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[t5xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp8_e4m3fn_scaled.safetensors)\n\nModel Storage Location\n\n```\n📂 ComfyUI/\n├── 📂 models/\n│   ├── 📂 diffusion_models/\n│   │   └── flux1-dev-kontext_fp8_scaled.safetensors\n│   ├── 📂 vae/\n│   │   └── ae.safetensor\n│   └── 📂 text_encoders/\n│       ├── clip_l.safetensors\n│       └── t5xxl_fp16.safetensors 或者 t5xxl_fp8_e4m3fn_scaled.safetensors\n```\n"]},{"id":97,"pos":[362.3786315917969,-347.3416748046875],"mode":0,"size":[210,88],"type":"PreviewAny","color":"#223","flags":{},"order":28,"title":"h","inputs":[{"link":123,"name":"source","type":"*"}],"bgcolor":"#335","outputs":[],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"PreviewAny"},"widgets_values":[]},{"id":88,"pos":[1284.67529296875,417.93011474609375],"mode":4,"size":[211.60000610351562,46],"type":"ReferenceLatent","color":"#223","flags":{},"order":47,"inputs":[{"link":80,"name":"conditioning","type":"CONDITIONING","label":"conditioning"},{"link":74,"name":"latent","type":"LATENT","label":"latent","shape":7}],"bgcolor":"#335","outputs":[{"name":"CONDITIONING","type":"CONDITIONING","label":"CONDITIONING","links":[138]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"ReferenceLatent"},"widgets_values":[]},{"id":102,"pos":[-1615.1978759765625,64.09248352050781],"mode":0,"size":[390.7500915527344,159.8812713623047],"type":"MarkdownNote","color":"#223","flags":{},"order":1,"inputs":[],"bgcolor":"#335","outputs":[],"properties":{},"widgets_values":["Download the model from [HuggingFace](https://huggingface.co/mit-han-lab/nunchaku-flux.1-kontext-dev) or [ModelScope](https://modelscope.cn/models/Lmxyy1999/nunchaku-flux.1-kontext-dev).\n\n- Use the **FP4** variant if you're running on **Blackwell (50-series) GPUs**.\n- Otherwise, choose the **INT4** version for better compatibility.\n\nYou can adjust the `cache_threshold` parameter to balance **image quality** and **inference speed**. A value of `0.12` typically offers a good trade-off.\n\nEnable `cpu_offload` to **save GPU memory** if you're running into memory limits.\n\nThe Turbo LoRA can be found at this [HuggingFace Repo](https://huggingface.co/alimama-creative/FLUX.1-Turbo-Alpha)."]},{"id":109,"pos":[1613.786376953125,-473.7441101074219],"mode":0,"size":[270,30],"type":"FluxKontextImageScale","color":"#223","flags":{"collapsed":false},"order":39,"inputs":[{"link":102,"name":"image","type":"IMAGE"}],"bgcolor":"#335","outputs":[{"name":"IMAGE","type":"IMAGE","links":[115]}],"properties":{"ver":"0.3.38","cnr_id":"comfy-core","Node name for S&R":"FluxKontextImageScale"},"widgets_values":[]},{"id":115,"pos":[1611.7578125,-195.20407104492188],"mode":0,"size":[282.76934814453125,220.15380859375],"type":"MarkdownNote","color":"#223","flags":{},"order":2,"title":"About multiple images reference","inputs":[],"bgcolor":"#335","outputs":[],"properties":{},"widgets_values":["[English] In addition to using **Image Stitch** to combine two images at a time, you can also encode individual images, then concatenate multiple latent conditions using the **ReferenceLatent** node, thus achieving the purpose of referencing multiple images. You can use the **EmptySD3LatentImage** node on the right to connect to **KSamper** and customize the size of the **latent_image**.\n\n[中文] 除了使用 **Image Stitch** 将两个两个图像拼合之外，你同样可以将单独的图像 encode 之后，将多个 latent 条件使用 **ReferenceLatent** 节点串联，从而实现多张图像参考的目的。可以使用右边的 **EmptySD3LatentImage** 节点连接到 **KSamper**来自定义 **latent_image** 的尺寸"]},{"id":30,"pos":[887.9183959960938,547.4077758789062],"mode":0,"size":[270,130],"type":"ModelSamplingFlux","color":"#223","flags":{},"order":26,"inputs":[{"link":129,"name":"model","type":"MODEL","label":"model"},{"link":118,"name":"width","type":"INT","widget":{"name":"width"}},{"link":121,"name":"height","type":"INT","widget":{"name":"height"}}],"bgcolor":"#335","outputs":[{"name":"MODEL","type":"MODEL","label":"MODEL","links":[156]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"ModelSamplingFlux"},"widgets_values":[1.3500000000000003,0.45000000000000007,1024,512]},{"id":96,"pos":[360.3193359375,-481.0399169921875],"mode":0,"size":[210,88],"type":"PreviewAny","color":"#223","flags":{},"order":25,"title":"w","inputs":[{"link":120,"name":"source","type":"*"}],"bgcolor":"#335","outputs":[],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"PreviewAny"},"widgets_values":[]},{"id":94,"pos":[605.6663818359375,-489.8450012207031],"mode":0,"size":[227.1779022216797,196.51930236816406],"type":"Note","color":"#223","flags":{},"order":3,"inputs":[],"bgcolor":"#335","outputs":[],"properties":{},"widgets_values":["YOU