ComfyUI

ComfyUI is an open-source, node-based application for generating images, videos, and audio using AI diffusion models. Created by comfyanonymous (Yannik Marek) and released on January 16, 2023, it uses a graph/flowchart interface where each tool or model component is represented by a node. Users connect these nodes to build complex generative AI workflows without writing code.

Unlike traditional image generation UIs that present a simple text-prompt-to-image interface, ComfyUI exposes the entire diffusion pipeline visually. A typical workflow involves separate nodes for the checkpoint model, text encoders (CLIP), positive/negative prompts, samplers (KSampler), latent space operations, upscalers, VAEs, and image output. This modular approach gives users fine-grained control over every step of the generation process.

By mid-2026, ComfyUI had amassed over 119,000 GitHub stars, 14,000 forks, and a valuation of $500 million. It is widely considered the most powerful and flexible interface for open-source generative AI, supporting models from Stable Diffusion 1.5 through Flux and beyond.

ComfyUI began as a solo side project by Yannik Marek (online alias: comfyanonymous) in January 2023. Marek had been involved with Stability AI, and his goal was to build a better interface for Stable Diffusion that addressed the limitations of existing UIs. The original AUTOMATIC1111 WebUI had become the de facto standard, but its monolithic design made it difficult to experiment with different pipeline configurations.

Within months, the project gained a passionate following among power users who wanted more control. The node-based approach was initially intimidating for beginners, but its power was undeniable. Every time a new diffusion architecture dropped - SDXL, SD3, Stable Video Diffusion, FLUX - ComfyUI was the first to support it, often within days.

By June 2024, Marek created Comfy Org, an organization dedicated to developing and maintaining ComfyUI with a growing team of core developers. In July 2024, Nvidia announced support for ComfyUI within its RTX Remix modding software. In August 2024, support was added for the Flux diffusion model, and Comfy Org joined the Open Model Initiative under the Linux Foundation.

In Q4 2024, ComfyUI raised a $16.2 million seed round led by Pace Capital. Additional funding brought the total to $47.5 million, and by 2026 ComfyUI's valuation had reached an estimated $500 million. In 2025, a security incident involving a compromised custom node called "LLMVision" highlighted the risks of the open plugin ecosystem, prompting Comfy Org to implement node signing and better security measures.

ComfyUI's core innovation is its node-based graph interface. Every operation in the generative AI pipeline is represented as a node:

• Checkpoint Loader - loads the base diffusion model (SD1.5, SDXL, Flux, etc.)
• CLIP Text Encoder - encodes text prompts into embeddings the model understands
• KSampler - the core sampling/diffusion step, controlling steps, CFG scale, and sampler type
• VAE Decode/Encode - converts between pixel space and latent space
• ControlNet - applies additional conditioning (pose, depth, edge maps)
• Upscale - increases image resolution (Latent, ESRGAN, etc.)
• LoRA/LyCORIS - applies fine-tuned model adapters
• Image Save - saves the final output

Users connect these nodes by dragging wires between output and input sockets. The resulting graph defines the entire generation pipeline, and workflows can be saved as JSON files and shared with others.

Queue System and Parallel Execution

ComfyUI features a task queue system where multiple prompts can be queued and processed sequentially or in parallel on multi-GPU setups. The queue is visible in the interface, and users can cancel, reorder, or skip tasks.

Built-in API Mode

ComfyUI includes a built-in API server that exposes the node graph as an HTTP API. This allows developers to call ComfyUI workflows programmatically from applications, websites, or other scripts, making ComfyUI a viable backend for third-party applications.

Custom Nodes System

ComfyUI's extensibility is its superpower. Thousands of custom nodes have been created by the community, adding everything from advanced samplers and schedulers to video generation (AnimateDiff, Stable Video Diffusion), audio generation, 3D and mesh generation, image editing, face restoration (GFPGAN, CodeFormer), upscaling models, Segment Anything (SAM) for masking, and IP-Adapter for image prompting.

