Site IconAI Tools
ComfyUI icon

ComfyUI

A powerful and modular Stable Diffusion GUI, based on a node workflow for image generation.

View on GitHub

ComfyUI: Node-Based Interface for Advanced Stable Diffusion Workflows

Introduction

ComfyUI (developed by ComfyAnonymous and community contributors, hosted at github.com/comfyanonymous/ComfyUI) is a powerful and highly modular open-source graphical user interface (GUI) for Stable Diffusion and other AI image and video generation models. It stands out due to its unique graph/nodes/flowchart-based interface, which allows users to design, execute, and share complex image generation pipelines with granular control over every step, all without needing to write traditional code.

Targeted at AI artists, developers, researchers, and technical users who seek deep customization and flexibility, ComfyUI provides a transparent way to experiment with and combine various models, samplers, and processing nodes. Its efficiency in resource management, particularly VRAM, also makes it a popular choice for running advanced workflows on consumer-grade hardware.

Key Features

ComfyUI offers a distinctive set of features centered around its node-based architecture:

  • Node-Based/Flowchart Interface:
    • Users build image (and video/audio with custom nodes) generation workflows by connecting various nodes, each representing a specific operation (e.g., loading a model, encoding a prompt, sampling, VAE decoding, upscaling).
    • Provides a visual representation of the entire generation pipeline, offering clarity and control.
  • Extensive Model Support:
    • Stable Diffusion Models: Supports all major versions including SD1.x, SD2.x, SDXL, SDXL Turbo, Stable Cascade, and the latest Stable Diffusion 3 (SD3) and SD3.5.
    • Video Models: Compatible with models like Stable Video Diffusion (SVD) for image-to-video and text-to-video tasks (often via custom nodes).
    • Audio Models: Support for models like Stable Audio (often via custom nodes).
    • 3D Models: Experimental support for models like Hunyuan3D 2.0.
    • Customization Components: Full support for loading and integrating:
      • Checkpoints (Base Models): .ckpt or .safetensors files.
      • LoRAs (Low-Rank Adaptations) & LyCORIS: For applying specific styles, characters, or concepts.
      • Textual Inversions (Embeddings): For custom concepts triggered by keywords.
      • VAEs (Variational Auto-Encoders): For image quality and color refinement.
      • ControlNet & T2I-Adapters: For precise control over image composition using conditioning images (depth maps, poses, canny edges, etc.).
  • Workflow Management & Sharing:
    • Save/Load Workflows: Workflows (the arrangement of nodes and their settings) can be saved as JSON files.
    • Embed Workflow in Images: Generated PNG images can embed the full workflow data, allowing others to easily load and reproduce or modify the exact setup.
  • Extensibility via Custom Nodes:
    • A vast and rapidly growing ecosystem of community-developed custom nodes extends ComfyUI's capabilities significantly.
    • ComfyUI Manager: A popular custom node that helps users install, update, and manage other custom nodes and missing models.
  • Efficient Resource Management:
    • Known for its smart memory management, allowing users to run complex workflows and large models (like SDXL and SD3) on GPUs with relatively lower VRAM (as low as 1-3GB for some basic tasks, though more is always better).
    • Only loads necessary model components into VRAM when they are actively used in the workflow.
  • Core Stable Diffusion Functionalities:
    • Text-to-Image (txt2img)
    • Image-to-Image (img2img)
    • Inpainting & Outpainting (often implemented with specialized nodes or workflows)
    • High-Resolution Fix (Hires Fix) workflows.
    • Upscaling (using various upscaler models and nodes).
  • Batch Processing & Queuing System:
    • Allows users to queue multiple generation tasks with different parameters.
  • Cross-Platform: Runs on Windows, Linux, and macOS (including Apple Silicon).
  • Open Source: Licensed under GPL-3.0, encouraging community development and modifications.
  • Headless Operation & API Potential: Can be run in a headless mode, and its structure allows for programmatic interaction, enabling it to be used as a backend for other applications or automated pipelines (e.g., running workflows via API calls).