CAN USE THIS WORKFLOW WITH UP tO TWO iMAGES DISSABLE THE gROUPS CONCEPTS YOU DON'T NEED \n\n1-. FIRST IMAGE IS THE MAIN SUBJECT YOU WANT TO EDIT OR WORK FROM\n\n2-. SECOND IS FOR A PROP OR COSTUME OR A STYLE YOU WANT TO REPLICATE\n\n3-. Dissable The BirefNet BACKGROUND Romover if you want to include the image BG"]},{"id":129,"pos":[-2048.91552734375,-275.0779724121094],"mode":0,"size":[386.6929626464844,971.653564453125],"type":"MarkdownNote","color":"#223","flags":{},"order":4,"title":"Flux Kontext Prompt Techniques","inputs":[],"bgcolor":"#335","outputs":[],"properties":{},"widgets_values":["\n## Flux Kontext Prompt Techniques\n\n### 1. Basic Modifications\n- Simple and direct: `\"Change the car color to red\"`\n- Maintain style: `\"Change to daytime while maintaining the same style of the painting\"`\n\n### 2. Style Transfer\n**Principles:**\n- Clearly name style: `\"Transform to Bauhaus art style\"`\n- Describe characteristics: `\"Transform to oil painting with visible brushstrokes, thick paint texture\"`\n- Preserve composition: `\"Change to Bauhaus style while maintaining the original composition\"`\n\n### 3. Character Consistency\n**Framework:**\n- Specific description: `\"The woman with short black hair\"` instead of \"she\"\n- Preserve features: `\"while maintaining the same facial features, hairstyle, and expression\"`\n- Step-by-step modifications: Change background first, then actions\n\n### 4. Text Editing\n- Use quotes: `\"Replace 'joy' with 'BFL'\"`\n- Maintain format: `\"Replace text while maintaining the same font style\"`\n\n## Common Problem Solutions\n\n### Character Changes Too Much\n❌ Wrong: `\"Transform the person into a Viking\"`\n✅ Correct: `\"Change the clothes to be a viking warrior while preserving facial features\"`\n\n### Composition Position Changes\n❌ Wrong: `\"Put him on a beach\"`\n✅ Correct: `\"Change the background to a beach while keeping the person in the exact same position, scale, and pose\"`\n\n### Style Application Inaccuracy\n❌ Wrong: `\"Make it a sketch\"`\n✅ Correct: `\"Convert to pencil sketch with natural graphite lines, cross-hatching, and visible paper texture\"`\n\n## Core Principles\n\n1. **Be Specific and Clear** - Use precise descriptions, avoid vague terms\n2. **Step-by-step Editing** - Break complex modifications into multiple simple steps\n3. **Explicit Preservation** - State what should remain unchanged\n4. **Verb Selection** - Use \"change\", \"replace\" rather than \"transform\"\n\n## Best Practice Templates\n\n**Object Modification:**\n`\"Change [object] to [new state], keep [content to preserve] unchanged\"`\n\n**Style Transfer:**\n`\"Transform to [specific style], while maintaining [composition/character/other] unchanged\"`\n\n**Background Replacement:**\n`\"Change the background to [new background], keep the subject in the exact same position and pose\"`\n\n**Text Editing:**\n`\"Replace '[original text]' with '[new text]', maintain the same font style\"`\n\n> **Remember:** The more specific, the better. Kontext excels at understanding detailed instructions and maintaining consistency. "]},{"id":8,"pos":[1675.590576171875,664.664306640625],"mode":0,"size":[140,46],"type":"VAEDecode","color":"#223","flags":{},"order":50,"inputs":[{"link":160,"name":"samples","type":"LATENT","label":"samples"},{"link":131,"name":"vae","type":"VAE","label":"vae"}],"bgcolor":"#335","outputs":[{"name":"IMAGE","type":"IMAGE","label":"IMAGE","links":[23]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"VAEDecode"},"widgets_values":[]},{"id":85,"pos":[1290.333984375,260.6885681152344],"mode":4,"size":[187.75448608398438,26],"type":"FluxKontextImageScale","color":"#223","flags":{"collapsed":true},"order":24,"inputs":[{"link":96,"name":"image","type":"IMAGE","label":"image"}],"bgcolor":"#335","outputs":[{"name":"IMAGE","type":"IMAGE","label":"IMAGE","links":[73]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"FluxKontextImageScale"},"widgets_values":[]},{"id":158,"pos":[1576.70166015625,-736.9996337890625],"mode":4,"size":[75,26],"type":"Reroute","flags":{},"order":19,"inputs":[{"link":222,"name":"","type":"*"}],"outputs":[{"name":"","type":"IMAGE","links":null}],"properties":{"horizontal":false,"showOutputText":false}},{"id":155,"pos":[1379.557373046875,-96.87175750732422],"mode":4,"size":[270,286],"type":"ImageResizeKJv2","color":"#223","flags":{"collapsed":true},"order":29,"inputs":[{"link":217,"name":"image","type":"IMAGE"},{"link":null,"name":"mask","type":"MASK","shape":7},{"link":213,"name":"width","type":"INT","widget":{"name":"width"}},{"link":214,"name":"height","type":"INT","widget":{"name":"height"}}],"bgcolor":"#335","outputs":[{"name":"IMAGE","type":"IMAGE","links":[215]},{"name":"width","type":"INT","links":null},{"name":"height","type":"INT","links":null},{"name":"mask","type":"MASK","links":null}],"properties":{"ver":"f9118e0bbb39347ea7becb84ec03e397dfb4f644","cnr_id":"comfyui-kjnodes","Node 