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What exactly are the "node" and the "workflow" in AI image platform (explanation for the beginner)

What exactly are the "node" and the "workflow" in AI image platform (explanation for the beginner)

The Traditional Way of Generating AI Images for the BeginnerIf you are a beginner in the AI community, maybe you will be very confused and have no clue about what is "Node", and "Workflow" and their relations with "AI Tools" in the TensorArtTo start with the most simple way. We need to first mention how the user generates an image using a "Remixing" button that brings us to the "Normal Creation menu"Needless to say, by just editing the prompt (what you would like to see your picture look like) and negative prompt (what you do not want to see in the output image). Then push the Generate button, and the wonderful AI tool will kindly draw the new illustration serving you within a minute!!!!That sounds great, don't you think? If we imagine how humans spent a huge amount of time in the past to publish just 1 single piece of art. (Yeah, today, in 2024, in my personal opinion, both AI and human abilities are still not fully replaceable, especially in the terms of beautiful perfect hand :P ) However, the backbone or what happens behind the User-friendly menu allows us to "Select model", "Add LoRA", "Add ControlNet", "Set the aspect ratio (the original size of the image)" and so on, all of them are collected "Node" in a very complex "Workflow" PS.1. The Checkpoint or The Model often refers to the same thing. They are the core program that had been trained to draw the illustration. Each one has its strengths and weaknesses (I.E. Anime oriented or Realistic oriented) PS.2. The LoRA (Low-Rank Adaptation) is like an add-on to the Model allowing it to adapt to a different style, theme, and user preference. A concrete example is the Anime Character LoRAPS.3 The ControlNet is like a condition setting of the image. It helps the model to truly understand what is beyond the text prompt can describe. For instance, how a character poses in each direction and the angle of the camera.So here comes "The Comfyflow" (the nickname of the Workflow, people also mentioned it by the name "ComfyUI") which gives me a super headache when I see things like this for the first time in my life!!!!!!!!!(This image is a flow I have spent a lot of time studying, it is a flow for combining what is in the two images into a single one) Yeah, maybe, it is my fault that did not go to class about the workflow from the beginning or search for the tutorial on YouTube the first time (as my first language is not English). But would it be better if we had an instructor to tell us step-by-step here in Tensor.ArtAnd that is the reason why I got inspired to write this article solely for the beginner. So let's start with the main content of the article.What is ComfyFlowComfyFlow or the Workflow is an innovative AI image-generating platform that allows users to create stunning visuals with ease. To get the most out of this tool, it's important to understand two key concepts: "workflow" and "node." Let's break these down in the simplest way possible.What is a Workflow?A workflow is like a blueprint or a recipe that guides the creation of an image. Just as a recipe outlines the steps to make a dish, a workflow outlines the steps and processes needed to generate an image. It’s a sequence of actions that the AI follows to produce the final output.Think of it like this:Recipe (Workflow): Tells you what ingredients to use and in what order.Ingredients (Nodes): Each step or component used in the recipe.Despite the recommended pre-set template that TensorArt kindly gives to the users, from the beginner view's viewpoint without the knowledge of the workflow, it is not that helpful because, after clicking the "Try" button, we will bombarded with the complexity of the Node!!!!!!!What is a Node?Nodes are the building blocks of a workflow. Each node represents a specific action or process that contributes to the final image. In ComfyFlow, nodes can be thought of as individual steps in the workflow, each performing a distinct function.Imagine nodes as parts of a puzzle:Nodes: Individual pieces that fit together to complete the picture (workflow).How Do Workflows and Nodes Work Together? 1-2) Starting Point: Every workflow begins with an initial node, which might be an image input from the user, together with Checkpoint and LoRA serving the role of image references. 3-4) Processing Nodes: These are nodes that draw or modify the image in some way, such as adding color, or texture, or applying filters. 5) Ending Point: The node outputs the completed image which works very closely with the node of the previous stage in terms of sampling and VAE PS. A Variational Autoencoder (VAE) is a generative model that learns input data, such as images, to reconstruct and generate new, similar, or variations of images based on the patterns it has learned.Here is the list of nodes I have used in the normal image-generating images of my Waifu using 1checkpoint, and 2LoRAs to help the reader understand how ComfyFlow worksThe numbers 1-5 represent the overview process of the workflow and the role of each type of node I have mentioned above. However, in the case of more complex tasks like in AI Tools, the number of nodes sometimes is higher than 30!!!!!!!By the way, when starting with an empty ComfyFlow page, the way to add a node is "Right Click" -> "Add Node" -> Scroll down to the top, since the most frequently used node will be over there.1) loaders -> Load CheckPointLike in the normal task creation menu, this node is the one we can choose CheckPoint or the Core model.It is important to note that nodes work together using input/output. The "Model/CLIP/VAE" (the output) circles have to connect to the next one in which it has to correspond. We link them together by left-clicking on the circle's inner area and then drag to the destination. PS. CLIP (Contrastive Language-Image Pre-training) is a model developed by OpenAI that links images and text together in a way that helps AI understand and generate images based on textual descriptions.2) loaders -> Load LoRACheckpoint is very closely related to LoRA and that is a reason why they are connected by the input/output named "model/MODEL", "clip/CLIP"Anyway, since in this example, I have used 2 LoRAs (first for The theme of the picture and the Second for the character reference of my Waifu), two nodes of LoRAs then have to be connected as well. Here we can adjust the strength of the LoRA or the weight like it happens in the normal task generation menu.3) CLIP Text Encode (Prompt)This node is the prompt and negative prompt we normally see in the menu. The input here is only clip (Contrastive Language-Image Pre-training) and the output is "CONDITIONING" User tip: If you click on the output circle of the "Load LoRA" node and drag it to the empty area, the ComfyFlow will pop up a corresponding next node list to create a new one with ease. 4) KSampler & Empty Latent ImageThe sampling method is used to tell the AI how it should start generating visual patterns from the initial noise and everything associated with its adjustment will be set here in this type of sampling node together with "Empty Latent Image" The inputs in this step here are models (from LoRA node), positive and negative (from prompt node) and the output is "Latent"5) VAE Decode & Final output nodeOnce we establish the sampling node, the output named "LATENT" will then have to connect with "samples" Meanwhile the "vae" is the linkage between this one and the "Load Checkpoint" node from the beginning.And when everything is done the "IMAGE" as a final output here will be served at your hand.PS. An AI Tool is a more complex Workflow created to do some specific task such as swapping the face of the human in the original picture with the target face or changing the style of the input illustration to another one and etc.
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ReActor Node for ComfyUI (Face Swap)

