If you’re running a homelab and you want to generate images locally without uploading anything to the cloud, you’re looking at three serious contenders: AUTOMATIC1111’s Stable Diffusion WebUI, ComfyUI, and InvokeAI. I’ve been running all three on my 8GB RTX 3070 for the last few months, and they’re genuinely different enough that “which is best” depends almost entirely on what you’re actually trying to do.
What These Tools Actually Do
All three run Stable Diffusion or compatible models locally. They all let you generate images from text prompts, do img2img work, inpainting, outpainting. They all support community models and can be self-hosted in Docker. But the way they’re structured, the way they handle workflows, and what they expect from you are pretty different.
Stable Diffusion WebUI is the one everyone knows. It started as a single-file Python script, got built into a web interface, and now it’s the most forked, most extended thing in the space. You run it, you get a web UI, you click buttons, images appear. There are thousands of extensions. Community is enormous.
ComfyUI took a different path. It’s a node-based workflow system. Instead of buttons and sliders, you’re building execution graphs. You drag nodes around, connect outputs to inputs, and hit generate. It’s more powerful for complex operations but it has a steeper entry cost.
InvokeAI is somewhere in the middle. It has a proper web UI with tabs and organized controls, but it also introduced canvas painting tools and a workflow system that’s becoming its main interface. It feels like someone sat down and said “what if we designed this for actual creative work instead of just prototyping.”
Installation and First Run
Stable Diffusion WebUI is the easiest to get running. Clone the repo, run a shell script, wait. On my machine that was about five minutes from nothing to a web browser with the interface loaded. The first model downloads automatically if you don’t have one. You generate your first image before you’ve had coffee.
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui
./webui.shComfyUI installation is also straightforward but it requires more upfront thinking. You need to grab the repo, install Python dependencies, grab a model manager, understand node layouts. First run I spent thirty minutes looking at an empty canvas wondering what to do. There’s a learning curve that’s genuine, not artificial.
InvokeAI has a setup wizard that walks you through things. It’s polished. It’s somewhere between the two in terms of friction. The wizard asks questions, sets environment variables, downloads a model, and you’re in. This was probably eight minutes for me on a second installation.
If you care about time-to-first-image, Stable Diffusion WebUI wins by a significant margin. If you don’t mind twenty minutes of reading docs and learning a UI pattern, ComfyUI isn’t worse, just different.
Stability Diffusion WebUI vs Competitors: Feature Comparison
| Feature | Stable Diffusion WebUI | ComfyUI | InvokeAI |
|---|---|---|---|
| Installation complexity | Lowest | Medium | Low |
| Learning curve | Flat | Steep | Gentle |
| Text-to-image | Yes | Yes | Yes |
| Inpainting/Outpainting | Yes (UI built in) | Yes (nodes) | Yes (canvas-based) |
| ControlNet support | Yes (native) | Yes (nodes) | Yes (experimental) |
| Batch processing | Yes (single queue) | Yes (node-based) | Yes (single queue) |
| API for automation | Yes (documented) | Yes (node output) | Yes (REST API) |
| Model switching | UI dropdown | Reload nodes | UI dropdown |
| Community extensions | Thousands | Growing but fewer | Moderate |
| VRAM efficiency | Medium | Very good | Very good |
| Active maintenance | Yes | Yes | Yes |
Where Each One Breaks
Stable Diffusion WebUI’s weakness is that it’s accumulated a lot of cruft. There are extension conflicts. UI updates sometimes break workflows. The default setup isn’t optimized for VRAM. I’ve had the interface become unresponsive more than once, especially when I’m batch processing and swapping models. With a 6GB GPU you’ll hit memory limits faster than with ComfyUI. The extension ecosystem is huge but also messy—you install something and suddenly your UI has three copies of the same feature.
ComfyUI’s weakness is adoption friction. If you’re trying to integrate it into something else, or show it to someone unfamiliar with node systems, they look at you like you’ve lost your mind. The node system is incredibly powerful once you understand it, but “understand” is the expensive part. Documentation is in Discord. Examples are scattered. If you want to use a new model architecture two weeks after it’s released, you might be waiting for a contributor to build the node.
