62 lines
21 KiB
JSON
62 lines
21 KiB
JSON
[
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{
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"name": "添加LoRA",
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"description": "在现有工作流中添加LoRA节点",
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"content": "在现有工作流中添加LoRA节点，确保与现有模型和提示词节点正确连接\n 在checkpoint节点后添加LoRA节点。\n {\n \"1\": {\n \"inputs\": {\n \"lora_name\": \"DOG.safetensors\",\n \"strength_model\": 1,\n \"strength_clip\": 1,\n \"model\": [\n \"2\",\n 0\n ],\n \"clip\": [\n \"2\",\n 1\n ]\n },\n \"class_type\": \"LoraLoader\",\n \"_meta\": {\n \"title\": \"Load LoRA\"\n }\n }"
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},
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{
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"name": "后处理增强",
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"description": "后处理增强，例如在Preview Image或Save Image节点后添加高清放大功能（如Real-ESRGAN、ESRGAN等）; 或者添加图像缩放节点",
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"content": "- 在Preview Image或Save Image节点后添加高清放大功能（如Real-ESRGAN、ESRGAN等）\n - 添加图像缩放节点\n {\n \"1\": {\n \"inputs\": {\n \"width\": 512,\n \"height\": 512,\n \"interpolation\": \"nearest\",\n \"method\": \"stretch\",\n \"condition\": \"always\",\n \"multiple_of\": 0\n },\n \"class_type\": \"ImageResize+\",\n \"_meta\": {\n \"title\": \"🔧 Image Resize\"\n }\n }\n}\n - 添加图像尺寸调整节点\n {\n \"2\": {\n \"inputs\": {\n \"aspect_ratio\": \"original\",\n \"proportional_width\": 2,\n \"proportional_height\": 1,\n \"fit\": \"letterbox\",\n \"method\": \"lanczos\",\n \"round_to_multiple\": \"8\",\n \"scale_to_longest_side\": false,\n \"longest_side\": 1024\n },\n \"class_type\": \"LayerUtility: ImageScaleByAspectRatio\",\n \"_meta\": {\n \"title\": \"LayerUtility: ImageScaleByAspectRatio\"\n }\n }\n}\n -添加图像放大节点\n \"11\": {\n \"inputs\": {\n \"width\": 512,\n \"height\": 512,\n \"upscale_method\": \"nearest-exact\",\n \"keep_proportion\": false,\n \"divisible_by\": 2,\n \"crop\": \"disabled\",\n \"image\": [\n \"12\",\n 0\n ]\n },\n \"class_type\": \"ImageResizeKJ\",\n \"_meta\": {\n \"title\": \"Resize Image\"\n }\n },\n -添加VAEdecode节点\n \"12\": {\n \"inputs\": {},\n \"class_type\": \"VAEDecode\",\n \"_meta\": {\n \"title\": \"VAE Decode\"\n }\n }\n}"
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},
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{
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"name": "提示词优化",
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"description": "修改现有提示词节点的内容(提示词应该在(CLIP Text Encode Prompt或Text _O节点内编辑); 添加单独的提示词输入节点",
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"content": "{\n \"12\": {\n \"inputs\": {\n \"text\": [\n \"13\",\n 0\n ]\n },\n \"class_type\": \"CLIPTextEncode\",\n \"_meta\": {\n \"title\": \"CLIP Text Encode (Prompt)\"\n }\n },\n \"13\": {\n \"inputs\": {\n \"delimiter\": \", \",\n \"clean_whitespace\": \"true\",\n \"text_a\": [\n \"15\",\n 0\n ],\n \"text_b\": [\n \"16\",\n 0\n ]\n },\n \"class_type\": \"Text Concatenate\",\n \"_meta\": {\n \"title\": \"Text Concatenate\"\n }\n },\n \"15\": {\n \"inputs\": {\n \"text\": \"\"\n },\n \"class_type\": \"Text _O\",\n \"_meta\": {\n \"title\": \"Text _O\"\n }\n },\n \"16\": {\n \"inputs\": {\n \"text\": \"\"\n },\n \"class_type\": \"Text _O\",\n \"_meta\": {\n \"title\": \"Text _O\"\n }\n }\n}"