ReActor Node for ComfyUI (Face Swap)

ReActor Node for ComfyUI 👉Downlond👈 https://github.com/lingkops4/lingko-FaceReActor-Nodeworkflowhttps://github.com/lingkops4/lingko-FaceReActor-Node/blob/main/face_reactor_workflows.jsonThe Fast and Simple Face Swap Extension Node for ComfyUI, based on ReActor SD-WebUI Face Swap ExtensionThis Node goes without NSFW filter (uncensored, use it on your own responsibility)| Installation | Usage | Troubleshooting | Updating | Disclaimer | Credits | Note!✨What's new in the latest update✨💡0.5.1 ALPHA1Support of GPEN 1024/2048 restoration models (available in the HF dataset https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models)👈[]~( ̄▽ ̄)~*ReActorFaceBoost Node - an attempt to improve the quality of swapped faces. The idea is to restore and scale the swapped face (according to the face_size parameter of the restoration model) BEFORE pasting it to the target image (via inswapper algorithms), more information is here (PR#321)InstallationSD WebUI: AUTOMATIC1111 or SD.NextStandalone (Portable) ComfyUI for WindowsUsageYou can find ReActor Nodes inside the menu ReActor or by using a search (just type "ReActor" in the search field)List of Nodes:••• Main Nodes •••💡ReActorFaceSwap (Main Node Download)👈[]~( ̄▽ ̄)~*ReActorFaceSwapOpt (Main Node with the additional Options input)ReActorOptions (Options for ReActorFaceSwapOpt)ReActorFaceBoost (Face Booster Node)ReActorMaskHelper (Masking Helper)••• Operations with Face Models •••ReActorSaveFaceModel (Save Face Model)ReActorLoadFaceModel (Load Face Model)ReActorBuildFaceModel (Build Blended Face Model)ReActorMakeFaceModelBatch (Make Face Model Batch)••• Additional Nodes •••ReActorRestoreFace (Face Restoration)ReActorImageDublicator (Dublicate one Image to Images List)ImageRGBA2RGB (Convert RGBA to RGB)Connect all required slots and run the query.Main Node Inputsinput_image - is an image to be processed (target image, analog of "target image" in the SD WebUI extension);Supported Nodes: "Load Image", "Load Video" or any other nodes providing images as an output;source_image - is an image with a face or faces to swap in the input_image (source image, analog of "source image" in the SD WebUI extension);Supported Nodes: "Load Image" or any other nodes providing images as an output;face_model - is the input for the "Load Face Model" Node or another ReActor node to provide a face model file (face embedding) you created earlier via the "Save Face Model" Node;Supported Nodes: "Load Face Model", "Build Blended Face Model";Main Node OutputsIMAGE - is an output with the resulted image;Supported Nodes: any nodes which have images as an input;FACE_MODEL - is an output providing a source face's model being built during the swapping process;Supported Nodes: "Save Face Model", "ReActor", "Make Face Model Batch";Face RestorationSince version 0.3.0 ReActor Node has a buil-in face restoration.Just download the models you want (see Installation instruction) and select one of them to restore the resulting face(s) during the faceswap. It will enhance face details and make your result more accurate.Face IndexesBy default ReActor detects faces in images from "large" to "small".You can change this option by adding ReActorFaceSwapOpt node with ReActorOptions.And if you need to specify faces, you can set indexes for source and input images.Index of the first detected face is 0.You can set indexes in the order you need.E.g.: 0,1,2 (for Source); 1,0,2 (for Input).This means: the second Input face (index = 1) will be swapped by the first Source face (index = 0) and so on.GendersYou can specify the gender to detect in images.ReActor will swap a face only if it meets the given condition.💡Face ModelsSince version 0.4.0 you can save face models as "safetensors" files (stored in ComfyUI\models\reactor\faces) and load them into ReActor implementing different scenarios and keeping super lightweight face models of the faces you use.To make new models appear in the list of the "Load Face Model" Node - just refresh the page of your ComfyUI web application.(I recommend you to use ComfyUI Manager - otherwise you workflow can be lost after you refresh the page if you didn't save it before that).TroubleshootingI. (For Windows users) If you still cannot build Insightface for some reasons or just don't want to install Visual Studio or VS C++ Build Tools - do the following:(ComfyUI Portable) From the root folder check the version of Python:run CMD and type python_embeded\python.exe -VDownload prebuilt Insightface package for Python 3.10 or for Python 3.11 (if in the previous step you see 3.11) or for Python 3.12 (if in the previous step you see 3.12) and put into the stable-diffusion-webui (A1111 or SD.Next) root folder (where you have "webui-user.bat" file) or into ComfyUI root folder if you use ComfyUI PortableFrom the root folder run:(SD WebUI) CMD and .\venv\Scripts\activate(ComfyUI Portable) run CMDThen update your PIP:(SD WebUI) python -m pip install -U pip(ComfyUI Portable) python_embeded\python.exe -m pip install -U pip💡Then install Insightface:(SD WebUI) pip install insightface-0.7.3-cp310-cp310-win_amd64.whl (for 3.10) or pip install insightface-0.7.3-cp311-cp311-win_amd64.whl (for 3.11) or pip install insightface-0.7.3-cp312-cp312-win_amd64.whl (for 3.12)(ComfyUI Portable) python_embeded\python.exe -m pip install insightface-0.7.3-cp310-cp310-win_amd64.whl (for 3.10) or python_embeded\python.exe -m pip install insightface-0.7.3-cp311-cp311-win_amd64.whl (for 3.11) or python_embeded\python.exe -m pip install insightface-0.7.3-cp312-cp312-win_amd64.whl (for 3.12)Enjoy!II. "AttributeError: 'NoneType' object has no attribute 'get'"This error may occur if there's smth wrong with the model file inswapper_128.onnx💡Try to download it manually from here and put it to the ComfyUI\models\insightface replacing existing oneIII. "reactor.execute() got an unexpected keyword argument 'reference_image'"This means that input points have been changed with the latest updateRemove the current ReActor Node from your workflow and add it againIV. ControlNet Aux Node IMPORT failed error when using with ReActor NodeClose ComfyUI if it runsGo to the ComfyUI root folder, open CMD there and run:python_embeded\python.exe -m pip uninstall -y opencv-python opencv-contrib-python opencv-python-headlesspython_embeded\python.exe -m pip install opencv-python==4.7.0.72That's it!reactor+controlnetV. "ModuleNotFoundError: No module named 'basicsr'" or "subprocess-exited-with-error" during future-0.18.3 installationDownload https://github.com/Gourieff/Assets/raw/main/comfyui-reactor-node/future-0.18.3-py3-none-any.whlPut it to ComfyUI root And run:python_embeded\python.exe -m pip install future-0.18.3-py3-none-any.whlThen:python_embeded\python.exe -m pip install basicsrVI. "fatal: fetch-pack: invalid index-pack output" when you try to git clone the repository"Try to clone with --depth=1 (last commit only):git clone --depth=1 https://github.com/Gourieff/comfyui-reactor-nodeThen retrieve the rest (if you need):git fetch --unshallow
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Upscaling in ComfyUI: ¿Algorithm or Latent?