Specific Use Cases

ComfyUI's flexibility and granular control make it ideal for:

  • Advanced AI Art Generation: Creating highly customized and complex images by precisely controlling every aspect of the diffusion process.
  • Experimental Image & Video Generation: Testing new models, techniques, and complex multi-stage workflows that might be difficult to implement in more linear UIs.
  • Research in Diffusion Models: Providing a transparent and modular environment for researchers to experiment with different components of the diffusion pipeline.
  • Precise Parameter Control & Reproducibility: Fine-tuning every parameter and easily saving/sharing exact workflows ensures reproducibility.
  • Developing Custom Image Generation Pipelines: Tailoring workflows for specific needs, such as character consistency, style blending, or complex ControlNet setups.
  • Batch Image Generation with Variations: Efficiently generating large sets of images with systematic variations in prompts, seeds, or other parameters.
  • Inpainting, Outpainting, and Complex Image Editing: Building sophisticated workflows for detailed image manipulation.
  • Learning Stable Diffusion Internals: The node-based structure helps users understand the flow of data and operations within the Stable Diffusion process.
  • Automated Image Generation (via API/scripting): Using ComfyUI as a backend for automated image creation tasks.

Usage Guide

Getting started with ComfyUI involves installation and learning its node-based interface:

  1. System Requirements:

    • GPU (Highly Recommended):
      • NVIDIA: Most NVIDIA GPUs with CUDA support will work. VRAM is crucial:
        • 3-4GB VRAM: Can run SD1.5 models, potentially with --lowvram flags.
        • 6-8GB VRAM: Good for SD1.5, basic SDXL usage.
        • 12GB+ VRAM: Recommended for comfortable SDXL, SD3, and more complex workflows.
      • AMD: Supported on Linux (via ROCm) and Windows (via DirectML or specific PyTorch versions). Setup can be more involved.
    • Apple Silicon (M-series): Supported via PyTorch's Metal Performance Shaders (MPS).
    • CPU-Only Mode: Possible but extremely slow for most practical image generation.
    • RAM: 8GB system RAM minimum, 16GB+ strongly recommended.
    • Storage: SSD is highly recommended. Space for ComfyUI, Python, dependencies, and model files (checkpoints, LoRAs, etc.).
    • Python: Version 3.10, 3.11, or 3.12 are commonly recommended (check current ComfyUI requirements).

  2. Installation:

    • Windows Portable: Download the standalone build for NVIDIA GPUs (or CPU-only) from the ComfyUI GitHub releases page. Extract and run. This is often the easiest way for Windows NVIDIA users.
    • Manual Installation (Windows, Linux, macOS):
      1. Install Python (ensure it's in your PATH).
      2. Install Git.
      3. Clone the ComfyUI repository: git clone https://github.com/comfyanonymous/ComfyUI.git
      4. Navigate to the ComfyUI directory: cd ComfyUI
      5. Install dependencies: pip install -r requirements.txt
      6. Install PyTorch with appropriate GPU support (CUDA for NVIDIA, ROCm for AMD on Linux, or MPS for Apple Silicon). E.g., for NVIDIA: pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cuXXX (replace cuXXX with your CUDA version, e.g., cu118 or cu121).
    • Download Models:
      • Place Stable Diffusion checkpoint models (.ckpt or .safetensors) in the ComfyUI/models/checkpoints/ directory.
      • Place LoRAs in ComfyUI/models/loras/, VAEs in ComfyUI/models/vae/, ControlNet models in ComfyUI/models/controlnet/, etc.
      • Models can be downloaded from Hugging Face, Civitai, and other sources.
    • Install ComfyUI Manager (Highly Recommended Custom Node):
      • This custom node makes it much easier to install and manage other custom nodes and missing models. Search for "ComfyUI Manager github" for installation instructions (usually involves cloning its repository into the ComfyUI/custom_nodes/ directory).

  3. Launching ComfyUI:

    • Windows Portable: Run the provided .bat file (e.g., run_nvidia_gpu.bat or run_cpu.bat).
    • Manual Install: Navigate to the ComfyUI directory in your terminal and run python main.py.
    • You might need to add command-line arguments for specific features (e.g., --listen to allow network access, --port XXXX for a different port, GPU selection flags).
    • Access the WebUI by opening http://127.0.0.1:8188 (default port) in your browser.

  4. Navigating the Node-Based Interface:

    • Canvas: The main area where you build your workflow by adding and connecting nodes.
    • Menu/Manager: A floating menu (often accessed by right-clicking on the canvas or a side panel) allows you to load default workflows, save/load workflows (JSON), manage the queue, and access the ComfyUI Manager.
    • Adding Nodes: Double-click on the canvas or right-click and select "Add Node" to search and add different types of nodes (loaders, samplers, image operations, etc.).
    • Connecting Nodes: Drag from an output slot of one node to an input slot of another compatible node to create a connection (edge/wire). Data flows along these connections.
    • Node Parameters: Each node has specific parameters you can adjust (e.g., model selection in "Load Checkpoint," prompt text in "CLIP Text Encode," seed/steps/CFG in "KSampler").

