Automatic1111 vs ComfyUI: which to choose for retouchers and photographers in 2026
Automatic1111 vs ComfyUI: which to choose for retouchers and photographers in 2026
When you first install Stable Diffusion locally, you face a fork in the road that defines the next six months of your work. On the left, Automatic1111 with familiar tabs, buttons and sliders. On the r
Intro
When you first install Stable Diffusion locally, you face a fork in the road that defines the next six months of your work. On the left, Automatic1111 with familiar tabs, buttons and sliders. On the right, ComfyUI with a blank canvas where you assemble a graph of nodes, like an electrical schematic. Both tools generate images from the same SD model, both support ControlNet, LoRA, inpainting and upscaling. But the working day inside each of them is structured in fundamentally different ways.
This article is for retouchers, marketplace photographers and AI practitioners who already understand what Stable Diffusion is, and are now choosing the interface in which to build their commercial pipeline. No religious wars, no cliched "the best one is the one that suits you". You will get concrete scenarios, numbers and tables.
I draw on the experience of retouchers who run SD daily for product photography, jewelry and marketplace catalogs. These people have no time for interface philosophy, they need to deliver batches of 200-500 frames and not miss a deadline. Accordingly, the lens of this article is strictly production-oriented.
Where Automatic1111 and ComfyUI came from
Stable Diffusion was released in summer 2022 as an open model, and almost immediately several web interfaces appeared around it. The winner of the first wave was the AUTOMATIC1111 Stable Diffusion WebUI, more commonly called A1111 or just "Automatic". It is a Gradio application written in Python that runs locally, opens in a browser and provides tabs for txt2img, img2img, inpaint, extras, train and so on. In essence, it is a control panel similar to Photoshop, only for a diffusion model.
ComfyUI appeared later, in early 2023, and took a different route. Instead of tabs, it offers a node-based workflow: you drag nodes onto a canvas (Load Checkpoint, CLIP Text Encode, KSampler, VAE Decode, Save Image) and connect them with lines. The result is a graph describing the entire generation process from loading the model to writing the file. This approach came from 3D software (Houdini, Blender Shader Editor, Substance Designer) and video compositing (Nuke, Fusion).
For a long time A1111 was the default for all beginners, while ComfyUI was considered a tool for geeks. But by 2025 the picture changed: ComfyUI received support for SDXL and FLUX earlier than Automatic, became faster on batches and established itself as the standard in commercial studios. A1111 remained the main tool for solo operators and those who joined the scene recently. Right now, in 2026, both are actively alive, and the choice between them is a real one rather than a foregone conclusion.
The conceptual difference: tabs versus a graph
The main difference is not technical but mental. A1111 is a GUI application in the classical sense: you open a tab, fill in fields, press Generate. Parameters are hidden in collapsibles, many things happen "by default", and you are not required to understand what is going on inside the pipeline.
ComfyUI forces you to assemble the pipeline yourself. You see every step: where the model is loaded, where the prompt is encoded, where the sampler does its work, where the VAE decodes the latent into pixels. This is both a plus and a minus. The plus is that you genuinely understand how diffusion works. The minus is that to generate a single image you have to build at least a basic graph of six nodes.
You can put it this way. A1111 is a vending coffee machine: you press a button, you get a latte. ComfyUI is an espresso machine with a portafilter: you have to grind the beans yourself, tamp the puck, set the pressure, but the coffee you get is the one you actually want, not the one the manufacturer pre-baked.
A1111: what people love and what they criticize
The pluses of Automatic1111, as practicing retouchers see them.
A low entry barrier. You launch it, open the txt2img tab, enter a prompt, hit Generate. That is all. No nodes, no pipeline logic. Within an hour after installation you are already getting meaningful images.
A huge ecosystem of extensions. Over more than three years, hundreds of plugins have grown around A1111: ControlNet, ADetailer, Regional Prompter, Dynamic Prompts, sd-webui-civitai-helper, the ultimate upscaler and dozens of others. Most install in a single click from the Extensions tab.
A clear img2img and inpaint tab. For a retoucher who works on a specific client image, this matters: you load the photo, mask the area, repaint in inpaint, save. In ComfyUI the same task requires assembling a graph.
Better integration with LoRA training inside the interface. Through extensions like Dreambooth or kohya-ss GUI, you can train directly in A1111.
The minuses.
Slower on batches. On runs of 100-500 frames, A1111 loses 20-40% of time to ComfyUI, especially on SDXL and FLUX. This is connected to how Automatic manages memory and cache.