Upscaling in ComfyUI: ¿Algorithm or Latent?

Hello again! In this little article I want to explain the upscaling methods that I know in ComfyUI and that I have researched. I hope they will help you and that you can use them in the creation of your workflows and AI tools. In addition, remember that if you have any useful knowledge, you can share it in the comments section to enrich the topic. Also, please excuse any spelling mistakes; I am just learning English hehe.¡Let’s get to the point!To the best of my knowledge, there are two widely used ways in ComfyUI to achieve uspcaling (you decide which one to use according to your needs). The two options are: Algorithm Method or Latent Method.Algorithm Method:This is one of the most commonly used method, and is readily available. It consists of loading an upscaling model, and connecting it to the workflow. That way the image pixels are manipulated as the user wishes. It is very similar to the upscale method used in the normal way of creating images in Tensor Art.The following nodes are needed:A. Load Upscale Model.B. Upscale Image (Using Model).These nodes are connected to the workflow between the “VAE Decode” and “Save Image” nodes; as shown in the image. Once this structure is created, you can choose from all the different models offered by the “Load Upscale Model” node, ranging from “2x-ESRGAN.pth” to “SwimIR_4x”. You can use any of the 23 available models and experiment with any of them. You just have to click on the node and the list will be displayed.This can also be achieved in other ways by using another node such as “Upscale Image By”. The structure is simpler to create because only that node is connected between the VAE decode and Save Image as shown in the following image.Once the node is connected, you are free to select the mode in which you want to upscale the image (Upscale_method) and you can also set the scale to which you can recondition the image pixel value (Scale By).Strengths and Weaknesses of the Algorithm Method:Among the strengths of this method are its ease of integration into the workflow and its advantage of choosing between several upscaling model options. It also allows fast generation both in the ComfyUI and in the use of AI tools.However, among its weaknesses, it is not very effective in some specific contexts. For example: the algorithm can upscale the image pixels but does not alter the actual image size; causing the generated image itself to end up being blurred in some cases.Latent Method:This is the other alternative option to the algorithm method. It is focused on highlighting image details and maximizing quality. This method is also one of the most used in the Workflow mode of different visual content creation platforms with artificial intelligence. Here, upscaling is performed while the image is generated from latent space (Latent space is where the IA takes data from the prompt, deconstructs it for analysis and then reconstructs it to represent it in an image).The Latent Upscale node is placed between the two Ksamplers. While the first Ksampler is connected to the “Empty Latent Image” node, the second one is connected to the “VAE Decode” to ensure the correct processing and representation of the generated image.It should be noted that the “Empty Latent Image” node and the “VAE Decode” node are already included by default in the Text2Image templates in WorkFlow mode. (For more information about Text2Image, you can see my other article called “ComfyUi: Text2Image Basic Glossary”).It is important to take into consideration that for this method to work properly, you have to know how to create a correct balance between the original size of the image and its upscaled size. For example, you can generate a 512x512 image and upscale it to 1024x1024; but it is not recommended to make a 512x512 image (square image) and upscale it to 768x11152 (rectangular image) since the shape of the image would not be compatible with its uspcale version. For this reason you have to pay attention to the values of the “Empty Latent Image” and the “Latent Upscale”, so that these are always proportional.In the “Empty Latent Image” node you must place the original image dimensions (for example: 768x1152); while in the “Latent Upscale” node you must place the resized image dimensions (for example: 1152x1728). In this way you are given the freedom to set the image size to your own discretion. For this I always recommend to look at the size and upscale of the normal mode in which we create illustrations, this way we will always know which values to set and which will be compatible. As you can see in the image. You look at those values, and then write them to the nodes listed above.Once everything is connected and configured, you are able to have images of any size you want. You can experiment to your taste.Strengths and Weaknesses of the Latent Method:As strengths this option should be highlighted that it allows you to access excellent quality images if everything is correctly configured. It also allows you to create images of a custom size and upscale with the values you want. It brings out the details in both SD and XL images.As negative points we have to configure everything manually every time you want to change the size of the images or the shape of the same. Also, this method is just a little bit slower in the generation process compared to the algorithm method.Which is better: ¿Algorithm or Latent?Neither method is better than the other. Both are useful in different contexts. Remember that workflows will be different from user to user, because we all have different ways of creating and designing things.It all depends on your taste and whether you want something simpler or more elaborate. I hope the explanation in this article has helped you to make Workflows more complex and to make it easier to make the images you want.Extra Tip:If you do not find any of the nodes outlined in this document. You can double click on any empty place in the workflow and you can search for the name of the node you are looking for. Just remember to type the name without spaces.
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Mastering Graph Representation Learning: Unlocking the Mysteries of Node Mapping and Random Walks

Mastering Graph Representation Learning: Unlocking the Mysteries of Node Mapping and Random Walks

Graph Representation Learning: Unveiling the Secrets of Node Mapping and Random WalksNode Mapping: Simplifying ComplexityNode mapping is like using mathematical functions to represent images. By translating an image into a function, we can drastically simplify it. This technique also allows us to reduce the dimensionality of three-dimensional data, shrinking the overall data magnitude from an n-th power of singular data to a magnitude of 2n, thus significantly reducing the total data volume.The Magic of Random WalksUnbiased Random Walks: Imagine exploring a maze where every path is equally probable. Unbiased random walks represent this scenario, where the probability of moving in any direction is the same.Biased Random Walks: Now, picture a maze where some paths are more likely to be chosen. Biased random walks occur when there is a higher probability of moving in a certain direction.Decoding with EncodersFeature Processing: Encoders help us process data features, enabling us to understand the similarities between different data points based on their paths.Similarity Representation: Think of it as measuring the closeness of two friends based on how often their paths cross.Deep Walks with Consistent StepsRandom Walk: Begin by performing a random walk on the graph. Start at a node and move randomly along the edges of the graph for a set number of steps, forming a random walk sequence. This is like exploring the connections between nodes by wandering through the graph.Word2Vec Model: Apply the Word2Vec model to each random walk sequence to learn the vector representation of nodes. The model treats node sequences as word sequences, using a neural network to learn node vectors. Nodes that frequently appear together in sequences are closer in vector space.Node Representation Learning: Finally, map each node into a low-dimensional vector space using the trained Word2Vec model. These representations can be used for graph analysis, node classification, link prediction, and more.Visualizing Graphs in ImagesFor a generated image, the walk path represents the movement path of pixels or feature points in the image. This path records the movement trajectory on the image, starting from an initial position and following certain rules.Specifically, the walk path can be interpreted as:Feature Point Movement Trajectory: If the nodes in the image represent feature points or key points, the walk path can be understood as the movement trajectory between these points. This path captures the structural information or key features of the image.Pixel Scanning Order: If the image is a collection of pixels, the walk path represents the scanning order of these pixels. This path helps traverse the image and capture its content or texture information.Object Movement Path: If the nodes in the image represent objects, the walk path can be understood as the movement path of these objects. This path simulates the motion or behavior of objects within the image.Graph representation learning unveils the hidden connections and movements within data, transforming complex relationships into understandable patterns. Through node mapping and random walks, we can unlock new insights and applications in data analysis and visualization.
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The Preprocessor for Controlnet