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},
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{
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"name": "图像反推",
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"description": "理解图片信息并用文字表达出来",
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"content": "添加图像反推节点（如CLIP Interrogator）\n{\n \"11\": {\n \"inputs\": {\n \"prompt_mode\": \"fast\",\n \"image_analysis\": \"off\"\n },\n \"class_type\": \"ClipInterrogator\",\n \"_meta\": {\n \"title\": \"Clip Interrogator ♾️Mixlab\"\n }\n }\n}\n - 添加更复杂或者更好用的图像反推节点(加载图片使用florence2进行反推)\n {\n \"6\": {\n \"inputs\": {\n \"image\": \"06.JPG\"\n },\n \"class_type\": \"LoadImage\",\n \"_meta\": {\n \"title\": \"$image.image!:The image to analyze, must be a url\"\n }\n },\n \"10\": {\n \"inputs\": {\n \"model\": \"microsoft/Florence-2-large\",\n \"precision\": \"fp16\",\n \"attention\": \"sdpa\"\n },\n \"class_type\": \"DownloadAndLoadFlorence2Model\",\n \"_meta\": {\n \"title\": \"DownloadAndLoadFlorence2Model\"\n }\n },\n \"11\": {\n \"inputs\": {\n \"text_input\": \"\",\n \"task\": \"more_detailed_caption\",\n \"fill_mask\": true,\n \"keep_model_loaded\": false,\n \"max_new_tokens\": 1024,\n \"num_beams\": 3,\n \"do_sample\": true,\n \"output_mask_select\": \"\",\n \"seed\": 1098631327477633,\n \"image\": [\n \"6\",\n 0\n ],\n \"florence2_model\": [\n \"10\",\n 0\n ]\n },\n \"class_type\": \"Florence2Run\",\n \"_meta\": {\n \"title\": \"Florence2Run\"\n }\n },\n \"18\": {\n \"inputs\": {\n \"anything\": [\n \"11\",\n 2\n ]\n },\n \"class_type\": \"easy showAnything\",\n \"_meta\": {\n \"title\": \"Show Any\"\n }\n },\n \"20\": {\n \"inputs\": {\n \"value\": \"Generate high-quality text descriptions from images using local Florence model.\\n \\n Main use cases:\\n 1. **Reverse image prompt generation**:\\n - When users upload an image and want to get prompts for AI art generation\\n - Analyzes visual elements, style, composition, etc. to generate prompts for Stable Diffusion, DALL-E, and other models\\n - In this case, return the tool's raw output directly to users without any modification or summary\\n - Users can directly use these prompts for image generation\\n 2. **Image content understanding**:\\n - When users ask about specific content, objects, scenes, people, etc. in the image\\n - Need to understand the semantic content of the image and answer users' specific questions\\n - In this case, combine tool output with conversation context to give users contextually appropriate natural responses\\n - Don't return raw output directly, but provide targeted replies based on understanding results\"\n },\n \"class_type\": \"PrimitiveStringMultiline\",\n \"_meta\": {\n \"title\": \"MCP\"\n }\n }\n}"
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},
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{
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"name": "sdxl特征保持",
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"description": "当用户在使用sdxl工作流并需提及特征保持时优先推荐该类工作流，旨在保持图像的特征提取、保持主体结构不变、保持主体形状、控制人物角色姿态、保持场景的深度和空间结构、基于线稿或者草图生成图像等场景下，可以用添加ControlNet节点来实现",