  5. Building a Basic Text-to-Image Workflow:

    • A default txt2img workflow often loads on startup. It typically includes:
      • Load Checkpoint: Select your base Stable Diffusion model.
      • CLIP Text Encode (Prompt): Input your positive prompt.
      • CLIP Text Encode (Prompt): Input your negative prompt.
      • Empty Latent Image: Define the output image dimensions.
      • KSampler (or KSampler Advanced): The core sampling node. Connect the model, positive/negative conditioning, and latent image. Set seed, steps, CFG, sampler name, scheduler.
      • VAE Decode: Converts the latent image from the KSampler into a pixel-space image. Connect the VAE from your checkpoint loader.
      • Save Image (or Preview Image): To see or save the output.

  6. Running the Workflow:

    • Click "Queue Prompt" (or press Ctrl+Enter / Cmd+Enter). The nodes will execute in order, and the final image will appear in the Preview Image or Save Image node.

  7. Saving/Loading Workflows:

    • Use the "Save (API Format)" and "Load" buttons in the menu to save your node setups as JSON files.
    • You can also drag a previously generated PNG image (that has workflow embedded) onto the ComfyUI canvas to load that workflow.

  8. Installing Custom Nodes:

    • Use the ComfyUI Manager to easily browse, install, and update community-created custom nodes that add new functionalities. A restart of ComfyUI is usually required after installing new custom nodes.

Hardware Requirements (General Guidelines)

  • GPU (Graphics Processing Unit): Essential for practical use. ComfyUI's smart VRAM management helps, but more VRAM is always better.
    • NVIDIA: Best supported.
      • Minimum: 3-4GB VRAM (e.g., GTX 1650) can run SD1.5 models, but will be slow and very limited for SDXL/SD3.
      • Recommended for SD1.5/Basic SDXL: 6-8GB VRAM (e.g., RTX 3060).
      • Ideal for SDXL/SD3 & Complex Workflows: 12GB+ VRAM (e.g., RTX 3080/3090, RTX 4070/4080/4090).
    • AMD: Supported on Linux (ROCm) and Windows (DirectML via certain PyTorch versions). Performance and compatibility can vary more than NVIDIA.
    • Apple Silicon (M-series): Supported via PyTorch's Metal (MPS). Performance depends on the chip (M1, M2, M3 and their Pro/Max/Ultra variants) and available unified memory.
  • RAM (System Memory): 16GB is a good starting point, 32GB or more is recommended for smoother operation, especially when loading multiple models or dealing with high-resolution images.
  • Storage: SSD is highly recommended for fast loading of ComfyUI, Python, and especially the large model files. You'll need space for the ComfyUI installation, Python environment, and several GBs for each checkpoint model, plus space for LoRAs, VAEs, ControlNets, and generated images.
  • CPU: A modern multi-core CPU is beneficial for general system responsiveness and some pre/post-processing tasks, but the GPU handles the core diffusion process.

Pricing & Plans

ComfyUI is free and open-source software, licensed under the GPL-3.0 license.

  • No cost for the software itself.
  • Costs are associated with:
    • Your hardware: The computer and GPU required to run it effectively.
    • Electricity consumption.
    • Optional cloud compute: If you choose to run ComfyUI on cloud GPU instances (e.g., RunPod, Google Colab, cloud provider VMs), you'll pay for the cloud compute time. Some services like ComfyOnline.app offer cloud-based ComfyUI workflows.

Frequently Asked Questions (FAQ)

Q1: What is ComfyUI? A1: ComfyUI is a free, open-source, and powerful graphical user interface for Stable Diffusion and other AI image/video models. It uses a node-based (flowchart) system that allows users to build and execute complex AI generation pipelines with fine-grained control.

Q2: How is ComfyUI different from Automatic1111 Stable Diffusion WebUI? A2: The main difference is the interface and workflow paradigm: * ComfyUI: Uses a node-based system. You connect different operational blocks (nodes) to create a visual flowchart of your generation process. This offers extreme flexibility, transparency, and is often more VRAM efficient for complex tasks. * Automatic1111: Uses a more traditional tab-based GUI with settings and dropdowns. It's very feature-rich and has a vast ecosystem of extensions but might be less transparent about the underlying workflow compared to ComfyUI.

