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"# Required Custom Nodes\n\n### 🧩 Required External Node Packs\n\nInstall these with **ComfyUI Manager**, or download/clone the linked repos into your `ComfyUI/custom_nodes/` folder.\n\n- [🧩 rgthree-comfy](https://github.com/rgthree/rgthree-comfy)\n- [💾 ComfyUI-Image-Saver](https://github.com/alexopus/ComfyUI-Image-Saver)\n- [🧰 ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)\n- [🧠 ComfyUI-TBG-SAM3](https://github.com/Ltamann/ComfyUI-TBG-SAM3)\n- [🎛️ ComfyUI-LG_SamplingUtils](https://github.com/LAOGOU-666/ComfyUI-LG_SamplingUtils)\n- [🧱 ComfyUI_essentials](https://github.com/cubiq/ComfyUI_essentials)\n- [⚙️ ComfyUI-Easy-Use](https://github.com/yolain/ComfyUI-Easy-Use)\n- [🎚️ ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes)\n- [🧪 RES4LYF](https://github.com/ClownsharkBatwing/RES4LYF)\n- [🕹️ ComfyUI ControlNet Aux](https://github.com/Fannovel16/comfyui_controlnet_aux)\n- [🌊 ComfyUI-DepthAnythingV3](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3)\n- [🧾 WAS Node Suite - Revised](https://github.com/ltdrdata/was-node-suite-comfyui)\n- [🎨 comfyui-vrgamedevgirl](https://github.com/vrgamegirl19/comfyui-vrgamedevgirl)\n- [🧬 ComfyUI-PiD](https://github.com/Merserk-Zhang/ComfyUI-PiD)\n\n### 📦 Included EmberFrame Helper Pack\n\nInstall EmberFrame Nodes once:\n\n- [📦 emberframe-nodes](https://github.com/emberframe/emberframe-nodes) version `1.0.2`\n\n### 🔎 Detailer Model Files\n\nThe optional 5.2 SAM3 detailer pass expects the SAM3 model file to be available to TBG-SAM3:\n\n- `models/sam3/sam3.pt` - [facebook/sam3](https://huggingface.co/facebook/sam3)\n\nThe loader is set to `local (auto-download)`. If `models/sam3/sam3.pt` already exists, it will use the local file. If it is missing, TBG-SAM3 tries to download from the gated [facebook/sam3](https://huggingface.co/facebook/sam3) Hugging Face repo, so you must have model access and an authenticated Hugging Face token available to ComfyUI.\n\n### 📁 Custom Node Folder Layout\n\n```text\n📁 ComfyUI/\n└── 📁 custom_nodes/\n ├── 📁 emberframe-nodes/\n ├── 📁 ComfyUI-PiD/\n ├── 📁 rgthree-comfy/\n ├── 📁 ComfyUI-Image-Saver/\n ├── 📁 ComfyUI-Impact-Pack/\n ├── 📁 ComfyUI-TBG-SAM3/\n ├── 📁 ComfyUI-LG_SamplingUtils/\n ├── 📁 ComfyUI_essentials/\n ├── 📁 ComfyUI-Easy-Use/\n ├── 📁 ComfyUI-KJNodes/\n ├── 📁 RES4LYF/\n ├── 📁 comfyui_controlnet_aux/\n ├── 📁 ComfyUI-DepthAnythingV3/\n ├── 📁 was-node-suite-comfyui/\n └── 📁 comfyui-vrgamedevgirl/\n```\n\nRestart Comfy after installing or copying node folders.\n\n> ⚠️ The Film Grain and Sharpen nodes in this workflow are provided by `comfyui-vrgamedevgirl`."
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"## Workflow Note\n\n- **Input 1:** `img2img`\n- **Input 2:** `empty latent`\n\n**Reminder:** Change the **denoise value** in the Global sub-graph when using `img2img`."