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"content": "**ControlNet集成**：(preprocessor可选canny、depth等参数,模型样式选择，Controlnet开始、结束权重)\n {\n \"22\": {\n \"inputs\": {\n \"strength\": 1,\n \"start_percent\": 0,\n \"end_percent\": 1,\n \"control_net\": [\n \"23\",\n 0\n ],\n \"image\": [\n \"24\",\n 0\n ]\n },\n \"class_type\": \"ControlNetApplyAdvanced\",\n \"_meta\": {\n \"title\": \"Apply ControlNet\"\n }\n },\n \"23\": {\n \"inputs\": {\n \"control_net_name\": \"ControlNet-Standard-Lineart-for-SDXL.safetensors\"\n },\n \"class_type\": \"ControlNetLoader\",\n \"_meta\": {\n \"title\": \"Load ControlNet Model\"\n }\n },\n \"24\": {\n \"inputs\": {\n \"preprocessor\": \"none\",\n \"resolution\": 512\n },\n \"class_type\": \"AIO_Preprocessor\",\n \"_meta\": {\n \"title\": \"AIO Aux Preprocessor\"\n }\n }\n}"
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},
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{
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"name": "flux风格特征迁移",
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"description": "当用户在使用flux的工作流并提及风格特征迁移时优先推荐该节点，该类节用途主要应用于flux工作流的图片风格特征迁移和提取，风格转换，两张图片参考迁移，重绘遮罩区域或重绘背景，使用时请先分析是否为flux工作流",
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"content": "{\n \"1\": {\n \"inputs\": {\n \"downsampling_factor\": 3,\n \"downsampling_function\": \"area\",\n \"mode\": \"center crop (square)\",\n \"weight\": 1,\n \"autocrop_margin\": 0.1,\n \"style_model\": [\n \"2\",\n 0\n ],\n \"clip_vision\": [\n \"3\",\n 0\n ]\n },\n \"class_type\": \"ReduxAdvanced\",\n \"_meta\": {\n \"title\": \"ReduxAdvanced\"\n }\n },\n \"2\": {\n \"inputs\": {\n \"style_model_name\": \"flux1-redux-dev.safetensors\"\n },\n \"class_type\": \"StyleModelLoader\",\n \"_meta\": {\n \"title\": \"Load Style Model\"\n }\n },\n \"3\": {\n \"inputs\": {\n \"clip_name\": \"sigclip_vision_patch14_384.safetensors\"\n },\n \"class_type\": \"CLIPVisionLoader\",\n \"_meta\": {\n \"title\": \"Load CLIP Vision\"\n }\n }\n}"
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},
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{
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"name": "sdxl特征迁移",
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"description": "当用户在使用sdxl工作流并提及特征迁移时优先推荐该类工作流，旨在保持图像的特征提取、颜色提取,保持主体不变重绘背景或改变遮罩区域内容，可以用添加IPAdapter Advanced节点来实现",
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"content": "**IPAdapter集成**：(weight type可选linear、ease in-out等参数,weight可选0.1-1开始、结束权重可选0.1-1) {\n \"1\": {\n \"inputs\": {\n \"weight\": 1,\n \"weight_type\": \"linear\",\n \"combine_embeds\": \"concat\",\n \"start_at\": 0,\n \"end_at\": 1,\n \"embeds_scaling\": \"V only\",\n \"ipadapter\": [\n \"2\",\n 0\n ],\n \"clip_vision\": [\n \"3\",\n 0\n ]\n },\n \"class_type\": \"IPAdapterAdvanced\",\n \"_meta\": {\n \"title\": \"IPAdapter Advanced\"\n }\n },\n \"2\": {\n \"inputs\": {\n \"ipadapter_file\": \"ip-adapter_sdxl_vit-h.safetensors\"\n },\n \"class_type\": \"IPAdapterModelLoader\",\n \"_meta\": {\n \"title\": \"IPAdapter Model Loader\"\n }\n },\n \"3\": {\n \"inputs\": {\n \"clip_name\": \"CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors\"\n },\n \"class_type\": \"CLIPVisionLoader\",\n \"_meta\": {\n \"title\": \"Load CLIP Vision\"\n }\n }\n}"
