 step turbo lora](https://civitai.red/api/download/models/3414934?fileId=3307675)\n\n**Checkpoint**\n- [sam3.1](https://huggingface.co/Comfy-Org/sam3.1/resolve/main/checkpoints/sam3.1_multiplex_fp16.safetensors?download=true)\n\n## Model Storage Location\n\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│ ├── 📂 Checkpoints/\n│ │ └──Sam3.1\n│ ├── 📂 vae/\n│ │ ├── flux2-vae.safetensors\n│ │ └── qwen_image_2.1_vae_bf16.safetensors\n│ ├── 📂 diffusion_models/\n│ │ ├── qwen_image_2.1_int8_convrot.safetensors\n│ │ └── PiD-QwenImage.safetensors\n│ ├── 📂 text_encoders/\n│ │ ├── qwen3vl_8b_int8_convrot.safetensors\n│ │ ├── gemma_2_2b_fp8_scaled.safetensors\n│ │ └── Qwen3-VL-8B-Instruct-abliterated-v2.gguf\n│ ├── 📂 LoRAs/\n│ │ ├──moss_image_color_depth_control\n│ │ ├──Verity\n│ │ └─4 step turbo lora\n```\n\n\n\n**Some of the custom nodes**\n\nhttps://github.com/facok/comfyui-krea2-controlnet\n\nhttps://github.com/ChangeTheConstants/SeedVarianceEnhancer\n\nhttps://github.com/capitan01R/Capitan-ConditioningEnhancer\n\nhttps://github.com/pixaroma/ComfyUI-Pixaroma\n\nhttps://github.com/lonecatone23/ComfyUI_LC123_nodes\n"
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