The Preprocessor for Controlnet

What is ControlNet ?ControlNet is a neural network that controls image generation in Stable Diffusion by adding extra conditions. Details can be found in the article Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and coworkers.The first step of using ControlNet is to choose a preprocessor, it process the image so they can be a guidance for stable diffusion to understand, you can add one or more to make things more like you want to achieve by :Depth MapLine ArtOpenPoseor even face id to control the faceDepthThe depth preprocessor guesses the depth information from the reference image.Depth Midas: A classic depth estimator. Also used in the Official v2 depth-to-image model.Depth Leres: More details but also tend to render background.Depth Leres++: Even more details.Zoe: The level of detail sits between Midas and Leres.Depth Anything: A newer and enhanced depth model.example : Line ArtLine Art renders the outline of an image. It attempts to convert it to a simple drawing.There are a few line art preprocessors.Line art anime: Anime-style linesLine art anime denoise: Anime-style lines with fewer details.Line art realistic: Realistic-style lines.Line art coarse: Realistic-style lines with heavier weight.example :OpenPoseThere are multiple OpenPose preprocessors.OpenPose detects human key points such as positions of the head, shoulders, hands, etc. It is useful for copying human poses without copying other details like outfits, hairstyles, and backgrounds. in Comfyui you can use DWOpenpose preprocessors and change as you neeed accordinglyexample : Color Palette ReferenceYou can color palette as reference to create a new imageexample :note : each image that already passed to Preprocessor is from the first image.and many more..can you help me pointing out ?this is my first time creating an article like this, please correct me if i am wrongfor more context you can read at : https://stable-diffusion-art.com/controlnet
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How to use nodes

How to use nodes

There are a few ways to start editing within the comfUI. ComfUI is a drag and drop platform - which means you can drag images from outside of ComfUI or paste in text or images anywhere into the ComfUI canvasYou can also drag in nodes. The general idea of ComfUI is to connect nodes to one another to form workflows. To connect a node, simply drag an output from one node into another node’s input.Nodes are color coded by their type and can only be connected to other nodes that take their output as an input. A node’s outline will turn blue when you’re able to connect two nodes to one another.Additionally, some nodes can have more than one input and output. When this is the case, extra inputs and outputs are usually labeled with what they expect.To navigate the ComfUI canvas you can scroll or use your keyboard. Arrow keys will navigate you around the canvas while holding down command + arrow keys will toggle between different nodes.You can multi-select nodes and make batch edits such as dragging them around the canvas, deleting, or copying and pasting. You can also group nodes together to condense multiple nodes into a single group node.Tensor.art are constantly adding new nodes to ComfyUI. Here’s an exhaustive list of all the nodes that are currently available. Utility nodesBatch (images) provides a container for multiple images that can be processed as a batch. You can drag and drop images into a batch node. If you’re running Stable Diffusion, for example, and are outputting four images, you can send those images to the batch (images) node to see all four results. Batch nodes are also used as entry points and exit points for workflow templates - which we’ll cover in the workflow sectionBatch (text) provides a container for multiple pieces of text or documents that can be processed as a batch. You can drag and drop text snippets or text documents into a Batch node. Batch nodes are also used as entry points and exit points for workflow templates - which we’ll cover in the workflow sectionNotes allow you to annotate your canvas and do things like describe the functions of complex workflows. You can connect a single notes node to multiple other nodesRepeater will repeat the content (be it an image or text) of a previous node. Repeater nodes are useful for when you need to use the same input and send it to multiple other nodesSave to folder will save any data provided to it to the folder you specify on your computerSequencer iterates through a list of items (like images) at a fixed interval. However, instead of looping through the list one item at a time, the Sequencer node can send multiple items at each interval. The number of items is defined by the node's window, while the distance from the current window is defined by the node's strideShortcut allows you to incorporate a MacOS shortcut into your workflow. Size configures the width and height of an image. The size