InvokeAI is more polished than either, but it feels like it’s still figuring out what it wants to be. The original UI was clean but static. Now they’re pushing a workflow system that looks a lot like ComfyUI but with less documentation. If they go all-in on workflows, casual users might feel left behind. Performance is good but I’ve had it use more VRAM than ComfyUI for the same operation.
The Real Difference: Workflow and Integration
Here’s where the comparison gets interesting. Stable Diffusion WebUI has an API. You can send it a request, get back image data, integrate it into scripts or other services. I’ve got a Home Assistant automation that triggers image generation through the WebUI API. Works fine. But the API reflects the UI philosophy—it’s endpoint-based, not workflow-based.
ComfyUI’s workflow system means you build complex multi-step operations inside the nodes. You can do ControlNet, img2img, upscaling, all in sequence without leaving the tool. If you want to automate it, you export the workflow as JSON and send it to the API. This is more flexible than it sounds, but it’s also more abstract. Your Home Assistant automation has to serialize node graphs.
InvokeAI sits between them. The API is REST-based and straightforward. The workflow system exists but isn’t mandatory. You can fire off a simple request like you would to WebUI, or you can build something more complex if you need it. This makes it the easiest to integrate into existing stuff.
Practical VRAM and Performance
On my RTX 3070 (8GB), Stable Diffusion WebUI uses about 6.2GB generating a 512×512 image with default settings. ComfyUI uses about 5.8GB for the same task. InvokeAI uses about 6GB. The difference is small, but ComfyUI’s memory optimization is noticeable when you’re running batch jobs. I can queue five generations in ComfyUI and watch them all complete. In WebUI, I have to wait between batches or I’ll hit OOM.
None of them will run comfortably on 4GB. You need 6GB minimum to avoid constant swapping. At 8GB you’re fine for single generations but batch work gets tight.
Generation speed is roughly the same across all three. My 768×768 Euler sampler at 20 steps takes about 18-22 seconds regardless of tool. It’s not a meaningful differentiator.
Extension Ecosystem and Long-Term Maintenance
Stable Diffusion WebUI has the largest extension community. There are upscalers, VAE tools, custom samplers, LoRA managers, everything. This is both an asset and a liability. I’ve installed extensions that don’t play well with each other. The ControlNet extension conflicts with one of the upscaler extensions I’m using. Nothing breaks, but the UI gets sluggish. You have to be careful about what you add.
ComfyUI’s extension system is newer and leaner. Fewer extensions means fewer conflicts but also fewer niche features. If you want something specific, you might have to build the node yourself or wait for someone to contribute it.
InvokeAI’s extension approach is closest to traditional software—plugins that follow a standard. Fewer of them, but they tend to be more stable. The trade-off is less customization.
Why I’m Still Using All Three
This is the part where I’m supposed to tell you one is the clear winner. I don’t actually think that’s true. Stable Diffusion WebUI is what I use for quick prototyping. Text prompt, click generate, done. It’s the default. ComfyUI is what I reach for when I need to do something complex and repeatable. Building a node graph takes longer but then I can export it and run it a hundred times. InvokeAI is what I use when I want something in the middle—more organized than WebUI, less abstract than ComfyUI. If I were giving someone a single recommendation, it depends on whether they value speed of learning or power of workflow. For my own work, having all three installed is the practical answer, even if it’s not the clean one.
FAQ
Can Stable Diffusion WebUI run on a 4GB GPU?
Technically yes, but you’ll be swapping to system RAM constantly and generation will be slow. 6GB minimum for reasonable performance, 8GB if you’re doing any batch work.
Is ComfyUI harder to use than Stable Diffusion WebUI?
Yes, but only at first. The node-based workflow is genuinely powerful once you understand it. You’re learning a different paradigm, not struggling with bad design. Plan for a few hours of reading before it clicks.
Which one is best for Home Assistant integration?
Stable Diffusion WebUI or InvokeAI. Both have straightforward REST APIs. ComfyUI can do it but you need to serialize workflow JSON, which is more work.
Do I need to choose just one?
No. I run all three on the same machine and use them for different purposes. They don’t interfere with each other as long as they’re on different ports.
Which uses the least VRAM?
ComfyUI edges out the others by a few hundred MB, but the difference is small enough that it shouldn’t be your deciding factor. All three are reasonably efficient.
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