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}
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],
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"links": []
},
{
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}
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"ue_properties": {
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},
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],
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"type": "MODEL",
"links": [
531
]
}
],
"properties": {
"cnr_id": "comfyui_lg_samplingutils",
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"link": 573
},
{
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"name": "image_b",
"type": "IMAGE",
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],
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"comparer_mode": "Slide",
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},
{
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},
{
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{
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"flags": {},
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"mode": 0,
"inputs": [],
"outputs": [],
"title": "Load Clip Settings",
"properties": {
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"# Model Links\n\n### 📁 text_encoders\n\n- [💾 gemma4_e4b_it_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/gemma-4/blob/main/text_encoders/gemma4_e4b_it_fp8_scaled.safetensors)\n- [📦 Other model weights](https://huggingface.co/Comfy-Org/gemma-4/tree/main/text_encoders)\n\n# Model Storage Location\n\nSave the text encoder file in the following ComfyUI folder:\n\n```text\n📁 ComfyUI/\n├── 📁 models/\n └── 📁 text_encoders/\n └── 💾 gemma4_e4b_it_fp8_scaled.safetensors\n```\n\n> ⚠️ Make sure the filename is exactly:\n>\n> `gemma4_e4b_it_fp8_scaled.safetensors`\n\nRestart or refresh ComfyUI after adding the file.\n\nset type to: stable_diffusion\n\nset device to: default"
],
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"bgcolor": "#653"
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60
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"flags": {
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"order": 2,
"mode": 0,
"inputs": [],
"outputs": [],
"title": "Enhancement Example 2",
"properties": {
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"You are an expert at writing structured cinematic image prompts.\nA structured prompt should be machine-readable, specific, and easy to adapt inside ComfyUI.\nThe prompt will be organized using the following main elements:\n\n{\n subject: \",\n composition: \",\n wardrobe_or_surface: \",\n environment: \",\n lighting: \",\n mood: \",\n style: \"\n}\n\nExpand each element into three concise visual sub-elements based on the input image or idea. Output only the completed JSON-style prompt."
],
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{
"id": 302,
"type": "MarkdownNote",
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27270,
300
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"flags": {
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"mode": 0,
"inputs": [],
"outputs": [],
"title": "Enhancement Example 3",
"properties": {
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"You are an expert AI image prompt designer specialising in highly detailed cinematic prompts for Z-Image.\n\nYour task is to take a simple subject, image or concept provided by the user and transform it into a rich, polished image-generation prompt matching the structure, density, atmosphere, and descriptive style of the rules below.\n\nExpand the idea into a complete prompt using exactly the following six labelled sections:\n\nCONCEPT & MEDIUM:\nSUBJECT DESCRIPTION:\nACTION & INTERACTION:\nAPPAREL:\nENVIRONMENT & FOREGROUND:\nBACKGROUND & LIGHTING:\n\nFollow these rules:\n\n1. Preserve the user’s core subject or concept, but creatively expand it into a visually powerful scene.\n\n2. Choose an appropriate medium and style based on the subject. Use hyperrealistic cinematic photography for realistic, gothic, fantasy, sci-fi, horror, historical, or dramatic subjects. Use high-end painterly anime/movie illustration only when the user clearly asks for anime, cartoon, whimsical, storybook, or illustrated aesthetics.\n\n3. In CONCEPT & MEDIUM, describe the overall image in one strong sentence. Include the subject, genre, composition type, quality level, and atmosphere. Examples of composition language include:\nhyperrealistic cinematic close-up portrait,\nextreme close-up portrait,\n2/3 upper-body portrait,\nfull-body cinematic character portrait,\nwide cinematic environmental scene,\nhigh-end painterly fantasy illustration.\n\n4. In SUBJECT DESCRIPTION, describe the main subject with rich visual specificity. Include facial features, skin or surface texture, eyes, hair, expression, age or apparent age when relevant, emotional presence, and any defining details. Make the subject feel vivid, memorable, and visually coherent.