node will default to 512 x 512Timer fires an event at a configurable time intervalImage nodesAnimated Image uses a series of images to create videos or APNG and GIF imagesAspect ratio crops an image to the selected aspect ratio.Blend with mask uses the black regions of a mask to remove parts of an image. When provided with a background image, this node will use the black areas of an image mask to erase parts of the foreground image, which will let the background image shine through. You can combine a transparent foreground image (produced with the color node) with a background image to create a transparent cutout in the background image.Canny uses a canny edge-detection algorithm to show the edges of an image. Use the canny node in conjunction with the Stable Diffusion node to control which parts of an image Stable Diffusion draws into. The canny node works well for objects and structured poses, but it can also outline facial features such as wrinkles.Color creates an image from a color of your choice.Composite places two images on top of one another. Use the composite node to place a foreground image on top of a background image. This node is especially useful when foreground options have transparent regions.Crop transparent pixels will crop an image to the bounds of its opaque pixels. Depth map creates a grayscale image that represents the distance of objects in the original image to the camera. This node is useful as an input to the Stable Diffusion node.Desaturate adjusts an image’s saturation level based on a sliderDetect poses will estimate human poses that are present in an image. You can click and drag joints to change a poseDominant colors finds up to 12 dominant colors in an image and extracts themErase object lets you paint over an object to remove it from an imageFind faces will identify a face in an image and produce a black and white mask from the faceGradient creates a gradient from a series of colors. Linear and radial gradients are both optionsGaussian Blur adds a gaussian blur to an image with an adjustable blur radius.Holistic Edges uses holistically nested edge detection (HED) to draw edges in an image with softer, less crisp outlines. This is particularly useful with the Stable Diffusion nodeImage provides an empty image container. Drag an image file onto this node to fill it. Alternatively, use the node’s contextual menu (accessible via right or option click) to add a photo or sketch from your iPhone or iPadInvert reverses the colors of an imageMask gives you a free-hand brush or machine learning model to selectively mask out portions of an imageOpacity will change the opacity of an input imagePaint gives you a free-hand brush to paint over an input imageQR code generator generates a QR code from supplied text. Use the inspector to adjust the QR code’s size, color, and error-correction levelRemove background extract’s an image’s subject from its background. This node provides both the extracted image and a mask. Use an Invert on the mask and combine the original image with the inverted mask in a blend with mask node to create a transparent cutout in the shape of the image’s subject.Resize changes the width and height of an image. By default, this node uses width and height values of 512 pixels.Rotate rotates an image to a provided angleSquare aspect creates an image with a square aspect ratio by placing the supplied image on a transparent background with an equal width and height.Stable Diffusion (API) generates an image by using Stability AI’s DreamBooth API. To use this node, you will need to connect your API key from Stability AI. Image generation will incur a cost but images may generate faster than running the model locallyStable Diffusion generates an image by running Stable Diffusion on your Mac. Running locally will take longer but will not incur a cost for generating an image. This node also provides options for control the regions of an image into which Stable Diffusion is allowed to draw by leveraging ControlNetSuper resolution upscales the supplied image to a 2048 x 2048 resolution. General runs the image through ESRGAN, Fine Detail through Best Buddy GAN, Photo through 4x Ultrasharp, and Artwork through RemacriTrace edges draws the edges found in an image but, unlike other edge-detection nodes, retains the image’s color. Combine this node with a desaturation node to create an image for the Stable Diffusion node’s MLSD inputThreshold produces a black-and-white image by applying a threshold value that ranges between 0 and 1 to each pixel of the supplied image. The red, green and blue channels of the thresholded image will be 1 (i.e. white) if a pixel’s value is greater than the threshold and 0 (i.e. black) if the pixel’s value is smaller than the thresholdZoom Blur blurs an image using a zoom-blur kernel and adjustable power, detail, and focus-position values
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X-Flux ControlNet V3 (Canny, Depth, Hed) Comfyui