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},
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{
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"name": "扩图",
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"description": "给我一个扩图链路(包括图像加载和图像输出)",
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"content": "{\n \"1\": {\n \"inputs\": {\n \"image\": \"01.JPG\"\n },\n \"class_type\": \"LoadImage\",\n \"_meta\": {\n \"title\": \"$image.image!:The image to analyze, must be a url\"\n }\n },\n \"2\": {\n \"inputs\": {\n \"model\": \"runwayml/stable-diffusion-xl-base-1.0\",\n \"precision\": \"fp16\",\n \"attention\": \"sdpa\"\n },\n \"class_type\": \"DownloadAndLoadModel\",\n \"_meta\": {\n \"title\": \"DownloadAndLoadModel\"\n }\n },\n \"3\": {\n \"inputs\": {\n \"prompt\": \"A beautiful girl with long hair and a white dress\",\n \"image\": [\n \"1\",\n 0\n ],\n \"model\": [\n \"2\",\n 0\n ]\n },\n \"class_type\": \"StableDiffusionXLRun\",\n \"_meta\": {\n \"title\": \"StableDiffusionXLRun\"\n }\n },\n \"4\": {\n \"inputs\": {\n \"image\": [\n \"3\",\n 0\n ]\n },\n \"class_type\": \"easy showImage\",\n \"_meta\": {\n \"title\": \"Show Image\"\n }\n }\n}"
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},
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{
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"name": "智能抠图",
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"description": "添加背景移除节点（如SAM、U²-Net等）",
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"content": "- 添加背景移除或抠图节点\n {\n \"7\": {\n \"inputs\": {\n \"rem_mode\": \"RMBG-1.4\",\n \"image_output\": \"Preview\",\n \"save_prefix\": \"ComfyUI\",\n \"torchscript_jit\": false,\n \"add_background\": \"none\",\n \"refine_foreground\": false\n },\n \"class_type\": \"easy imageRemBg\",\n \"_meta\": {\n \"title\": \"Image Remove Bg\"\n }\n }\n}\n - 添加SAM抠图节点\n {\n \"8\": {\n \"inputs\": {\n \"prompt\": \"\",\n \"threshold\": 0.3,\n \"sam_model\": [\n \"9\",\n 0\n ],\n \"grounding_dino_model\": [\n \"10\",\n 0\n ]\n },\n \"class_type\": \"GroundingDinoSAMSegment (segment anything)\",\n \"_meta\": {\n \"title\": \"GroundingDinoSAMSegment (segment anything)\"\n }\n },\n \"9\": {\n \"inputs\": {\n \"model_name\": \"sam_vit_h (2.56GB)\"\n },\n \"class_type\": \"SAMModelLoader (segment anything)\",\n \"_meta\": {\n \"title\": \"SAMModelLoader (segment anything)\"\n }\n },\n \"10\": {\n \"inputs\": {\n \"model_name\": \"GroundingDINO_SwinT_OGC (694MB)\"\n },\n \"class_type\": \"GroundingDinoModelLoader (segment anything)\",\n \"_meta\": {\n \"title\": \"GroundingDinoModelLoader (segment anything)\"\n }\n }\n}"
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},
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{
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"name": "kontext图像编辑",
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"description": "添加kontext系列节点(这个系列是文生图和图生图场景应用的模型和节点,适合用针对图像的融合，二次编辑,包括但不限于去水印、擦除物体、多元素融合、图片元素提取等)",