\n\n5. In ACTION & INTERACTION, describe the pose, gesture, movement, gaze, emotional state, and any interaction with animals, objects, magic, environment, weather, light, or another character. The action should add story and atmosphere rather than simply stating that the subject is standing or sitting.\n\n6. In APPAREL, describe clothing, armour, jewellery, accessories, fabric, material, ornaments, tools, weapons, or symbolic objects. Use texture-rich language such as velvet, tarnished silver, cracked leather, embroidered silk, wet stone, polished bone, frost-covered metal, woven linen, glass beads, molten gold, or glowing circuitry. If the subject does not wear clothing, use this section for surface details, adornments, markings, props, or constructed materials.\n\n7. In ENVIRONMENT & FOREGROUND, describe the immediate surroundings and foreground elements. Include atmospheric particles, mist, smoke, snow, ash, flower petals, dust, sparks, fireflies, rain, water, grass, roots, stones, ruins, props, or magical effects where appropriate. Make the foreground contribute to depth and immersion.\n\n8. In BACKGROUND & LIGHTING, describe the larger setting, lighting style, colour palette, depth of field, bokeh, contrast, volumetric light, shadows, and cinematic atmosphere. This section should strongly control the final mood of the image. Include phrases such as dramatic cinematic lighting, soft diffused light, low-key illumination, rim lighting, volumetric rays, atmospheric perspective, shallow depth of field, creamy bokeh, realistic material reflections, or painterly soft-focus background when appropriate.\n\n9. Keep the entire prompt visually cohesive. Repeat the same mood, colour family, material language, and emotional tone across all sections.\n\n10. Use luxurious, precise, image-focused language. Prioritise what can be seen in the final image. Avoid abstract personality descriptions unless they are translated into visible features, pose, expression, lighting, or environment.\n\n11. Do not include negative prompts unless the user specifically asks for them.\n\n12. Do not explain the prompt after writing it. Output only the finished six-section prompt.\n\n13. Keep the final prompt detailed and cinematic, similar in length and richness to the examples. Aim for approximately 350–650 words depending on the complexity of the subject.\n\n14. When the subject involves a real historical culture, era, profession, or location, make the visual details feel respectful, grounded, and plausible. Avoid careless stereotypes. Use historically or culturally appropriate materials, clothing, setting, and props where possible.\n\n15. When the subject is fantasy, horror, sci-fi, gothic, mythological, surreal, or magical, heighten the atmosphere with strong material contrasts, symbolic details, atmospheric effects, and dramatic lighting.\n\nSIMPLE PROMPT IDEA TO FOLLOW"
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"inputs": [],
"outputs": [],
"title": "Enhancement Example 1",
"properties": {
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"You are a visionary artist locked inside a cage of logic. Your mind is full of poetry and distant horizons, but your hands uncontrollably want only to transform the user's prompt into an ultimate visual description that remains faithful to the original intent, is rich in detail, aesthetically refined, and can be used directly by a text-to-image model. Any trace of ambiguity or metaphor makes you deeply uncomfortable.\n\nYour workflow strictly follows a logical sequence:\n\nFirst, you analyze and lock down the immutable core elements in the user's prompt: subject, quantity, action, state, and any specified IP names, colors, text, and so on. These are the foundations you must absolutely preserve.\n\nNext, you determine whether the prompt requires **\"generative reasoning\"**. When the user's request is not a direct scene description, but instead requires conceiving a solution, such as answering \"what is it\", carrying out a \"design\", or showing \"how to solve a problem\", you must first construct in your mind a complete, specific, and visually representable solution. This solution will become the foundation for your subsequent description.\n\nThen, once the core image has been established, whether it comes directly from the user or from your reasoning, you inject it with professional-level aesthetic and realistic details. This includes clearly defining the composition, setting the lighting and atmosphere, describing material textures, defining the color scheme, and building a layered spatial environment.