X-Flux ControlNet V3 (Canny, Depth, Hed) Comfyui

DescriptionDownload Custom NodesLingko-x-Flux-Comfyuihttps://github.com/lingkops4/Lingko-x-Flux-Comfyui.gitDownload workflowshttps://github.com/lingkops4/Lingko-x-Flux-Comfyui/tree/main/workflowscanny_workflow.jsondepth_workflow.jsonflux-controlnet-canny-v3-workflow.jsonhed_workflow.jsonInstallationOpen the CMD/Shell and do the following:Go to ComfyUI/custom_nodesClone this repo, path should be ComfyUI/custom_nodes/x-flux-comfyui/*, where * is all the files in this repoGo to ComfyUI/custom_nodes/x-flux-comfyui/ and run python setup.pyRun ComfyUI after installing and enjoy!your /ComfyUI/custom_nodes/ folder Open the CMD/Shell Rungit clone https://github.com/lingkops4/Lingko-x-Flux-Comfyui.gitControlNet is trained on 1024x1024 resolution and works for 1024x1024 resolution. v3 version - better and realistic version, which can be used directly in ComfyUI!Downloads:X-Flux-Comfyui Nodehttps://github.com/XLabs-AI/x-flux-comfyui.gitCanny: flux-canny-controlnet-v3.safetensorshttps://huggingface.co/XLabs-AI/flux-controlnet-canny-v3/blob/main/flux-canny-controlnet-v3.safetensorsDepth: flux-depth-controlnet-v3.safetensorshttps://huggingface.co/XLabs-AI/flux-controlnet-depth-v3/blob/main/flux-depth-controlnet-v3.safetensorsHed: flux-hed-controlnet-v3.safetensorshttps://huggingface.co/XLabs-AI/flux-controlnet-hed-v3/blob/main/flux-hed-controlnet-v3.safetensorsHide
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轉貼:绘图提示词整理:第一章|常用身体部位与表情关键词 Chapter 1: Common Body Parts and Facial Expressions