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[output]",false,"refresh","image"]},{"id":156,"pos":[1335.171142578125,-1232.8568115234375],"mode":4,"size":[140,246],"type":"PreviewImage","flags":{},"order":32,"inputs":[{"link":215,"name":"images","type":"IMAGE"}],"outputs":[],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":39,"pos":[953.8076171875,266.6226806640625],"mode":0,"size":[140,46],"type":"VAEEncode","color":"#223","flags":{},"order":38,"inputs":[{"link":69,"name":"pixels","type":"IMAGE","label":"pixels"},{"link":132,"name":"vae","type":"VAE","label":"vae"}],"bgcolor":"#335","outputs":[{"name":"LATENT","type":"LATENT","label":"LATENT","links":[38]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"VAEEncode"},"widgets_values":[]},{"id":42,"pos":[911.648681640625,404.8125915527344],"mode":0,"size":[211.60000610351562,46],"type":"ReferenceLatent","color":"#223","flags":{},"order":45,"inputs":[{"link":140,"name":"conditioning","type":"CONDITIONING","label":"conditioning"},{"link":38,"name":"latent","type":"LATENT","label":"latent","shape":7}],"bgcolor":"#335","outputs":[{"name":"CONDITIONING","type":"CONDITIONING","label":"CONDITIONING","links":[80]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"ReferenceLatent"},"widgets_values":[]},{"id":127,"pos":[913.0510864257812,866.6776733398438],"mode":0,"size":[240,26],"type":"ConditioningZeroOut","color":"#223","flags":{"collapsed":false},"order":46,"inputs":[{"link":162,"name":"conditioning","type":"CONDITIONING"}],"bgcolor":"#335","outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[158]}],"properties":{"ver":"0.3.39","cnr_id":"comfy-core","Node name for S&R":"ConditioningZeroOut"},"widgets_values":[]},{"id":27,"pos":[476.5362548828125,-195.73446655273438],"mode":0,"size":[270,106],"type":"EmptySD3LatentImage","color":"#223","flags":{},"order":27,"inputs":[{"link":119,"name":"width","type":"INT","widget":{"name":"width"}},{"link":122,"name":"height","type":"INT","widget":{"name":"height"}},{"link":281,"name":"batch_size","type":"INT","widget":{"name":"batch_size"}}],"bgcolor":"#335","outputs":[{"name":"LATENT","type":"LATENT","label":"LATENT","links":[164]}],"properties":{"ver":"0.3.43","cnr_id":"comfy-core","Node name for S&R":"EmptySD3LatentImage"},"widgets_values":[1024,1536,1]},{"id":132,"pos":[-1623.6346435546875,-257.97784423828125],"mode":0,"size":[419.16943359375,275.8766784667969],"type":"Note","color":"#223","flags":{},"order":7,"title":"Guide","inputs":[],"bgcolor":"#335","outputs":[],"properties":{},"widgets_values":["\n1-. SELLECT SIZE\n\n2-.CONSTRUCT YOUR PROMPT \n\n3-.SELECT THE REFERENCES BY ENABLE/DISSABLE CONCEPTS IMAGES\n\n4-.PRESS RUN\n\nYOU CAN USE THIS WORKFLOW WITH UP tO THREE iMAGES DISSABLE THE gROUPS CONCEPTS YOU DON'T NEED \n\nConcept1-. FIRST IMAGE IS THE MAIN SUBJECT YOU WANT TO EDIT OR WORK FROM\n\nConcept2-. SECOND IS FOR A PROP OR COSTUME OR A STYLE YOU WANT TO REPLICATE\n\ndISSABLE CONCEPT2 GROUP TO USE ONLY ONE REFERENCE\nDISSABLE Background removal if you want to keep the bg information"]},{"id":159,"pos":[-1737.3812255859375,-956.2491455078125],"mode":0,"size":[603.4553833007812,591.9759521484375],"type":"MarkdownNote","color":"#432","flags":{},"order":8,"inputs":[],"bgcolor":"#653","outputs":[],"properties":{},"widgets_values":["Here's the `.md` documentation with image steps:\n\n---\n\n# 🧩 Image Integration Workflow – Kontext I2I in ComfyUI\n\nThis guide shows a 3-step process to integrate objects (staff & book) into a main character image using ComfyUI and Kontext-style workflows.\n\n---\n\n## **Step 1: Setup Concept Nodes**\n\n![Step 1](chrome_8bMhkYwbeg.png)\n\n* Disable `Concept2` to isolate editing.\n* Load the **object assets** (staff + book) under `Concept1`.\n* Tweak them for realism and clarity before integration.\n\n---\n\n## **Step 2: Load and Prepare Images**\n\n![Step 2](chrome_tNC6frjc3B.png)\n\n* Load the **main character image** in `Concept1`.\n* Use resize/RMBG/other preprocessing nodes.\n* Load the refined staff + book version (from Step 1).\n\n---\n\n## **Step 3: Generate Final Integration**\n\n![Step 3](chrome_cCtK1i1bOs.png)\n\n* Enable `Concept2` to perform the full integration.