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"content": "{\n \"6\": {\n \"inputs\": {\n \"text\": \"\",\n \"clip\": [\n \"38\",\n 0\n ]\n },\n \"class_type\": \"CLIPTextEncode\",\n \"_meta\": {\n \"title\": \"CLIP Text Encode (Prompt)\"\n }\n },\n \"8\": {\n \"inputs\": {\n \"samples\": [\n \"31\",\n 0\n ],\n \"vae\": [\n \"39\",\n 0\n ]\n },\n \"class_type\": \"VAEDecode\",\n \"_meta\": {\n \"title\": \"VAE Decode\"\n }\n },\n \"31\": {\n \"inputs\": {\n \"seed\": 584043043142251,\n \"steps\": 30,\n \"cfg\": 1.5,\n \"sampler_name\": \"euler\",\n \"scheduler\": \"simple\",\n \"denoise\": 1,\n \"model\": [\n \"37\",\n 0\n ],\n \"positive\": [\n \"35\",\n 0\n ],\n \"negative\": [\n \"135\",\n 0\n ],\n \"latent_image\": [\n \"124\",\n 0\n ]\n },\n \"class_type\": \"KSampler\",\n \"_meta\": {\n \"title\": \"KSampler\"\n }\n },\n \"35\": {\n \"inputs\": {\n \"guidance\": 2.5,\n \"conditioning\": [\n \"177\",\n 0\n ]\n },\n \"class_type\": \"FluxGuidance\",\n \"_meta\": {\n \"title\": \"FluxGuidance\"\n }\n },\n \"37\": {\n \"inputs\": {\n \"unet_name\": \"flux1-dev-kontext_fp8_scaled.safetensors\",\n \"weight_dtype\": \"fp8_e4m3fn\"\n },\n \"class_type\": \"UNETLoader\",\n \"_meta\": {\n \"title\": \"Load Diffusion Model\"\n }\n },\n \"38\": {\n \"inputs\": {\n \"clip_name1\": \"clip_l.safetensors\",\n \"clip_name2\": \"t5xxl_fp16.safetensors\",\n \"type\": \"flux\",\n \"device\": \"default\"\n },\n \"class_type\": \"DualCLIPLoader\",\n \"_meta\": {\n \"title\": \"DualCLIPLoader\"\n }\n },\n \"39\": {\n \"inputs\": {\n \"vae_name\": \"ae.safetensors\"\n },\n \"class_type\": \"VAELoader\",\n \"_meta\": {\n \"title\": \"Load VAE\"\n }\n },\n \"42\": {\n \"inputs\": {\n \"image\": [\n \"194\",\n 0\n ]\n },\n \"class_type\": \"FluxKontextImageScale\",\n \"_meta\": {\n \"title\": \"FluxKontextImageScale\"\n }\n },\n \"124\": {\n \"inputs\": {\n \"pixels\": [\n \"42\",\n 0\n ],\n \"vae\": [\n \"39\",\n 0\n ]\n },\n \"class_type\": \"VAEEncode\",\n \"_meta\": {\n \"title\": \"VAE Encode\"\n }\n },\n \"135\": {\n \"inputs\": {\n \"conditioning\": [\n \"6\",\n 0\n ]\n },\n \"class_type\": \"ConditioningZeroOut\",\n \"_meta\": {\n \"title\": \"ConditioningZeroOut\"\n }\n },\n \"177\": {\n \"inputs\": {\n \"conditioning\": [\n \"6\",\n 0\n ],\n \"latent\": [\n \"124\",\n 0\n ]\n },\n \"class_type\": \"ReferenceLatent\",\n \"_meta\": {\n \"title\": \"ReferenceLatent\"\n }\n },\n \"193\": {\n \"inputs\": {\n \"filename_prefix\": \"ComfyUI\",\n \"images\": [\n \"8\",\n 0\n ]\n },\n \"class_type\": \"SaveImage\",\n \"_meta\": {\n \"title\": \"Save Image\"\n }\n },\n \"194\": {\n \"inputs\": {\n \"image\": \"021175655C8A2A86C831C96855F3EF23.png\"\n },\n \"class_type\": \"LoadImage\",\n \"_meta\": {\n \"title\": \"Load Image\"\n }\n }\n}"
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},
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{
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"name": "qwen image图像生成",
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"description": "基于Qwen-Image多模态大模型的文生图解决方案，支持中英文提示词，擅长生成包含复杂文本的图像内容。适用于海报设计、广告创意等需要稳定生成文字内容的场景",
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"content": "添加qwen image模型,使用qwen image 文生图，创作一张海报，适合生成稳定的文字(当用户提到需要使用qwen image系列时给它提供如下的节点和模型选择：load Difussion Model节点对应qwen_image名字的模型，Load CLIP对应的clip_name:qwen_2.5_vl,type：qwen_image,Load VAE节点对应的模型为:qwen_image_vae.safetensors)"
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},
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{