Hard to version your workflow. Settings are scattered across tabs, scripts and extensions. To convey to another person "this is how I make a jewelry catalog for a marketplace", you have to describe it in words or screenshots. In ComfyUI it is enough to send a JSON file of the graph.
Development has slowed down. Major releases of Automatic come out rarely, and support for new architectures (FLUX, SD3, SDXL Lightning, video models) arrives with a lag relative to ComfyUI. A fork called Forge has appeared, which partially closes this gap, but it is no longer A1111 itself.
The API is limited. A1111 has a built-in REST API, but it is less flexible than what ComfyUI offers with its task queue and WebSocket notifications.
ComfyUI: what people love and what they criticize
The pluses.
Performance. On identical hardware ComfyUI is usually 15-40% faster than A1111 due to smarter memory management, caching of intermediate latents and reuse of models between runs. On batches the difference is even more visible.
Workflow reproducibility. The graph is saved as JSON or directly in the PNG metadata. You open someone else's image, press the Load button, get the same graph. This changes studio work: you can build a reference pipeline for a product category and reuse it without losses.
Flexibility. Any non-standard scenario, for example "pass the latent through two different samplers, blend with a mask and finish via inpaint", can be assembled in Comfy in five minutes. In A1111 such a thing requires workarounds via scripts or sequential runs.
Early arrival of new models. FLUX.1, SD3, video models (Hunyuan, LTX, Wan) are almost always supported first in ComfyUI. If you want to work at the cutting edge, ComfyUI handles this question better.
API and automation. A task queue, WebSocket, headless mode, easy integration into a Python or Node.js backend. Pipelines for generative services, Telegram bots and SaaS products are written on top of ComfyUI.
The minuses.
The entry barrier is higher. The first two days you will spend more time studying nodes than generating. Official documentation is sparse, the core knowledge sits on YouTube and in other people's graphs from civitai.
Nodes are excessive for simple tasks. If you simply want to generate an image from a prompt and fix a face, in A1111 that is two clicks, while in Comfy that is a graph of 8-12 nodes. Yes, you can save a template, but the initial assembly takes time.
Extension management is less mature. ComfyUI Manager covers the basic needs, but conflicts between custom nodes are more common than between Extensions in A1111. Often something breaks after an update.
UX is not settled yet. Context menus, node alignment, minimap, graph search: all of these have either appeared recently or still work imperfectly. A workflow of 50+ nodes turns into "find a needle in a haystack".
Comparison table on key parameters
| Parameter | Automatic1111 | ComfyUI |
|---|---|---|
| Entry barrier | Low, an hour to the first image | Medium, a day or two to learn the nodes |
| Generation speed (one image) | Baseline | 15-25% faster |
| Generation speed (batch of 100+) | Baseline | 25-40% faster |
| VRAM management | Standard | Aggressive offload, works on 6-8 GB |
| ControlNet | Via extension, stable | Native, via nodes, more flexible |
| LoRA | Convenient through UI and tags | Via Load LoRA node, several can be chained |
| Inpainting | A strong side | Doable, but takes longer to assemble |
| Img2img | Native tab | A graph of 5-7 nodes |
| Extensions | Hundreds, single-click | ComfyUI Manager, slightly less mature |
| API | REST, basic | REST + WebSocket, advanced |
| Workflow versioning | Screenshots and text | JSON or PNG metadata |
| Support for FLUX, SD3 | With a delay, via Forge | Native, from day one |
| Updates | Rare | Frequent, sometimes breaking |
| Team work | Difficult | JSON exchange solves it |
| Suitable for solo | Yes | Yes, after training |
| Suitable for a studio | Conditionally | Yes, the best choice |
When to choose Automatic1111
A solo retoucher who takes turnkey orders, and each frame is processed individually. Volumes are small, there are no reproducibility requirements, ControlNet and inpaint are used situationally. A1111 here saves your time at the learning stage and does not get in the way of your work.
A photographer who is trying AI for the first time. If you have just heard about Stable Diffusion and want to understand whether you need it at all, install A1111. Within an hour you will get a working tool. If you do not like it, you can drop it without regret.
A teacher and blogger. When you record a tutorial video, it is easier to explain in Automatic tabs than in a graph. The audience reproduces the actions more quickly.
Someone for whom the Train function is critical. Inside A1111 it is more convenient to train embeddings, hypernetworks and simple LoRAs. ComfyUI requires external tools like kohya-ss.