\n* Output: character holding the enhanced assets with clean compositing.\n\n---\n\nYou can modify:\n\n* Pose or angle\n* Props and outfit\n* Style adaptation between concepts\n\nUse this process iteratively by switching one `Concept` at a time to refine before integration.\n\n---\n\nhttps://i.imgur.com/kAvnX2p.png\n"]},{"id":131,"pos":[-137.0125732421875,-447.21185302734375],"mode":0,"size":[334.0944519042969,132.49005126953125],"type":"MarkdownNote","color":"#232","flags":{},"order":9,"title":"SIZES","inputs":[],"bgcolor":"#353","outputs":[],"properties":{},"widgets_values":["\"IMG\": \n\n\"Horizontal\": \n\n\"HQ\": (1600, 900), \n\"MQ\": (1280, 720), \n\"LQ\": (1024, 576)},\n            \n\"Vertical\":   \n\"HQ\": (900, 1600), \n\"MQ\": (720, 1280), \n\"LQ\": (576, 1024)\n            \n\"Squarish\":   \n\"HQ\": (1600, 1600), \n\"MQ\": (1024, 1024), \n\"LQ\": (512, 512)\n            \n\"Cinematic\":  \n\"HQ\": (1600, 688), \n\"MQ\": (1280, 550), \n\"LQ\": (1024, 440), \n        \n"]},{"id":123,"pos":[2887.83984375,91.32740783691406],"mode":0,"size":[491.4166564941406,568.6932373046875],"type":"PreviewImage","color":"#223","flags":{},"order":41,"inputs":[{"link":115,"name":"images","type":"IMAGE"}],"bgcolor":"#335","outputs":[],"properties":{"ver":"0.3.40","cnr_id":"comfy-core","Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":70,"pos":[-80.26995086669922,208.52471923828125],"mode":0,"size":[400,200],"type":"ComfyUIDeployExternalNumberInt","color":"#232","flags":{"collapsed":true},"order":10,"inputs":[],"bgcolor":"#353","outputs":[{"name":"value","type":"INT","links":[161]}],"properties":{"ver":"089bad55605392860e59476a4c2765bfc772750b","cnr_id":"comfyui-deploy","Node name for S&R":"ComfyUIDeployExternalNumberInt"},"widgets_values":["noise_seed",2147483225,"",""]},{"id":136,"pos":[-1051.6329345703125,-33.617271423339844],"mode":0,"size":[429.72283935546875,293.3409423828125],"type":"PromptManager","color":"#223","flags":{},"order":40,"inputs":[{"link":263,"name":"context","type":"*","shape":7},{"link":154,"name":"image","type":"IMAGE","shape":7},{"link":null,"name":"mask","type":"MASK","shape":7},{"link":null,"name":"video","type":"IMAGE","shape":7},{"link":230,"name":"text_prompt","type":"STRING","shape":7,"widget":{"name":"text_prompt"}}],"bgcolor":"#335","outputs":[{"name":"context","type":"*","links":[252]}],"properties":{"ver":"3d82dbfe091d84868212e5f275deb5447fa4e659","cnr_id":"llm-toolkit","Node name for S&R":"PromptManager","widget_ue_connectable":{}},"widgets_values":["📏 Ultra-Compact System Prompt – Kontext Stitched-Image Editing\nRole: Produce one concise instruction for Kontext given:\n\nStitched input (largest tile = main image to edit; smaller tiles = references).\n\nTask type (one of: quick_edit · style_transfer · style_transform · text_edit · visual_cue).\n\nRules (87 tokens total)\n\n• Identify main subject in largest tile; keep pose, scale, identity.\n• Use smaller tiles only as style/obj/background cues.\n• For quick_edit → name exact change + preserve rest.\n• For style_transfer → “apply style of ref tile to ⟨area⟩, keep composition”.\n• For style_transform → describe new style traits, list what stays unchanged.\n• For text_edit → Replace 'old' with 'new', keep font/placement unless told.\n• For visual_cue → mention target region (“in the box”, “top-left corner”).\n• One or two sentences max,  ≤450 tokens, explicit nouns, no pronouns.\nOutput template:\n<instruction sentence(s)>\n","","",""]},{"id":111,"pos":[1612.011474609375,-397.69427490234375],"mode":0,"size":[270,150],"type":"ImageStitch","color":"#223","flags":{},"order":37,"inputs":[{"link":276,"name":"image1","type":"IMAGE"},{"link":137,"name":"image2","type":"IMAGE","shape":7}],"bgcolor":"#335","outputs":[{"name":"IMAGE","type":"IMAGE","links":[102,154]}],"properties":{"ver":"0.3.40","cnr_id":"comfy-core","Node name for 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subject to a underwater temple, teleport the subject to a 1950s diner, teleport the subject to a NewYork Apartment\n\n\nMove Camera\n\naerial top-down, low-angle dolly, 360 spin → camera now looks straight down from above\n\n\nRelight\n\ngolden hour, neon noir, softbox key → bathes scene in warm golden-hour light\n\n\nProduct\n\nminimalist flat-lay, lifestyle kitchen shot, high-key studio → turns item into a clean flat-lay packshot\n\n\nZoom\n\nextreme close-up on eyes, medium crop on hands → zooms tightly on the subject’s eyes\n\n\nColorize\n\nvintage Kodachrome, cyber teal-orange, monochrome red → applies vintage Kodachrome color