Q3: Is ComfyUI beginner-friendly? A3: ComfyUI has a steeper initial learning curve than some simpler UIs due to its node-based nature. However, once the basic concepts are understood, it offers unparalleled control. Many beginners start by loading pre-made workflows (shared as JSON files or embedded in images) and then gradually learn to modify and build their own.

Q4: What AI models can I use with ComfyUI? A4: ComfyUI supports a wide range of Stable Diffusion models (SD1.x, SD2.x, SDXL, SDXL Turbo, Stable Cascade, SD3, SD3.5), as well as community fine-tunes, LoRAs, LyCORIS, Textual Inversions, VAEs, and ControlNet models. Through custom nodes, support extends to other types of models like video (Stable Video Diffusion) and audio models.

Q5: Where can I download models for ComfyUI? A5: You can download Stable Diffusion models (checkpoints, LoRAs, etc.) from popular community hubs like: * Hugging Face (https://huggingface.co/models): For official base models and many open-source fine-tunes. * Civitai (https://civitai.com/): A large repository for community-created Stable Diffusion models, LoRAs, and other resources.

Q6: How do I share my ComfyUI workflows? A6: You can save your entire node setup as a JSON file. Additionally, when you generate an image (PNG), ComfyUI can embed the workflow data directly into the image file. Others can then drag that PNG onto their ComfyUI canvas to load the exact workflow.

Q7: What are custom nodes in ComfyUI? A7: Custom nodes are add-ons created by the community that extend ComfyUI's functionality. They can add new samplers, image processing tools, model loaders, UI enhancements, video generation capabilities (like AnimateDiff), and much more. The ComfyUI Manager custom node is highly recommended for easily installing and managing other custom nodes.

Q8: Does ComfyUI require a powerful GPU? A8: While ComfyUI is known for its efficient VRAM management (able to run on GPUs with as low as 1-3GB VRAM for very basic tasks), a more powerful GPU (NVIDIA with 6GB+ VRAM, ideally 8GB-12GB+ for SDXL/SD3) will provide a significantly better and faster experience, allowing for higher resolutions and more complex workflows.

Related Blog Articles & Tutorials

Here are examples of helpful resources for learning and using ComfyUI:

Community & Support

  • GitHub Repository (Discussions & Issues): The primary hub for development, bug reports, feature requests, and technical discussions. (https://github.com/comfyanonymous/ComfyUI/discussions)
  • Discord Servers: While there isn't one single official ComfyUI Discord explicitly run by comfyanonymous, many large Stable Diffusion communities have dedicated ComfyUI channels. The GitHub README mentions:
    • Unofficial ComfyUI Discord: (Often shared within the community, search for active links)
    • Matrix Space: #comfyui_space:matrix.org
  • Online Communities: Subreddits like r/ComfyUI and r/StableDiffusion are excellent places for sharing workflows, asking questions, and seeing what others are creating.

Ethical Considerations & Safety

  • User Responsibility: ComfyUI is a powerful tool that allows users to load and combine any compatible Stable Diffusion models and custom nodes. Users are solely responsible for the ethical implications of the content they generate and must adhere to the licenses of all models and assets used.
  • Content Generation: The UI itself does not inherently filter content beyond what the loaded models are trained (or not trained) to produce. Users control the models and prompts.
  • Custom Nodes: While the custom node ecosystem is a major strength, users should be cautious and install custom nodes only from trusted sources to avoid potential security risks.
  • License: ComfyUI is licensed under GPL-3.0. This means any derivative works or distributions that include ComfyUI code must also be open-sourced under the GPL-3.0 license. Using ComfyUI to generate images does not typically impose GPL restrictions on the images themselves (image rights depend on model licenses), but distributing modified versions of ComfyUI software does.

Last updated: May 16, 2025

Found an error in our documentation?Email us for assistance

Related Projects

StableDiffusion

StableDiffusion

...

The most advanced text-to-image model developed by Stability AI.

Stable Diffusion WebUI

Stable Diffusion WebUI

...

A browser interface for Stable Diffusion, featuring a rich set of features and easy-to-use interface.

InvokeAI

InvokeAI

...

A professional, creative-friendly interface for Stable Diffusion.

Back to Ai Image Generation