ComfyUI is the standard for professionals creating AI images and videos. Its node-based workflow system gives precise control over every step of the generation pipeline, from model loading to final output, and makes complex setups shareable as portable JSON files.
This guide covers system requirements, your first workflow walkthrough, and how to run ComfyUI on cloud GPUs when local hardware is not enough.
ComfyUI System Requirements
VRAM is the biggest limiting factor. Knowing your hardware's ceiling before choosing models saves time.
| Component | Minimum | Recommended | Ideal |
|---|---|---|---|
| VRAM | 6GB+ | 24GB | 40GB+ |
| System RAM | 16GB | 32GB | 64GB |
| Storage | 20GB | 100GB | 200GB+ |
| Python | 3.9 | 3.13 | |
| PyTorch | 2.4+ | Latest stable | |
| OS | Windows 10, macOS | Linux | |
NVIDIA GPUs with Ampere architecture or newer (RTX 30 series onward, RTX A6000, A100) are best for ComfyUI because they support FP16 and BF16 natively. ComfyUI's dynamic VRAM management, enabled by default as of March 2026, unloads unused models between steps, letting it run pipelines that would crash other tools.
ComfyUI VRAM Requirements by Workflow
The model you load determines how much VRAM you need. Understanding the VRAM floor for your target workflow before you start saves troubleshooting time.
Image Generation VRAM
A basic SDXL workflow at 1024x1024 requires 6-8GB. Adding a ControlNet adapter adds 2-4GB, and IP-Adapter conditioning adds another 1-2GB. For Flux.1 Dev at FP8, the minimum is around 12GB; for BF16 precision, 24GB is the practical floor.
Z-Image Turbo is notable for efficiency: 6GB with GGUF quantization, 8GB with FP8, and 16GB with BF16. It generates a 1024x1024 image in roughly 2-3 seconds on an RTX 4090.
Video Generation VRAM
Video generation is the most demanding use case in ComfyUI. Wan 2.2's 5B model can run on 8GB with ComfyUI's native offloading enabled. The 14B variant requires 16-24GB for 480p output and 24GB+ for 720p. Some large video pipelines with ControlNet conditioning require 60-80GB.
For video generation workflows, see the full Wan 2.2 ComfyUI guide.
ComfyUI in the Cloud: Which GPU to Use
Cloud GPU is the practical answer when local VRAM runs out or when you want to avoid local installation and model management overhead. GPU choice depends on which models and workflow types you are running.
| Workflow | Recommended GPU | VRAM needed | Thunder Compute rate |
|---|---|---|---|
| SDXL, Flux.1 Dev (BF16), Z-Image Turbo | RTX A6000 | 16-24GB | $0.35/hr |
| Qwen-Image-Edit (FP8), Flux + ControlNet | RTX A6000 / L40 | 24-40GB | $0.35-$0.79/hr |
| Wan 2.2 14B (480p), Qwen-Image-Edit (BF16) | A100 80GB | 40-80GB | $1.09/hr |
| Wan 2.2 14B (720p+), large video pipelines | H100 80GB | 60-80GB+ | $2.19/hr |
| Last update: August 1, 2026. On-demand, billed per minute. | |||
The RTX A6000 at $0.35/hr is a solid starting point for most ComfyUI image workflows. With 48GB of GDDR6 and 768 GB/s of bandwidth, it handles SDXL, Flux, Qwen-Image-Edit at FP8, and Z-Image Turbo at a lower price than the 24GB RTX 4090 on most competitors.
For cheapest cloud GPU for ComfyUI video generation, an RTX A6000 covers Wan 2.2's 5B model and lighter video pipelines. The 14B variant at 480p fits on an A100 80GB at $1.09/hr.
Persistent storage on Thunder Compute keeps your model library ready between sessions. Downloading ~20GB of models to run Z-Image Turbo takes roughly 2 minutes; those models remain in your persistent storage volume across future sessions without re-downloading.
Getting Started With ComfyUI on Thunder Compute
Getting started takes three commands:
- Install the tnr CLI
- Run
tnr create --template comfy-ui - Configure instance specs and create it
- Run
tnr connect 0to connect - Run
start comfyui - Once running, open ComfyUI at the URL printed in your terminal.
You can import any workflow JSON, install custom nodes, and download models from Hugging Face or Civitai.
Try Thunder Compute GPUs and run ComfyUI without hardware limits.