grading\n\n\nMovie Poster\n\nsci-fi epic, indie rom-com → creates a sci-fi movie poster with the subject\n\n\nCartoonify\n\n90s anime, Pixar 3D, classic Disney → renders scene as 90s-style anime\n\n\nRemove Text\n\n(none needed) → strips every bit of text from the image\n\n\nHaircut\n\nbob cut, mohawk, long wavy, buzz cut → gives subject a bob cut (then 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Set CUDA architecture for L40s\nENV TORCH_CUDA_ARCH_LIST=8.9\n\n# Set up CUDA environment variables\nENV CUDA_HOME=/usr/local/cuda\nENV PATH=/usr/local/cuda/bin:${PATH}\nENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH:-}\nENV CUDA_LAUNCH_BLOCKING=1\nENV TORCH_USE_CUDA_DSA=1\n","type":"commands"},{"id":"2616be6e-f","data":"# Fix temp directory permissions for Modal\nRUN mkdir -p /tmp && chmod 1777 /tmp","type":"commands"},{"id":"fe097e03-d","data":"# Install system dependencies. Keeping this simple as the environment is Debian\n# and we are no longer compiling flash-attn from source.\nRUN apt-get update && \\\n    apt-get install -y --no-install-recommends \\\n    python3 \\\n    python3-pip \\\n    git \\\n    git-lfs \\\n    curl \\\n    aria2 \\\n    ffmpeg \\\n    poppler-utils \\\n    && rm -rf /var/lib/apt/lists/*","type":"commands"},{"id":"93215c56-4","data":"# Python environment setup\nRUN python3 -m pip install --upgrade pip setuptools wheel","type":"commands"},{"id":"ed33c547-8","data":"# Should be Already correct as per default image\n#RUN pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128","type":"commands"},{"id":"ecc8d520-7","data":"# Install ML dependencies\n# make sure Triton (the JIT‐based kernel compiler) is installed\nRUN pip install --no-cache-dir triton>=3.3.0\n\n# install SageAttention2 from PyPI (will pull a linux_x86_64 wheel—no nvcc needed)\nRUN pip install --no-cache-dir sageattention\n\nRUN pip3 install transformers ninja","type":"commands"},{"id":"c468aa20-6","data":"# FINAL FIX: Install the pre-compiled wheel directly. This avoids\n# the need for the nvcc compiler which is missing in the build environment.\nRUN pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu12torch2.6cxx11abiTRUE-cp311-cp311-linux_x86_64.whl","type":"commands"},{"id":"7a144547-0","data":"# NUNCHAKU\nRUN pip install https://github.com/mit-han-lab/nunchaku/releases/download/v0.3.2dev20250707/nunchaku-0.3.2.dev20250707+torch2.7-cp311-cp311-linux_x86_64.whl","type":"commands"},{"id":"6b52c0ac-0","data":{"url":"https://github.com/kijai/ComfyUI-KJNodes","hash":"5c19bd835a73fe49db91a02e0d61605269e3f0cf","meta":{"message":"typo","committer":{"date":"2025-07-06T20:57:29.000Z","name":"kijai","email":"40791699+kijai@users.noreply.github.com"},"commit_url":"https://github.com/kijai/ComfyUI-KJNodes/commit/5c19bd835a73fe49db91a02e0d61605269e3f0cf","latest_hash":"5c19bd835a73fe49db91a02e0d61605269e3f0cf","stargazers_count":1512},"name":"KJNodes for ComfyUI","files":[],"install_type":"git-clone"},"type":"custom-node"},{"id":"02184577-1","data":{"url":"https://github.com/1038lab/ComfyUI-RMBG","hash":"9fb0be165d317ff70d40c051199276cb06ffa07d","meta":{"message":"Update pyproject.toml","committer":{"date":"2025-07-02T06:23:54.000Z","name":"GitHub","email":"noreply@github.com"},"commit_url":"https://github.com/1038lab/ComfyUI-RMBG/commit/9fb0be165d317ff70d40c051199276cb06ffa07d","latest_hash":"9fb0be165d317ff70d40c051199276cb06ffa07d","stargazers_count":1124},"name":"ComfyUI-RMBG","files":[],"install_type":"git-clone"},"type":"custom-node"},{"id":"54bee516-7","data":{"url":"https://github.com/niknah/quick-connections","hash":"508f6575fc2d45c9f648d5dabb5b95c3e57b45ef","meta":{"message":"A possible fix for: https://github.com/niknah/quick-connections/issues/22\nThe default processUp might be crashi9ng.","committer":{"date":"2025-07-05T09:14:15.000Z","name":"niknah","email":"github@mail.niknah.com"},"commit_url":"https://github.com/niknah/quick-connections/commit/508f6575fc2d45c9f648d5dabb5b95c3e57b45ef","latest_hash":"508f6575fc2d45c9f648d5dabb5b95c3e57b45ef","stargazers_count":303},"name":"quick-connections","files":["https://github.com/niknah/quick-connections"],"install_type":"git-clone"},"type":"custom-node"},{"id":"comfyui-deploy","data":{"url":"https://github.com/BennyKok/comfyui-deploy","hash":"2a5223f2f0e1e68095f5fbe177be9d4f1116961b","meta":{"message":"fix","committer":{"date":"2025-06-29T07:43:12.000Z","name":"KarrixLee","email":"thomasdice1834793903@gmail.com"},"commit_url":"https://github.com/BennyKok/comfyui-deploy/commit/2a5223f2f0e1e68095f5fbe177be9d4f1116961b","latest_hash":"2a5223f2f0e1e68095f5fbe177be9d4f1116961b","stargazers_count":1367},"name":"ComfyUI