ComfyUI and Node-Based Image Workflows

ComfyUI appeared in early 2023, created by a developer known as comfyanonymous. It has become one of the most widely used interfaces for open generative models, supporting Stable Diffusion variants, Flux and many video and audio models. Instead of a form with sliders, you see the pipeline itself. A minimal text-to-image graph shows each part of a latent diffusion model. Load Checkpoint outputs three things: the denoising model, the CLIP text encoder and the VAE. Two CLIP Text Encode nodes turn the positive and negative prompts into conditioning. Empty Latent Image creates the starting noise canvas at a chosen size. KSampler runs the denoising loop using the seed, step count, guidance (CFG) scale, sampler and scheduler. VAE Decode converts the final latent into pixels, and Save Image writes the file. Seeing these steps separately makes other ideas clear. Image-to-image, for example, is just starting from an encoded image and denoising it only partway. Graphs are saved as JSON and embedded in the images they produce, so results can be reproduced. ComfyUI also caches results and re-runs only the nodes whose inputs changed, so editing a prompt does not reload the model. Hardware is the main constraint. NVIDIA GPUs have the best support. Around 8 GB of VRAM is a commonly cited comfortable minimum for SDXL-class work, and larger image or video models benefit from 12 to 24 GB or more, although quantized models and memory offloading help. AMD, Intel and Apple Silicon can work with extra setup and lower speed, and CPU-only use is very slow. Custom nodes extend ComfyUI enormously, but each one is Python code that runs with your user account's permissions. There have been reported cases of malicious node packages stealing data. Install only well-known, actively maintained nodes, prefer .safetensors model files over pickle-based .ckpt files, and consider running ComfyUI in a separate environment or container.