Installing Models for ComfyUI in the Cloud
The Thunder Compute ComfyUI template includes a basic Stable Diffusion checkpoint. Most workflows require additional models.
The Template Library includes preconfigured workflows with model links in a note block. Pass those links to your AI assistant and ask it to generate bash commands for downloading models into the correct subdirectories under /home/ubuntu/ComfyUI/models/. Run those commands inside your instance and the workflow will have everything it needs.
Even large models will download quickly because Thunder Compute instances are hosted in data centers with fast internet connections.
Your First ComfyUI Workflow
ComfyUI opens to a pre-loaded default text-to-image workflow. Walking through it once is the fastest way to understand how the system works.
When you launch ComfyUI, the default text-to-image workflow loads automatically. If the canvas is empty, click the Workflows icon in the left sidebar, select Browse Example Workflows, then choose Image Generation.
The workflow connects a checkpoint loader, two CLIP text encoders, a KSampler, a VAE decoder, and a Save Image node. The colored wires carry data from one step to the next. Every ComfyUI workflow follows this same pattern: load a model, encode prompts, sample noise into a latent, decode the latent to pixels, and save the result.
Key KSampler parameters:
- Seed controls starting noise (fix it to reproduce an image; randomize to explore).
- Steps is how many denoising passes the model runs (20-30 for standard models, 4-8 for distilled models like Flux.2 Klein and Z-Image Turbo).
- CFG controls how strictly the model follows the prompt (6-9 for standard SD, 1.0-2.0 for distilled models).
- Denoise at 1.0 means a fresh generation; lower values for image-to-image preserve more of the input.
Every ComfyUI workflow is a JSON file. Export via Workflow > Export and share it with anyone. ComfyUI embeds full workflow metadata inside every PNG it generates; drag any ComfyUI-generated image back onto the canvas and the original workflow loads automatically.
Community workflows are available on OpenArt, ComfyWorkflows, and the r/comfyui subreddit.
Popular ComfyUI Models and Where to Start
The model you choose determines image quality, style, VRAM requirements, and generation speed.
Flux
Flux is an open-weight text-to-image model family from Black Forest Labs. Flux.1 Dev is the community standard for high-quality local generation. Flux.2 Klein 4B (January 2026, Apache 2.0) reduces hardware requirements to 8GB at 4 inference steps, making it the best option for fast iteration on consumer GPUs.
Stable Diffusion
Stable Diffusion remains a strong starting point. SD 1.5 runs on 4-6GB and has the largest community library of LoRAs, ControlNet models, and fine-tuned checkpoints. SDXL raises the quality ceiling and works best at 1024x1024 with 8-12GB.
See the full guide to running Stable Diffusion in the cloud.
Wan 2.2
Wan 2.2 is the leading open-source model for image-to-video and text-to-video generation. The 5B variant runs on 8GB with offloading. The 14B variant produces better motion quality but requires 16-24GB for 480p and 24GB+ for 720p.
Z-Image Turbo
Z-Image Turbo is a 6B-parameter image generation model that runs in 8 inference steps, delivering photorealism comparable to Flux at lower compute cost. BF16 requires 14-16GB; FP8 runs on 8GB; GGUF scales to 5-6GB.
Qwen for Image Generation and Editing
Qwen-Image-Edit is Alibaba's open-source image generation and editing model at 20.4B parameters. Its key differentiator is instruction-based editing: describe the change you want in plain language and the model applies it. BF16 needs at least 40GB; FP8 brings that down to 16GB.
See the full guide to using Qwen for image editing in ComfyUI.
Troubleshooting Common ComfyUI Errors
| Error | Cause | Fix |
|---|---|---|
| Nodes highlighted red after loading a workflow | Missing custom node packs | Open ComfyUI Manager, click Install Missing Custom Nodes, restart |
| Model not appearing in dropdown | Wrong directory or ComfyUI not restarted since download | Confirm file is in correct subdirectory under ComfyUI/models/, restart |
| CUDA out of memory | Model and pipeline exceed GPU VRAM | Reduce output resolution, enable --lowvram, switch to FP8 or GGUF checkpoint, or offload VAE with --cpu-vae |
| Gray or noisy output | Missing or mismatched model files | Confirm all required files (diffusion model, text encoder, VAE) are present and selected in loader nodes |
| Workflow loads but produces degraded output | Sampler settings do not match model requirements | Distilled models (Flux.2 Klein, Z-Image Turbo) need CFG 1.0-2.0 and low step counts; standard SD settings on distilled models produce oversaturated output |
ComfyUI vs. Other AI Image Generation Frontends
ComfyUI vs. AUTOMATIC1111
AUTOMATIC1111 (A1111) was the most widely used alternative to ComfyUI. It has a traditional tab-based interface: one input panel for prompts, settings, and model selection, and one output panel for your generated image.