Supported Models

ComfyUI natively supports Stable Diffusion 1.5, 2.1, SDXL, SD3, SD3.5, Flux (dev, schnell, pro), Stable Video Diffusion, AnimateDiff, Stable Audio, ControlNet, IP-Adapter, InstantID, PuLID, DeepFloyd IF, Playground v2.5, Kolors, Hunyuan, and other models.

1. Complete Pipeline Control - Access to every parameter in the diffusion pipeline: samplers, schedulers, CFG scaling, noise injection, cross-attention control, and more.
2. Reproducible Workflows - Workflows are saved as portable JSON files with the full graph structure and parameter values, enabling exact reproduction.
3. First-Class Model Support - Consistently supports the newest open models within days of release.
4. Memory Efficiency - Only loads model components actually needed, aggressively frees GPU memory between tasks.
5. Multi-GPU and Distributed - Supports multi-GPU setups for parallel generation and model parallelism.
6. Video and Audio Generation - Through custom node integrations for AnimateDiff, Stable Video Diffusion, CogVideo, Mochi, Stable Audio, and AudioLDM 2.
7. Comfy Cloud and API - Managed hosting service for running workflows without local hardware. API enables production backend use.

Feature	ComfyUI	AUTOMATIC1111	Forge	Midjourney
Interface	Node-based graph	Tabbed form UI	Simplified form UI	Discord/Web app
Learning Curve	Steep	Moderate	Low	Very low
Pipeline Control	Full	High	Moderate	Limited
Model Support Speed	Fastest	Fast	Moderate	N/A
Custom Nodes	10,000+	3,000+	Compatible	N/A
API Mode	Built-in	Via extension	Via extension	Official API
Memory Efficiency	Excellent	Good	Good	N/A (cloud)
Video Generation	Yes (AnimateDiff, etc.)	Limited	Limited	No
License	GPLv3	AGPLv3	AGPLv3	Proprietary
GitHub Stars	119K	148K	78K	N/A

Despite being free and open source, Comfy Org has built a multi-faceted business around the project:

• Comfy Cloud - Managed hosting service where users pay for GPU compute time to run workflows remotely.
• API-as-a-Service - ComfyUI workflows exposed as billable API endpoints, taking a margin on each inference call.
• Enterprise Licensing - Customized deployments for companies needing on-premise or dedicated ComfyUI infrastructure.
• Node Marketplace (planned) - Potential revenue sharing from a curated custom node ecosystem.

The core application remains free, open source, and fully functional locally. Revenue comes from convenience and infrastructure services around it, similar to the GitLab model.

• LLMVision Extension Compromise (2025) - A popular custom node was compromised when a malicious actor distributed a forked version containing malware capable of exfiltrating data. Comfy Org responded with node signing and security warnings for unverified nodes.
• Steep Learning Curve Criticism - While power users celebrate the node-based interface, newcomers often find it overwhelming. Competitors like Forge and Fooocus gained traction by offering simplified alternatives. Comfy Org has responded with better documentation and example workflows.
• OpenAI/Adobe API Competition - As ComfyUI pushes into cloud and API markets, it competes with proprietary platforms offering legal indemnification for commercial use - a feature ComfyUI's open model ecosystem cannot guarantee due to varying model licenses.

Installation

• Local - Clone the repo, install Python dependencies, and run main.py. Works on Windows, macOS, and Linux with NVIDIA/AMD/Apple Silicon GPUs.
• Portable - Windows portable packages available for users who prefer not to set up Python manually.
• Cloud - Comfy Cloud (paid) or community-run instances on RunPod, Vast.ai, Banana, and other GPU cloud platforms.

Basic Workflow Example

A minimal text-to-image workflow in ComfyUI involves connecting: Checkpoint Loader → CLIP Text Encoder (positive prompt) → CLIP Text Encoder (negative prompt) → KSampler → VAE Decode → Image Save. This can be extended with LoRAs, ControlNets, upscalers, and face restoration by adding nodes to the graph.