When to choose ComfyUI
A product photography studio or a jewelry studio with a steady flow. If you have 200-500 frames a day, a 30% difference in speed is two or three working hours. Over a month that adds up to enough that a training course pays for itself.
A marketplace seller with regular product shoots. You build a reference workflow for a category once, and after that every new batch of goods passes through it without manual tuning. Reproducibility is money: one person assembles the graph, the rest replicate it.
A developer of AI pipelines. If you are building a service, a bot or an internal tool for an agency, ComfyUI is the de facto standard for the backend. API, queue, headless, support for new models: it is all there.
A retoucher who plans to work with AI as a long-term skill. In a year or two the ability to read a workflow from PNG metadata will become as basic as reading PSD layers. The earlier you start, the earlier you gain an advantage.
Can you switch later
You can and you should. Knowledge transfers between the two interfaces almost completely. Prompt engineering, understanding of samplers, CFG, steps, denoising strength, ControlNet conditioning, LoRA strength: these are all the same concepts, only wrapped in different UI.
The typical path of a professional looks like this. For the first two or three months a person lives in Automatic1111, understands what diffusion is, builds up a base of prompts and LoRAs. Then for one specific project (a batch task, a non-standard pipeline, integration into production) they try ComfyUI and discover that the graph comes together faster than they thought. After that Comfy becomes the main tool, while A1111 remains for one-off experiments and for cases when it is convenient to twist sliders in a UI.
The reverse path also happens, but more rarely. People who started with ComfyUI and switched to Automatic usually do so for the sake of a specific extension or because they teach AI and want to simplify the material for the audience.
What pros use in commercial work
In commercial retouching and AI post-production the layout is approximately as follows.
Product and jewelry studios. ComfyUI is the main tool, A1111 is the backup. Reference workflows per category (rings, earrings, watches, packaging, footwear, clothing on a mannequin) are stored as JSON and applied on a conveyor.
Marketplace photographers. Here the split is by volume. Up to 50 SKUs a month you can live in Automatic. From 50 and up people move to ComfyUI for the sake of speed and reproducibility.
Agencies producing AI visuals for brands. ComfyUI at almost 100%, because the client often asks "give us five more variants with different lighting", and without a reproducible graph this turns into pain.
Solo retouchers working with photographers on an outsourced basis. More often A1111, because tasks are one-off and integration into a pipeline is not required.
Educational programs in AI retouching. The trend of the last year: courses start teaching on Automatic as the starting tool, and closer to the end of the program move students to ComfyUI. That is reasonable, because one complements the other.
Conclusion: what to choose in 2026
In short: start with the one that is closer to your current level, and do not be afraid to change your choice in three months.
For a beginner without an AI background, Automatic1111 is better. Less frustration, a faster first result, a softer learning curve. When you hit the ceiling (and you will, if you work commercially), the transition to ComfyUI will take a week.
For a professional with a flow of tasks, ComfyUI right away. The time spent on learning the nodes will pay off after the very first batch of 200 frames. Workflow reproducibility is your main asset, which is hard to overestimate.
A middle option: install both interfaces in parallel. They do not conflict, they share the same model files via symlinks, and you can run the same LoRA in both. This is a normal practice, and it is also the best way to understand which tool is really yours.
And the main thing: the interface is not magic. The magic is in understanding models, prompts, ControlNet, LoRA stacks, color grading after generation, in the ability to build a pipeline for a specific product. Without this knowledge A1111 will give you beautiful junk, and ComfyUI will give you beautiful junk faster.
Where to learn product-focused AI retouching
If you are a marketplace photographer or a retoucher who wants to introduce AI into the shooting of jewelry, watches, packaging and product work, take a look at the AI PRO course at gdefoto.com.
The program is built around Automatic1111 as the starting tool, because on it you can fastest get to grips with the basics: prompts for product work, ControlNet for preserving the shape of the item, LoRAs for specific product categories, inpaint of background elements and shadows. After that we show how to move to ComfyUI when the order flow grows, and how to save the workflow for a team.
Unlike general AI courses of the "we will teach you to generate pictures" type, AI PRO is sharpened for commercial shooting: jewelry with correct highlights, identical angles in a series, preservation of metal and stone texture, marketplace-ready background without artifacts. This is not "creative SD for art", this is a tool for a catalog.
Details, the program and registration for the nearest cohort on the course page: gdefoto.com/lk/ai-pro/buy/.