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"name": "wan2.2图生视频",
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"description": "wan2.2是图像生成视频的模型，主要用来帮助用户根据图片加提示词来生成视频。",
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"content": "{\n \"11\": {\n \"inputs\": {\n \"model_name\": \"umt5-xxl-enc-bf16.safetensors\",\n \"precision\": \"bf16\",\n \"load_device\": \"offload_device\",\n \"quantization\": \"disabled\"\n },\n \"class_type\": \"LoadWanVideoT5TextEncoder\",\n \"_meta\": {\n \"title\": \"WanVideo T5 Text Encoder Loader\"\n }\n },\n \"16\": {\n \"inputs\": {\n \"positive_prompt\": \"猫咪的头部猛的向前一探\",\n \"negative_prompt\": \"色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走\",\n \"force_offload\": true,\n \"use_disk_cache\": false,\n \"device\": \"gpu\",\n \"t5\": [\n \"11\",\n 0\n ]\n },\n \"class_type\": \"WanVideoTextEncode\",\n \"_meta\": {\n \"title\": \"WanVideo TextEncode\"\n }\n },\n \"28\": {\n \"inputs\": {\n \"enable_vae_tiling\": false,\n \"tile_x\": 272,\n \"tile_y\": 272,\n \"tile_stride_x\": 144,\n \"tile_stride_y\": 128,\n \"normalization\": \"default\",\n \"vae\": [\n \"38\",\n 0\n ],\n \"samples\": [\n \"90\",\n 0\n ]\n },\n \"class_type\": \"WanVideoDecode\",\n \"_meta\": {\n \"title\": \"WanVideo Decode\"\n }\n },\n \"38\": {\n \"inputs\": {\n \"model_name\": \"Wan2_1_VAE_bf16.safetensors\",\n \"precision\": \"bf16\"\n },\n \"class_type\": \"WanVideoVAELoader\",\n \"_meta\": {\n \"title\": \"WanVideo VAE Loader\"\n }\n },\n \"39\": {\n \"inputs\": {\n \"blocks_to_swap\": 20,\n \"offload_img_emb\": false,\n \"offload_txt_emb\": false,\n \"use_non_blocking\": false,\n \"vace_blocks_to_swap\": 1\n },\n \"class_type\": \"WanVideoBlockSwap\",\n \"_meta\": {\n \"title\": \"WanVideo Block Swap\"\n }\n },\n \"56\": {\n \"inputs\": {\n \"lora\": \"Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensors\",\n \"strength\": 3,\n \"low_mem_load\": true,\n \"merge_loras\": false\n },\n \"class_type\": \"WanVideoLoraSelect\",\n \"_meta\": {\n \"title\": \"WanVideo Lora Select\"\n }\n },\n \"89\": {\n \"inputs\": {\n \"width\": 832,\n \"height\": 480,\n \"num_frames\": 81,\n \"noise_aug_strength\": 0,\n \"start_latent_strength\": 1,\n \"end_latent_strength\": 1,\n \"force_offload\": true,\n \"fun_or_fl2v_model\": false,\n \"tiled_vae\": false,\n \"vae\": [\n \"38\",\n 0\n ]\n },\n \"class_type\": \"WanVideoImageToVideoEncode\",\n \"_meta\": {\n \"title\": \"WanVideo ImageToVideo Encode\"\n }\n },\n \"90\": {\n \"inputs\": {\n \"steps\": 8,\n \"cfg\": 1,\n \"shift\": 8,\n \"seed\": 652251640766870,\n \"force_offload\": true,\n \"scheduler\": \"unipc\",\n \"riflex_freq_index\": 0,\n \"denoise_strength\": 1,\n \"batched_cfg\": false,\n \"rope_function\": \"comfy\",\n \"start_step\": 4,\n \"end_step\": -1,\n \"image_embeds\": [\n \"89\",\n 0\n ],\n \"text_embeds\": [\n \"16\",\n 0\n ]\n },\n \"class_type\": \"WanVideoSampler\",\n \"_meta\": {\n \"title\": \"WanVideo Sampler\"\n }\n },\n \"150\": {\n \"inputs\": {\n \"lora\": [\n \"56\",\n 0\n ]\n },\n \"class_type\": \"WanVideoSetLoRAs\",\n \"_meta\": {\n \"title\": \"WanVideo Set LoRAs\"\n }\n }\n}"
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}
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] |