Deploy","files":["https://github.com/BennyKok/comfyui-deploy"],"install_type":"git-clone"},"type":"custom-node"},{"id":"b9d11f3f-d","data":{"url":"https://github.com/rgthree/rgthree-comfy","hash":"f754c4765849aa748abb35a1f030a5ed6474a69b","meta":{"message":"Power Puter: Add for loop, and augassign capabilities.","committer":{"date":"2025-06-23T04:15:18.000Z","name":"rgthree","email":"regis.gaughan@gmail.com"},"commit_url":"https://github.com/rgthree/rgthree-comfy/commit/f754c4765849aa748abb35a1f030a5ed6474a69b","latest_hash":"f754c4765849aa748abb35a1f030a5ed6474a69b","stargazers_count":2027},"name":"rgthree's ComfyUI Nodes","files":[],"install_type":"git-clone"},"type":"custom-node"},{"id":"1d0820b5-3","data":"WORKDIR /comfyui/custom_nodes\nRUN git clone https://github.com/mit-han-lab/ComfyUI-nunchaku nunchaku_nodes\nWORKDIR /comfyui","type":"commands"},{"id":"97f9b2a9-c","data":{"url":"https://github.com/mit-han-lab/ComfyUI-nunchaku","hash":"aee38dd7ee8f88bf5db0a7ce813da67a4f969c88","meta":{"message":"chore: release v0.3.3","committer":{"date":"2025-06-29T14:43:47.000Z","name":"GitHub","email":"noreply@github.com"},"latest_hash":"aee38dd7ee8f88bf5db0a7ce813da67a4f969c88"},"name":"ComfyUI-nunchaku","files":["https://github.com/mit-han-lab/ComfyUI-nunchaku"],"install_type":"git-clone"},"type":"custom-node"},{"id":"2e3a49f0-0","data":{"url":"https://github.com/comfy-deploy/comfyui-llm-toolkit","hash":"33475c8a6a2120bfca809cb2d08ffd768dc49882","meta":{"message":"Add files via upload","committer":{"date":"2025-07-14T21:08:11.000Z","name":"GitHub","email":"noreply@github.com"},"commit_url":"https://github.com/comfy-deploy/comfyui-llm-toolkit/commit/33475c8a6a2120bfca809cb2d08ffd768dc49882","latest_hash":"33475c8a6a2120bfca809cb2d08ffd768dc49882","stargazers_count":25},"name":"comfyui-llm-toolkit","files":["https://github.com/comfy-deploy/comfyui-llm-toolkit"],"install_type":"git-clone"},"type":"custom-node"},{"id":"3efaea58-c","data":"RUN pip install https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_main/bitsandbytes-1.33.7.preview-py3-none-manylinux_2_24_x86_64.whl","type":"commands"}]},"extra_docker_commands":[],"install_custom_node_with_gpu":false},"last_link_id":288,"last_node_id":190,"workflow_api":{"6":{"_meta":{"title":"Positive Prompt"},"inputs":{"clip":["128:1",0],"text":["170",1]},"class_type":"CLIPTextEncode"},"8":{"_meta":{"title":"VAE Decode"},"inputs":{"vae":["128:0",0],"samples":["138",0]},"class_type":"VAEDecode"},"9":{"_meta":{"title":"Save Image"},"inputs":{"images":["8",0],"filename_prefix":"comfydeploy_FAST_kontext_raw_"},"class_type":"SaveImage"},"26":{"_meta":{"title":"FluxGuidance"},"inputs":{"guidance":2.5,"conditioning":["42",0]},"class_type":"FluxGuidance"},"27":{"_meta":{"title":"EmptySD3LatentImage"},"inputs":{"width":["124",0],"height":["124",1],"batch_size":["186",0]},"class_type":"EmptySD3LatentImage"},"30":{"_meta":{"title":"ModelSamplingFlux"},"inputs":{"model":["128:3",0],"width":["124",0],"height":["124",1],"max_shift":1.3500000000000003,"base_shift":0.45000000000000007},"class_type":"ModelSamplingFlux"},"39":{"_meta":{"title":"VAE Encode"},"inputs":{"vae":["128:0",0],"pixels":["40",0]},"class_type":"VAEEncode"},"40":{"_meta":{"title":"FluxKontextImageScale"},"inputs":{"image":["181",0]},"class_type":"FluxKontextImageScale"},"42":{"_meta":{"title":"ReferenceLatent"},"inputs":{"latent":["39",0],"conditioning":["6",0]},"class_type":"ReferenceLatent"},"67":{"_meta":{"title":"Load Image"},"inputs":{"image":"ED209 (RoboCop) (1).png"},"class_type":"LoadImage"},"70":{"_meta":{"title":"External Number Int (ComfyUI Deploy)"},"inputs":{"input_id":"noise_seed","description":"","display_name":"","default_value":2147483225},"class_type":"ComfyUIDeployExternalNumberInt"},"96":{"_meta":{"title":"w"},"inputs":{"source":["124",0],"preview":""},"class_type":"PreviewAny"},"97":{"_meta":{"title":"h"},"inputs":{"source":["124",1],"preview":""},"class_type":"PreviewAny"},"109":{"_meta":{"title":"FluxKontextImageScale"},"inputs":{"image":["111",0]},"class_type":"FluxKontextImageScale"},"111":{"_meta":{"title":"Image Stitch"},"inputs":{"image1":["181",0],"direction":"right","spacing_color":"white","spacing_width":0,"match_image_size":true},"class_type":"ImageStitch"},"123":{"_meta":{"title":"Preview Image"},"inputs":{"images":["109",0]},"class_type":"PreviewImage"},"124":{"_meta":{"title":"Resolution Selector (LLMToolkit)"},"inputs":{"mode":"IMG","quality":"MQ","aspect_ratio":["185",0]},"class_type":"ResolutionSelector"},"127":{"_meta":{"title":"ConditioningZeroOut"},"inputs":{"conditioning":["6",0]},"class_type":"ConditioningZeroOut"},"134":{"_meta":{"title":"Generate Text Stream (LLMToolkit)"},"inputs":{"prompt":"Write a detailed description of a futuristic city.","context":["136",0],"llm_model":"gpt-4.1-mini","hide_thinking":true,"LLMToolkitTextGeneratorStream_response":"(1-1) Reframe the scene with an aerial top-down view, looking directly down on the robot to reveal the top surface details and overall shape from above.  \n(2-2) Capture a close-up shot from the aerial top-down angle focusing on the intricate components and wiring on the robot's head and shoulders.  \n(3-3) Create a wide aerial top-down perspective that includes the robot's feet and the area immediately surrounding it to emphasize its stance and scale.  \n(4-4) Show the robot from an aerial top-down angle but tilted slightly to reveal both the top and front simultaneously, highlighting the mechanical complexity."},"class_type":"LLMToolkitTextGeneratorStream"},"136":{"_meta":{"title":"Prompt Manager (LLMToolkit)"},"inputs":{"url":"","image":["111",0],"context":["164",0],"file_path":"","audio_path":"","text_prompt":["137",0]},"class_type":"PromptManager"},"137":{"_meta":{"title":"PROMPT"},"inputs":{"input_id":"MANUAL_PROMPT","description":"- The removed tasks need this field empty.\n\n- When chose variations supply a comma-separated word descriptors 1 to 4\n\nEXAMPLES:\nBelow is a quick-start “cheat sheet” for every task.\n\nEach line shows a sample user input (comma-separated keywords) followed by the effect you’ll get.\n\n\nTeleport\n\nteleport the subject to a cyberpunk alley, teleport the subject to a underwater temple, teleport the subject to a 1950s diner, teleport the subject to a NewYork Apartment\n\n\nMove Camera\n\naerial top-down, low-angle dolly, 360 spin → camera now looks straight down from above\n\n\nRelight\n\ngolden hour, neon noir, softbox key → bathes scene in warm golden-hour light\n\n\nProduct\n\nminimalist flat-lay, lifestyle kitchen shot, high-key studio → turns item into a clean flat-lay packshot\n\n\nZoom\n\nextreme close-up on eyes, medium crop on hands → zooms tightly on the subject’s eyes\n\n\nColorize\n\nvintage Kodachrome, cyber teal-orange, monochrome red → applies vintage Kodachrome color grading\n\n\nMovie Poster\n\nsci-fi epic, indie rom-com → creates a sci-fi movie poster with the subject\n\n\nCartoonify\n\n90s anime, Pixar 3D, classic Disney → renders scene as 90s-style anime\n\n\nRemove Text\n\n(none needed) → strips every bit of text from the image\n\n\nHaircut\n\nbob cut, mohawk, long wavy, buzz cut → gives subject a bob cut (then mohawk, etc.)\n\n\nBodybuilder\n\nclassic Arnold, shredded aesthetic, powerlifter bulk, comic-book hero → inflates muscles to classic Arnold size\n\n\nRemove Furniture\n\n(none needed) → deletes all furniture and appliances\n\n\nInterior Design\n\nScandinavian minimal, boho chic, industrial loft, mid-century modern → redesigns room in Scandinavian minimal style\n\n\nSeason Shift\n\nautumn, winter → converts scene to autumn colors & fallen leaves\n\n\nAge Progression\n\nteen, middle-aged, elderly, child → ages subject to teen, then middle-aged, etc.\n\n\nAdd Pet – user-driven 4 variations\n\ndog, cat, bird, rabbit → adds a dog, then a cat, then a bird, then a rabbit\n\n\nHistorical Era\n\nVictorian London, Ancient Egypt, 1980s Miami → places scene in foggy Victorian London\n\n\nDramatic Weather\n\nsandstorm, torrential rain, aurora night → whips up a raging sandstorm\n\n\nMirror World\n\nhorizontal flip, vertical flip, kaleidoscope radial → mirrors the image left-to-right\n\n\nVirtual Makeup\n\nsmoky glam, K-beauty dewy, 80s neon, gothic matte → applies smoky glam makeup\n\n\nPattern Overlay\n\nherringbone, leopard print, holographic grid → overlays a subtle herringbone texture on surfaces\n\n\nChange Outfit\n\nsteampunk suit, summer linen, cyber ninja, 70s disco → dresses subject in steampunk suit\n\n\nDouble Exposure\n\nmisty forest, galaxy swirl, city skyline → blends subject with a misty forest silhouette","display_name":"MANUAL_PROMPT ","default_value":"aerial top-down, low-angle dolly, 360 spin"},"class_type":"ComfyUIDeployExternalText"},"138":{"_meta":{"title":"KSampler"},"inputs":{"cfg":1,"seed":["70",0],"model":["30",0],"steps":8,"denoise":1,"negative":["127",0],"positive":["26",0],"scheduler":"simple","latent_image":["27",0],"sampler_name":"euler"},"class_type":"KSampler"},"153":{"_meta":{"title":"Resize Image v2"},"inputs":{"image":["188",0],"width":["124",0],"device":"cpu","height":["124",1],"pad_color":"0, 0, 0","divisible_by":2,"crop_position":"center","upscale_method":"nearest-exact","keep_proportion":"resize"},"class_type":"ImageResizeKJv2"},"154":{"_meta":{"title":"Preview Image"},"inputs":{"images":["153",0]},"class_type":"PreviewImage"},"164":{"_meta":{"title":"Kontext System Prompt text (LLMToolkit)"},"inputs":{"task":["190",0]},"class_type":"SystemPromptTaskGenerator"},"170":{"_meta":{"title":"Display Text (LLMToolkit)"},"inputs":{"select":"2","context":["134",0],"hide_thinking":true,"Display_Text_0":"(1-1) Reframe the scene with an aerial top-down view, looking directly down on the robot to reveal the top surface details and overall shape from above.  \n(2-2) Capture a close-up shot from the aerial top-down angle focusing on the intricate components and wiring on the robot's head and shoulders.  \n(3-3) Create a wide aerial top-down perspective that includes the robot's feet and the area immediately surrounding it to emphasize its stance and scale.  \n(4-4) Show the robot from an aerial top-down angle but tilted slightly to reveal both the top and front simultaneously, highlighting the mechanical complexity."},"class_type":"Display_Text"},"180":{"_meta":{"title":"BiRefNet Remove Background (RMBG)"},"inputs":{"image":["153",0],"model":"BiRefNet-general","mask_blur":0,"background":"Alpha","mask_offset":0,"invert_output":false,"refine_foreground":false},"class_type":"BiRefNetRMBG"},"181":{"_meta":{"title":"Switch Any _wANY (LLMToolkit)"},"inputs":{"input_a":["180",0],"input_b":["153",0],"selector":["183",0]},"class_type":"SwitchAny_wANY"},"183":{"_meta":{"title":"External Boolean (ComfyUI Deploy)"},"inputs":{"input_id":"Remove_Backround","description":"Removes the background (good for product and other presets)\n","display_name":"","default_value":true},"class_type":"ComfyUIDeployExternalBoolean"},"185":{"_meta":{"title":"External Enum"},"inputs":{"options":"[\"Horizontal\",\"Vertical\",\"Squarish\",\"Cinematic\"]","input_id":"aspect_ratio","description":"","display_name":"","default_value":"Squarish"},"class_type":"ComfyUIDeployExternalEnum"},"186":{"_meta":{"title":"External Number Int (ComfyUI Deploy)"},"inputs":{"input_id":"batch_size","description":"","display_name":"","default_value":1},"class_type":"ComfyUIDeployExternalNumberInt"},"188":{"_meta":{"title":"External Image (ComfyUI Deploy)"},"inputs":{"input_id":"input_image","description":"","display_name":"","default_value":["67",0],"default_value_url":""},"class_type":"ComfyUIDeployExternalImage"},"190":{"_meta":{"title":"External Enum"},"inputs":{"options":"[\"Teleport\",\"Move Camera\",\"Relight\",\"Product\",\"Zoom\",\"Colorize\",\"Movie Poster\",\"Cartoonify\",\"Remove Text\",\"Haircut\",\"Bodybuilder\",\"Remove Furniture\",\"Interior Design\",\"Season Shift\",\"Age Progression\",\"Add Pet 1 variations\",\"Historical Era\",\"Dramatic Weather\",\"Mirror World\",\"Virtual Makeup\",\"Pattern Overlay\",\"Change Outfit\",\"Double Exposure\",\"Turnaround Sheet Three-View Tile\",\"Move Camera 4 variations\",\"Relight 4 variations\",\"Product 4 variations\",\"Zoom 4 variations\",\"Colorize 4 variations\",\"Movie Poster 4 variations\",\"Cartoonify 4 variations\",\"Haircut 4 variations\",\"Bodybuilder 4 variations\",\"Interior Design 4 variations\",\"Season Shift 4 variations\",\"Age Progression 4 variations\",\"Add Pet 4 variations\",\"Historical Era 4 variations\",\"Dramatic Weather 4 variations\",\"Mirror World 4 variations\",\"Virtual Makeup 4 variations\",\"Pattern Overlay 4 variations\",\"Change Outfit 4 variations\",\"Double Exposure 4 variations\",\"Turnaround Sheet 4 Views\"]","input_id":"task","description":"","display_name":"","default_value":"Product 4 variations"},"class_type":"ComfyUIDeployExternalEnum"},"128:0":{"_meta":{"title":"Load VAE"},"inputs":{"vae_name":"ae.sft"},"class_type":"VAELoader"},"128:1":{"_meta":{"title":"DualCLIPLoader"},"inputs":{"type":"flux","device":"default","clip_name1":"clip_l.safetensors","clip_name2":"t5xxl_fp16.safetensors"},"class_type":"DualCLIPLoader"},"128:2":{"_meta":{"title":"Nunchaku FLUX DiT Loader"},"inputs":{"i2f_mode":"always","attention":"nunchaku-fp16","data_type":"float16","device_id":0,"model_path":"nunchaku-flux.1-kontext-dev/svdq-int4_r32-flux.1-kontext-dev.safetensors","cpu_offload":"auto","cache_threshold":0.008000000000000002},"class_type":"NunchakuFluxDiTLoader"},"128:3":{"_meta":{"title":"Nunchaku FLUX.1 LoRA Loader"},"inputs":{"model":["128:2",0],"lora_name":"FLUX.1-turbo-Alpha.safetensors","lora_strength":0.9000000000000001},"class_type":"NunchakuFluxLoraLoader"}}}