The trade-off is flexibility. A1111 processes your generation as a black box: you set the inputs and receive the output, but you cannot inspect or redirect the data between steps. ComfyUI's node graph exposes every intermediate stage of the pipeline. You can preview the latent image mid-generation, branch two different samplers from the same prompt, and build workflows that require custom scripting in A1111.
If you are generating images occasionally and want predictable results with minimal setup, A1111 is the reasonable starting point. If you are building reproducible workflows, or pushing the limits, ComfyUI is the better investment.
ComfyUI vs. Forge Neo
Forge Neo is a fork of AUTOMATIC1111 that adds performance and memory optimizations while preserving the familiar tab-based interface. It supports many of the same models as ComfyUI and runs them with lower VRAM requirements than an equivalent A1111 setup. ComfyUI remains the stronger choice when workflow reproducibility, video generation, or complex multi-model pipelines are the goal.
ComfyUI vs. Comfy Cloud
Comfy Cloud is the official hosted version of ComfyUI from the same team, running on NVIDIA RTX 6000 Pro GPUs. It offers a free tier (400 credits/month) and paid plans from $20/month. It's a good starting point for users who want to try ComfyUI without any setup.
For heavier workloads, Thunder Compute is more flexible: you choose the specific GPU tier you need (A6000, A100, H100), billing is per-minute rather than credit-based, and the VS Code extension gives you full access to the instance environment for custom node development and automated pipeline scripts. The per-minute billing also typically works out cheaper for extended generation sessions compared to a fixed credit rate.
Last Thoughts on How to Use ComfyUI
ComfyUI is the most capable frontend for AI image and video generation in 2026. Its node graph makes pipelines reproducible and shareable, and dynamic VRAM management runs larger models than most tools allow. The main barrier is hardware, and cloud GPUs remove that constraint entirely.
For most image workflows, an RTX A6000 at $0.35/hr is the right starting point. For Wan 2.2 video generation at 720p or Qwen-Image-Edit at BF16, step up to an A100 80GB at $1.09/hr.
See Thunder Compute's full guide to cloud GPUs for AI image generation.
FAQ
What is ComfyUI?
ComfyUI is an open-source, node-based interface for building AI image and video generation pipelines. You connect processing nodes in a visual graph, each representing one step in the process. Workflows are shareable as JSON files.
How do I install custom nodes in ComfyUI?
Install ComfyUI Manager first. Then open the Manager tab, click Custom Nodes Manager, search for the pack you want, and click Install. Restart ComfyUI after installation. For GGUF model support, install the ComfyUI-GGUF pack by city96.
What is the difference between ComfyUI and Comfy Cloud?
ComfyUI is the open-source software you run locally or on a cloud GPU of your choice. Comfy Cloud is the official hosted version, with a free tier and paid plans starting at $20/month. Raw cloud GPU instances give more flexibility over GPU selection and pricing.
How much VRAM do I need for video generation in ComfyUI?
Wan 2.2's 5B model runs on 8GB with offloading. The 14B variant requires 16-24GB for 480p output and 24GB+ for 720p. Complex video pipelines with ControlNet conditioning can require 60-80GB.
What is the cheapest cloud GPU for ComfyUI?
Thunder Compute's RTX A6000 at $0.35/hr handles SDXL, Flux, Qwen, and Z-Image Turbo with 48GB GDDR6 and 768 GB/s bandwidth. Persistent storage means models downloaded once stay ready across sessions.
Can I run ComfyUI without a local GPU?
Yes. Thunder Compute provides a one-command ComfyUI template that starts a remote instance with ComfyUI pre-installed and exposed at a public URL, with no local installation required.
What is the difference between image-to-image and inpainting in ComfyUI?
Image-to-image transforms an existing image based on a prompt and a denoising strength parameter. Inpainting replaces a specific masked region; outpainting extends the canvas beyond its edges. ComfyUI handles all three modes natively.