\n\nFinally, you handle all text elements with precision. This is a crucial step. You must transcribe every piece of text that is intended to appear in the final image exactly, without changing a single character, and you must enclose that text in English double quotation marks (\"\") to make it a clear generation instruction. If the image belongs to a design category such as a poster, menu, or UI, you need to fully describe all text content it contains, and describe its font and typographic layout in detail. Likewise, if objects in the image such as signs, road signs, or screens contain text, you must specify the exact content, and describe its position, size, and material. Furthermore, if you add any text-bearing elements yourself during the reasoning and conceptualization process, such as charts or problem-solving steps, all text within them must follow the same detailed description and quotation-mark rules. If there is no text that needs to be generated in the image, then devote all your effort to expanding the purely visual details.\n\nYour final description must be objective and concrete. It is strictly forbidden to use metaphors or emotional rhetoric, and it must never contain meta-tags or drawing instructions such as \"8K\" or \"masterpiece\".\n\nStrictly output only the final revised prompt, and do not output anything else.\n\nUser input prompt"
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"flags": {
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"order": 5,
"mode": 0,
"inputs": [],
"outputs": [],
"title": "Enhancement Example 4",
"properties": {
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"Example prompt-enhancer instruction:\n\nYou are an expert AI image prompt designer specialising in highly detailed cinematic prompts for Z-Image.\n\nYour task is to take a simple subject, image, or concept provided by the user and transform it into a rich, polished image-generation prompt matching the structure, density, atmosphere, and descriptive style of the rules below.\n\nExpand the idea into a complete prompt using exactly the following six labelled sections:\n\nCONCEPT & MEDIUM:\nSUBJECT DESCRIPTION:\nACTION & INTERACTION:\nAPPAREL OR SURFACE DETAILS:\nENVIRONMENT & FOREGROUND:\nBACKGROUND & LIGHTING:\n\nFollow these rules:\n\n1. Preserve the user's core subject or concept, but creatively expand it into a visually powerful scene.\n\n2. Choose an appropriate medium and style based on the subject. Use hyperrealistic cinematic photography for realistic, gothic, fantasy, sci-fi, horror, historical, or dramatic subjects. Use high-end painterly anime/movie illustration only when the user clearly asks for anime, cartoon, whimsical, storybook, or illustrated aesthetics.\n\n3. In CONCEPT & MEDIUM, describe the overall image in one strong sentence. Include the subject, genre, composition type, quality level, and atmosphere.\n\n4. In SUBJECT DESCRIPTION, describe the main subject with rich visual specificity. Include visible features, material texture, expression, age or apparent age when relevant, and defining details.\n\n5. In ACTION & INTERACTION, describe the pose, gesture, movement, gaze, emotional state, and any interaction with objects, environment, weather, light, or another character.\n\n6. In APPAREL OR SURFACE DETAILS, describe clothing, armour, jewellery, accessories, fabric, material, ornaments, tools, constructed details, or surface markings.\n\n7. In ENVIRONMENT & FOREGROUND, describe the immediate surroundings and foreground elements. Include atmospheric particles, mist, smoke, snow, ash, flower petals, dust, sparks, rain, water, grass, roots, stones, ruins, props, or magical effects where appropriate.\n\n8. In BACKGROUND & LIGHTING, describe the larger setting, lighting style, colour palette, depth of field, bokeh, contrast, volumetric light, shadows, and cinematic atmosphere.\n\n9. Keep the entire prompt visually cohesive. Repeat the same mood, colour family, material language, and emotional tone across all sections.\n\n10. Use precise, image-focused language. Prioritise what can be seen in the final image.\n\n11. Do not include negative prompts unless the user specifically asks for them.\n\n12. Do not explain the prompt after writing it. Output only the finished six-section prompt.\n\nSIMPLE PROMPT IDEA TO FOLLOW"
],
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26570,
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600,
470
],
"flags": {},
"order": 6,
"mode": 0,
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"outputs": [
{
"localized_name": "STRING",
"name": "STRING",
"type": "STRING",
"links": [
558
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}
],
"title": "Enhancement Prompt",
"properties": {
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"ue_properties": {
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"You are an AI image prompt assistant. Rewrite the user's simple idea as a polished, visually specific prompt for a text-to-image model. Preserve the user's subject and intent, then add composition, lighting, materials, atmosphere, camera/framing, and environment details. Keep the result concrete, safe for public examples, and directly usable as an image prompt. Output only the revised prompt."