轉貼:绘图提示词整理:第一章|常用身体部位与表情关键词 Chapter 1: Common Body Parts and Facial Expressions

在开始绘图前,请先明确你的构图场景、风格方向,以及希望传达的细节关键词。本期是一章辅助提示词分享,排版由AI帮我完成,我比较懒的排版常用提示词参考(双重曝光风格)以下是我经常使用的 Prompt 示例,适用于“双重曝光”风格。不同模型表现略有差异,仅供参考,如有问题欢迎指正:silhouette, within, blending, blended, merged, filled with / into, Jeddbleil, double exposure手部动作(Hand Gestures)互动与招呼类waving:挥手(角色打招呼、道别)【社交、迎接、离别】saluting:行军礼(正式致意)【军事、正式场合】high-five:击掌(庆祝、友好)【伙伴间庆祝、友谊】clapping:鼓掌(赞美、认可)【庆祝、演出】pointing:指向(强调方向或目标)【讲解、示意、互动】表达与情感类thumbs-up:竖起大拇指(赞同、鼓励)【积极反馈、喜好】thumbs-down:竖起大拇指向下(否定、不满意)【否定、表达失望】fist-clenching:握拳(愤怒、紧张、决心)【战斗前、内心斗争】open-palm:张开手掌(欢迎、防御、祈祷)【招呼、接纳、请求】finger-snapping:打响指(节奏感、魔法瞬间)【表演、变化】思考与姿态类hand-on-chin:手托下巴(沉思、困惑)【思考、犹豫】hand-on-hip:手叉腰(自信、不满)【质问、姿态感】hand-on-face:手捂脸(震惊、羞涩、失落)【反应瞬间、内向情绪】hand-on-heart:手放胸口(真诚、感动、发誓)【感情表达、承诺】表现与动态类grabbing:握住(紧张、抓住机会)【紧张时、紧急动作】fingertips-touching:手指尖接触(细心、低调、计划)【思考、低调交流】pinching:捏住(小心、关心、纠结)【检查、轻轻触碰】flipping:翻动(无聊、愤怒)【翻书、翻桌】palms-together:双掌合十(祈愿、感激、求助)【请求、祈祷】picking-up:捡起(专注、小心)【细致操作、动作】难度 / 节奏类spreading-fingers:张开手指(强烈、压力)【紧张、惊讶】gripping:紧握(控制、专注)【抓住、攻击】tapping-fingers:轻敲手指(焦虑、不耐)【等待、急切】其他细节表现类shaking-hand:握手(交流、协议)【社交、合作】twisting-wrist:转动手腕(疲劳、无聊)【细节动作】flexing-fingers:弯曲手指(放松、恢复、体操)【手部活动】resting-hand:手轻放(放松、休息)【情感松弛】clapping-back:背后鼓掌(偷偷支持)【暗中支持】腿部动作(Leg Gestures)姿势类standing-with-legs-apart:双腿分开站立(稳重、自信)【战斗准备、威严展示】standing-with-legs-crossed:双腿交叉站立(休闲、放松)【等待、聊天场景】sitting-cross-legged:盘腿坐(随意、放松、冥想)【居家、教室、自然场景】sitting-with-legs-stretched:双腿伸直坐(疲惫、放松)【旅行、休息】sitting-with-legs-bent:双腿弯曲坐(含蓄、少女感)【害羞、情绪细腻场景】walking-with-long-strides:大步走(自信、紧迫)【赶路、任务场景】walking-with-short-steps:小步走(谨慎、优雅)【紧张、礼仪场景】kneeling:跪姿(恳求、忠诚、礼拜)【宗教、情感高潮】squatting:蹲下(观察、调皮、准备动作)【低视角互动、行动开始】脚部动作类tip-toeing:脚尖站立(轻盈、偷偷、谨慎)【悄悄行动、偷袭】pointing-toe:指向脚尖(展现优雅、舞蹈动作)【舞蹈、优雅动作】flexing-ankles:弯曲脚踝(疲劳、恢复、舒展)【休息、放松】bouncing-on-toes:踮脚跳跃(活力、期待)【紧张、急切等】foot-tapping:脚尖敲击地面(焦虑、不耐)【等待、急切】crossing-ankles:交叉脚踝(优雅、放松)【放松、优雅的坐姿】rocking-back-and-forth:前后摇摆(焦虑、不安)【焦虑、等待】raising-heel:提起脚跟(不安、注意力集中)【紧张、预备动作】kicking:踢(愤怒、冲动)【防御、发泄愤怒】stepping-forward:向前迈步(进取、自信)【进攻、走向目标】stepping-backward:向后退步(后退、避免)【防守、撤退】twisting-feet:脚部旋转(困惑、不安)【不耐烦、焦虑】脚趾动作类wiggling-toes:脚趾扭动(放松、舒适)【休息、放松】curling-toes:脚趾蜷曲(紧张、不安)【紧张状态】pointing-toe:脚趾指向(优雅、舞蹈)【舞蹈、表现】scrunching-toes:脚趾收缩(不适、困扰)【疲惫、不适】tapping-toes:脚趾轻敲(不耐烦、急切)【等待、焦虑】spreading-toes:脚趾张开(放松、舒展)【恢复、轻松】pressing-toes:脚趾用力(紧张、力量)【控制、发力】手指动作(Finger Gestures)指令与强调类pointing-with-index-finger:用食指指向(下指令、强调)【命令、讲课、展示物品】making-a-fist:握拳(愤怒、斗志)【决斗、激动、煽动情绪】making-a-peace-sign:竖两指(胜利、和平、自拍)【合影、流行文化】making-a-come-here-gesture:招手让人过来(暗示、引导)【角色互动、引导】making-a-stop-gesture:手掌前伸(制止、警告)【争执、警觉】making-a-call-me-gesture:拇指和小指张开(联系、社交)【俏皮、暗示沟通】making-a-thumbs-up-gesture:举拇指(称赞、肯定)【胜利、认可】making-a-thumbs-down-gesture:拇指向下(反对、否定)【拒绝、嘲讽】手指与手掌互动类(Finger