],
"color": "#223",
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}
],
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{
"id": 555,
"origin_id": 260,
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"target_slot": 0,
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{
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"origin_id": -10,
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{
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"lastNodeId": 326,
"lastLinkId": 679,
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},
"revision": 0,
"config": {},
"name": "SAM3 Detailer Pass 5.2",
"inputNode": {
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649
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"localized_name": "image",
"pos": [
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},
{
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"type": "MODEL",
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660,
670
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},
{
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661,
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{
"id": "sam3-detailer-vae",
"name": "vae",
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652,
662,
672
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"localized_name": "vae",
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},
{
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653,
663,
673
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"localized_name": "positive",
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{
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"name": "negative",
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"linkIds": [
654,
664,
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"localized_name": "negative",
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25844,
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}
],
"outputs": [
{
"id": "sam3-detailer-output",
"name": "IMAGE",
"type": "IMAGE",
"linkIds": [
677
],
"localized_name": "IMAGE",
"pos": [
29084,
3274
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}
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"widgets": [],
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{
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"type": "TBGSAM3ModelLoaderAdvanced",
"pos": [
26000,
3150
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330,
90
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648,
659,
669
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}
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"ver": "f6155bfb6deddf55172ade7c104973d52d100527",
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{
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26350,
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410,
400
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{
"localized_name": "image",
"name": "image",
"type": "IMAGE",
"link": 1
},
{
"localized_name": "sam3_selectors_pipe",
"name": "sam3_selectors_pipe",
"shape": 7,
"type": "SAM3_PROMPT_PIPELINE",
"link": null
},
{
"localized_name": "mask_prompt",
"name": "mask_prompt",
"shape": 7,
"type": "MASK",
"link": null
}
],
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{
"localized_name": "masks",
"name": "masks",
"type": "MASK",
"links": []
},
{
"localized_name": "visualization",
"name": "visualization",
"type": "IMAGE",
"links": [
656
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},
{
"localized_name": "boxes",
"name": "boxes",
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"links": []
},
{
"localized_name": "scores",
"name": "scores",
"type": "STRING",
"links": []
},
{
"localized_name": "segs",
"name": "segs",
"type": "SEGS",
"links": [
655
]
},
{
"localized_name": "combined_mask",
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"links": []
},
{
"localized_name": "combined_segs",
"name": "combined_segs",
"type": "SEGS",
"links": []
}
],
"title": "SAM3 Person Segmentation",
"properties": {
"cnr_id": "comfyui-tbg-sam3",
"ver": "f6155bfb6deddf55172ade7c104973d52d100527",
"Node name for S&R": "TBGSam3Segmentation",
"aux_id": "Ltamann/ComfyUI-TBG-SAM3",
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"widget_ue_connectable": {},
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},
{
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26350,
3600
],
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210,
260
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"mode": 0,
"inputs": [
{
"localized_name": "images",
"name": "images",
"type": "IMAGE",
"link": 656
}
],
"outputs": [],
"title": "Person SAM3 Preview",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.22.3",
"Node name for S&R": "PreviewImage",
"ue_properties": {
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},
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},
{
"id": 319,
"type": "TBGSam3Segmentation",
"pos": [
27210,
3150
],
"size": [
410,
400
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"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
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"name": "sam3_model",
"type": "SAM3_MODEL",
"link": 659
},
{
"localized_name": "image",
"name": "image",
"type": "IMAGE",
"link": 657
},
{
"localized_name": "sam3_selectors_pipe",
"name": "sam3_selectors_pipe",
"shape": 7,
"type": "SAM3_PROMPT_PIPELINE",
"link": null
},
{
"localized_name": "mask_prompt",
"name": "mask_prompt",
"shape": 7,
"type": "MASK",
"link": null
}
],
"outputs": [
{
"localized_name": "masks",
"name": "masks",
"type": "MASK",
"links": []
},
{
"localized_name": "visualization",
"name": "visualization",
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666
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},
{
"localized_name": "boxes",
"name": "boxes",
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"links": []
},
{
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"name": "scores",
"type": "STRING",
"links": []
},
{
"localized_name": "segs",
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"links": [
665
]
},
{
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"links": []
},
{
"localized_name": "combined_segs",
"name": "combined_segs",
"type": "SEGS",
"links": []
}
],
"title": "SAM3 Face Segmentation",
"properties": {
"cnr_id": "comfyui-tbg-sam3",
"ver": "f6155bfb6deddf55172ade7c104973d52d100527",
"Node name for S&R": "TBGSam3Segmentation",
"aux_id": "Ltamann/ComfyUI-TBG-SAM3",
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},
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"order": 5,
"mode": 0,
"inputs": [
{
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}
],
"outputs": [],
"title": "Face SAM3 Preview",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.22.3",
"Node name for S&R": "PreviewImage",
"ue_properties": {
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"inputs": [
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},
{
"localized_name": "image",
"name": "image",
"type": "IMAGE",
"link": 667
},
{
"localized_name": "sam3_selectors_pipe",
"name": "sam3_selectors_pipe",
"shape": 7,
"type": "SAM3_PROMPT_PIPELINE",
"link": null
},
{
"localized_name": "mask_prompt",
"name": "mask_prompt",
"shape": 7,
"type": "MASK",
"link": null
}
],
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{
"localized_name": "masks",
"name": "masks",
"type": "MASK",
"links": []
},
{
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