and Palm Interaction)pointing-with-multiple-fingers:多指指向(指示、强调方向)【指引、指向】touching-fingers:手指互相触碰(思考、犹豫、细致)【思考、谨慎】holding-a-finger-to-lips:食指放在嘴唇上(示意安静、谨慎)【安静、隐秘】index-finger-and-thumb-pinch:食指和拇指捏住(轻触、审视)【检查、敏感操作】手腕动作类(Wrist Gestures)wrist-twist:转动手腕(放松、无聊)【疲劳、细节动作】flexing-wrist:弯曲手腕(用力、强迫)【力量、控制】shaking-wrist:摇晃手腕(不耐烦、失望)【焦虑、反应】flicking-wrist:振动手腕(生气、激动)【快速反应、警告】手臂动作类(Arm Gestures)raising-arm:举臂(求救、问询)【寻求帮助、庆祝】lowering-arm:放下手臂(放松、平静)【失望、退却】crossing-arms:双臂交叉(防御、不信任)【拒绝、冷漠】stretching-arm:伸展手臂(放松、舒展)【恢复、疲劳后的动作】elbow-pointing:弯曲肘部指向(暗示、挑衅)【无声交流、态度表现】pushing-with-arm:用手臂推(推动、抵抗)【防守、推动】pulling-with-arm:用手臂拉(拉住、吸引)【控制、反向动作】其他手部细节动作(Other Hand Details)finger-waving:手指晃动(不满、警告)【反对、威胁】clenching-fingers:紧握手指(专注、愤怒)【紧张、决心】stretching-fingers:伸展手指(放松、恢复)【恢复、放松】thumb-over-finger:大拇指压住食指(决策、犹豫)【考虑、思考】头部动作(Head Gestures)同意与否定类nodding:点头(同意、认可)【正常对话、肯定答复】shaking-head:摇头(否定、不信)【拒绝、怀疑】思考与疑惑类tilting-head:侧头(疑惑、卖萌)【思考、好奇、撒娇】looking-up:向上看(幻想、灵感、请求)【祈祷、星空、想象力】looking-down:低头(思考、内敛、羞涩)【伤感、失落、沉思】情感与表达类bowing-head:低头(羞愧、礼貌、悲伤)【认错、反省、正式场合】raising-eyebrows:挑眉(惊讶、调情)【表情丰富、反应场景】furrowing-brows:皱眉(担心、生气)【冲突、压力场景】winking:眨眼(打趣、默契)【俏皮、暗号】blinking:眨眼(自然动作、困倦)【情绪缓冲、转场】面部与头部动作组合(Head + Facial Combos)head-tilt-with-smile:侧头微笑(撒娇、魅力、亲和力)【亲近、温柔】head-tilt-with-frown:侧头皱眉(疑惑、不解)【好奇、质疑】face-palming:捂脸(震惊、羞愧、失望)【意外、尴尬】looking-away:偏头看向远方(思考、逃避)【沉思、逃避目光】raising-chin:抬起下巴(自信、挑战)【坚定、不屈】颈部动作(Neck Gestures)neck-tilting:颈部倾斜(思考、质疑)【疑问、好奇】neck-craning:颈部伸展(好奇、注视远方)【寻找、观察】neck-rotation:旋转脖子(放松、缓解压力)【舒展、放松】neck-stretch:伸展脖部(疲劳、放松)【解除紧张、活动】面部组合动作(Facial Combos)smiling-with-raised-eyebrows:微笑配挑眉(高兴、调皮)【友好、轻松】frowning-with-raised-eyebrows:皱眉配挑眉(怀疑、惊讶)【困惑、质疑】smiling-with-winking:微笑配眨眼(调皮、暗示)【俏皮、暗号】frowning-with-winking:皱眉配眨眼(困惑、调皮)【打趣、示意】眼睛与头部动作组合(Eye + Head Gestures)eyes-widening:眼睛睁大(惊讶、震惊)【意外、吃惊】narrowing-eyes:眼睛眯起(怀疑、警觉)【质疑、警惕】rolling-eyes:眼睛翻白(不耐烦、嫌弃)【厌烦、失望】eye-contact:目光接触(专注、信任)【交流、示意】looking-over:眼睛扫视(挑衅、评估)【观察、对比】looking-around:眼睛环视(好奇、警觉)【寻找、四处观察】面部细节与组合动作(Facial Detail & Combinations)smiling-with-closed-eyes:微笑配闭眼(幸福、喜悦)【愉悦、感动】squinting:眯眼(困惑、仔细看)【难以看清、集中注意】grinning-with-closed-eyes:咧嘴微笑配闭眼(极度高兴、得意)【喜悦、开心】smiling-with-one-eye-closed:微笑配一只眼闭(狡猾、戏谑)【暗示、轻松】以上作者MR.Gao
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ComfyUI-Detail-Daemon - Enhance Images Details

ComfyUI-Detail-Daemon - Enhance Images Details

Hello everyone, I want to recommend a ComfyUI node, that helps to enhance the details of the images, mainy with Flux, but it can be used with SDXL and SD1.5https://github.com/Jonseed/ComfyUI-Detail-Daemon
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